QU4LITY | Digital Reality in Zero Defect Manufacturing

Summary

QU4LITY will demonstrate, in a realistic, measurable, and replicable way an open, certifiable and highly standardised, SME-friendly and transformative shared data-driven ZDM product and service model for Factory 4.0 through 5 strategic ZDM plug & control lighthouse equipment pilots and 9 production lighthouse facility pilots.

QU4LITY will also demonstrate how European industry can build unique and highly tailored ZDM strategies and competitive advantages:

  • significantly increase operational efficiency, scrap reduction,
  • prescriptive quality management
  • energy efficiency
  • defect propagation avoidance 
  • improved smart product customer experience
  • new digital business models; e.g. outcome-based and product servitisation

Qu4lity will do this through an orchestrated open platform ecosystem, ZDM atomized components and digital enablers across all phases of product and process lifecycle. The main goal is to build an autonomous quality model to meet the Industry 4.0 ZDM challenges.

Results, demos, etc. (108)
More information & hyperlinks
Web resources: https://qu4lity-project.eu/
https://cordis.europa.eu/project/rcn/220162/factsheet/en
Start date: 01-01-2019
End date: 31-07-2022
Total budget - Public funding: 15 998 180,00 Euro - 15 998 180,00 Euro
Twitter: @EuQu4lity
Cordis data

Original description

QU4LITY will demonstrate, in a realistic, measurable, and replicable way an open, certifiable and highly standardised, SME-friendly and transformative shared data-driven ZDM product and service model for Factory 4.0 through 5 strategic ZDM plug & control lighthouse equipment pilots and 9 production lighthouse facility pilots. QU4LITY will also demonstrate how European industry can build unique and highly tailored ZDM strategies and competitive advantages (significantly increase operational efficiency, scrap reduction, prescriptive quality management, energy efficiency, defect propagation avoidance and improved smart product customer experience, and foster new digital business models; e.g. outcome-based and product servitisation) through an orchestrated open platforms ecosystem, ZDM atomized components and digital enablers (Industry 4.0 digital connectivity & edge computing package, plug & control autonomous manufacturing equipment, real-time data spaces for process monitoring & adaptation, simulation data spaces for digital process twin continuity, AI-powered analytic data spaces for cognitive digital control twin composable services, augmented worker interventions, European quality data marketplace) across all phases of product and process lifecycle (engineering, planning, operation and production) building upon the QU4LITY autonomous quality model to meet the Industry 4.0 ZDM challenges (cost and time effective brownfield ZDM deployment, flexible ZDM strategy design & adaptation, agile operation of zero defect processes & products, zero break down sustainable manufacturing process operation and human centred manufacturing).

Status

SIGNED

Call topic

DT-ICT-07-2018-2019

Update Date

27-10-2022
Geographical location(s)
Structured mapping
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Comment:
  • Reference architecture and blueprints: Reference architecture, vocabularies, open data models, standards, best practices, SDOs/SSOs contribution, clustering activities, …
  • Digital enablers and Equipment solutions: digital enablers, ZDM platforms and equipment solutions, patents in autonomous manufacturing asset & control, autonomous digital twin MBSE platforms.
  • A Pan-European innovation ecosystem clustering activities (DMP Cluster, OPEN DEI Cluster, 4ZDM Cluster, Connected Factories) and the Digital Factory Alliance (DFA)
Results:

The main innovation will be represented by the introduction in production of MPFQ model fused with AQ control loops: Functional Integration and Correlation between Material, Quality, Process and Appliance Functions.

Comment:

One the one hand, some pilot owners expect that no particular skills will be requested after the QU4LITY project development implementations.  For example:

  • all systems should remain accessible by the majority of the workers without specific expertise or knowledge (where for instance each correlation system has to remain within a blackbox and only provide rules outputs for production lines).  
  • The AR app and the first training on the machine will be enough for start the production with new operators on the line.  
  • In essence the job profile will remain the same, however, the operators need to understand & be able to work with these new technologies. This requires some basic knowledge on the (digitalized) systems, for the operators a lot can be captured in SOP’s (standard operation procedures), but the technical support staff should also have some basic knowledge on the workings and the hardware/software side of the systems in order to be able to support the shopfloor where needed

 

New job profiles and associated skills are:  Digital Business Processes Analyst, Expert in Machine Learning Algorithms, DevOps Development knowledge, Data scientist (programming and statistical knowledge), Artificial Inteligence knowledge, Cybersecurity expert, Ontology architects and modellers in MBSE, Digitalized systems Shopfloor worker, Digital and connectivity engineering, New systems integration Manufacturing Engineer, Cloud -Data Formats - Data analytics Engineer, Product, manufacturing and quality global knowledge.

Re- and upskilling needs were identified in the following areas: AI and Data analytics; Agile development, Multi disciplinary project management (IT, mechanical, electrical engineering); Design Thinking; Standardization; Data Analysis and Data Space technology for Manufacturing; IT Skills : Docker environment and languages like phyton of json; Data Analytics : basic skills , BI softwares
Programming languages such as C#, C ++, HTML, Java, Microsoft .NET and SQL Server ; data tools for data cleaning and preprocessing, data parsing, data feature engineering; machine to machine (M2M) data and protocols; Machine Learning Skilling for all languages/ ML Systems; Data analysis skills

The following knowledge delivery mechanism where identified as relevant:  AR/VR, gamification, on-the-job training, vocational training, MooCs (Massive Open Online Courses)

  • For newcomers to the field of Zero Defect Manufacturing, MOOCs are the way to go, since they can cover more aspects of the Industry4.0 and ZDM, not just the data science part. For the work force already in place, vocational training or on the job training would be recommended, herewith quickly adapting to the new working situations.  On the job training would be enough to transmit knowledge of the technology. The solution develop has to be as user friendly as possible and be quickly understandable either on the HMI aspect and on the hardware side.

Project clusters are groups of projects that cooperate by organising events, generating joint papers, etc...

Results:

Concerning the ecological and economic operation of a factory, data analytics tools in combination with simulation approaches can contribute to improved throughput, bottleneck-reduction, or both for the production line. Through the optimization of the processes, production execution on organization and logistic level can be optimized by reducing the amount of material within the system, the lead times, or both.

Results:

Deviation on cycle-time, from 98% (as is) to 99% (to be)

Results:

Quality- Fall Off Rate, from 95% (as is) to 98,5% (to be)

Results:

improve false positive rate by 20%.  Measured as false positives rate, actual value is considered confidential.

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: Quality,; guidance for onfiguration management - activity that applies technical and administrative direction over the life cycle of a product and service, its configuration identification and status, and related product and service configuration information.

Comment: Associated to QU4LITY Reference Architecture: Control Service

Details: Quality, requirements for a quality management system

Comment: Associated to QU4LITY Reference Architecture: Cloud

Details: Cloud Computing, Concepts

Comment: Associated to QU4LITY Reference Architecture: Digital Twin and Planning Services

Details: Blockchain

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: Information model (QIF) and data formed into XML instance files support the entire scope of model based definition manufacturing quality workflow

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: Qualit modelling and support of quality workflow scenarious

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: Qualit modelling and support of quality workflow scenarious

Comment: Associated to QU4LITY Reference Architecture: IoT Hubs

Details: IoT, interoperability framework

Comment: Associated to QU4LITY Reference Architecture: Cross-cutting

Details: IoT, Use cases

Comment: Associated to QU4LITY Reference Architecture: Distributed Trustworthiness Layer

Details: Security RA

Comment: Associated to QU4LITY Reference Architecture: Data-driven Modelling and Learning Service

Details: AI, Framework for AI systems using ML

Comment: Associated to QU4LITY Reference Architecture: Cross-cutting

Details: AI, Use cases

Comment: Associated to QU4LITY Reference Architecture: Cross-cutting

Details: IoT RA

Comment: Associated to QU4LITY Reference Architecture: Edge/Fog

Details: Edge Computing

Comment: Associated to QU4LITY Reference Architecture: Cross-cutting

Details: Industrial IoT standards and roadmapping

Comment: Associated to QU4LITY Reference Architecture: IoT Automation Services

Details: Production system engineering; process quality and optimisation designer; distributed industrial-process measurement and control systems

Comment: Associated to QU4LITY Reference Architecture: Digital Twin and Planning Services

Details: RAMI4.0 - Digital twin, process optimization run-time

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: AAS, Interoperability

Comment: Associated to QU4LITY Reference Architecture: Digital Twin and Planning Services

Details: General principles of the Digital Factory framework

Comment: Associated to QU4LITY Reference Architecture: Cross-cutting

Details: RA/RM (basic standard) applied for QU4LITY-RA

Comment: Associated to QU4LITY Reference Architecture: Control Service

Details: OS: Robot Operating System

Comment: Associated to QU4LITY Reference Architecture: Digital Twin and Planning Services

Details: IIRA

Results:

Sintef delivered information to put “soft” part of organization also in daily management structure. Nowadays the KPI’s are hard technical related. Other topic is to use digital tools for operator- whiteboard sessions. People claim they are more digital oriented at home compared to work floor.

Stakeholder-training Logbook: No results obtained yet, as the implementation is not far enough to train stakeholders.

Comment: Associated to QU4LITY Reference Architecture: Distributed Trustworthiness Layer

Details: Cyber Security in Industrial Environments

Comment: Associated to QU4LITY Reference Architecture: Distributed Trustworthiness Layer

Details: Cybersecurity (basis standard, inkl. terms and concepts relating to cyber security and cyber risk management)

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: Information security management systems; Vocabulary

Comment: Associated to QU4LITY Reference Architecture: Collaboration, Business and Operation Service (CRM, ERP/MES, SCM, DSS)

Details: ISMS: Establishing, implementing, maintaining and continually improving an information security management system within the context of the organization (Information security)

Comment: Associated to QU4LITY Reference Architecture: Distributed Trustworthiness Layer

Details: Security RA

Comment: Associated to QU4LITY Reference Architecture: Edge/Fog

Details: Edge Computing

Comment: Associated to QU4LITY Reference Architecture: Distributed Trustworthiness Layer

Details: General concepts and principles of IT security evaluation; general model of evaluation

Comment: Associated to QU4LITY Reference Architecture: Distributed Trustworthiness Layer

Details: IT Security, requirements, evaluation criteria for IT security

Comment: Associated to QU4LITY Reference Architecture: Network

Details: Connectivity requirements: Specifications (models) for low-data-rate wireless connectivity with fixed, portable, and moving devices with no battery or very limited battery consumption requirements

Comment: Associated to QU4LITY Reference Architecture: Factory Network/ Field and Proximity Network: Analog Inputs, Real-Time Ethernet, Digital IOs, Camera Link, P&P

Details: Feldbus/Device level (EtheCAT, PROFINET): Generic concept of fieldbuse

Comment: Associated to QU4LITY Reference Architecture: Corporate Network/ Production OT Access Network: Deterministic Ethernet (TSN), OPC-UA, IDS/NGSI-LD

Details: AMQP

Comment: Associated to QU4LITY Reference Architecture: IoT Automation Services

Details: Production system engineering; process quality and optimisation designer; distributed industrial-process measurement and control systems

Comment: Associated to QU4LITY Reference Architecture: IoT Automation Services

Details: Production system engineering; process quality and optimisation designer; distributed industrial-process measurement and control systems

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: AAS, Interoperability

Comment: Associated to QU4LITY Reference Architecture: Digital Twin and Planning Services

Details: General principles of the Digital Factory framework

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: Information security management systems; Vocabulary

Comment: Associated to QU4LITY Reference Architecture: Cross-cutting

Details: RA/RM (basic standard) applied for QU4LITY-RA

Comment: Associated to QU4LITY Reference Architecture: Cloud

Details: Use of open souce plattform for messaging and data streaming.

Comment: Associated to QU4LITY Reference Architecture: IoT Automation Services

Details: Production system engineering; process quality and optimisation designer; distributed industrial-process measurement and control systems

Comment: Associated to QU4LITY Reference Architecture: Distributed Trustworthiness Layer

Details: Cyber Security in Industrial Environments

Comment:

Associated to QU4LITY Reference Architecture: Corporate Network/ Production OT Access Network: Deterministic Ethernet (TSN), OPC-UA, IDS/NGSI-LD

Details: OPC UA: an industrial M2M communication protocol for Interoperability; Information modelling
 

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: AAS, Interoperability

Comment: Associated to QU4LITY Reference Architecture: Digital Twin and Planning Services

Details: General principles of the Digital Factory framework

Comment: Associated to QU4LITY Reference Architecture: Digital Twin and Planning Services

Details: Blockchain

Comment: Associated to QU4LITY Reference Architecture: Control Service

Details: OS: Robot Operating System

Comment: Associated to QU4LITY Reference Architecture: Cloud

Details: Use of open souce plattform for messaging and data streaming.

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: Vocabulary, Interoperability, used to define the schema and enable a publisher to describe datasets and data services in a catalog using a standard model and vocabulary that facilitates the consumption and aggregation of metadata from multiple catalogs.

Comment: Associated to QU4LITY Reference Architecture: Factory Network/ Field and Proximity Network: Analog Inputs, Real-Time Ethernet, Digital IOs, Camera Link, P&P

Details: Feldbus/Device level (EtheCAT, PROFINET): Generic concept of fieldbuse

Comment: Associated to QU4LITY Reference Architecture: IoT Automation Services

Details: Production system engineering; process quality and optimisation designer; distributed industrial-process measurement and control systems

Comment: Associated to QU4LITY Reference Architecture: Distributed Trustworthiness Layer

Details: Cyber Security in Industrial Environments

Comment:

Associated to QU4LITY Reference Architecture: Corporate Network/ Production OT Access Network: Deterministic Ethernet (TSN), OPC-UA, IDS/NGSI-LD

Details: OPC UA: an industrial M2M communication protocol for Interoperability; Information modelling
 

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: AAS, Interoperability

Comment: Associated to QU4LITY Reference Architecture: Digital Twin and Planning Services

Details: General principles of the Digital Factory framework

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: Data Models: proposes an overarching integrated conceptual model that describes interactions between the physical world, the user, and digital information, the context for AR-assisted learning and other parameters of the environment

Comment: Associated to QU4LITY Reference Architecture: Digital Twin and Planning Services

Details: Blockchain

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: Qualit modelling and support of quality workflow scenarious

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: Information model (QIF) and data formed into XML instance files support the entire scope of model based definition manufacturing quality workflow

Comment: Associated to QU4LITY Reference Architecture: Cloud

Details: Cloud computing, Fundamentalsn for Cloud services and devices: data flow, data categories and data use;

Comment: Associated to QU4LITY Reference Architecture: Cross-cutting

Details: JSON

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: IoT, Semantic interoperability

Comment: Associated to QU4LITY Reference Architecture: Assets & Smart Products

Details: LCM: Enabling digital physical asset lifecycle management spanning plants, platforms and facilities; functional and interoperability requirements for Critical Infrastructure Management on a cross-sector basis

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: Interoperable information model, vocabulary, and encoding mechanisms for representing statements about the usage of content and services.

Comment: Associated to QU4LITY Reference Architecture: Corporate Network/Plant IT Service Network: 5G, Wireless, Wired Etherne

Details: Interoperability & Common APIs: straightforward integration of a machine tool into higher level IT systems

Comment: Associated to QU4LITY Reference Architecture: …

Details: Applied to address performance analysisi and porcess optimisazion (i.e. allows to capture data that is required to do performance analysis of production facilities)

Comment: Associated to QU4LITY Reference Architecture: Control Service

Details: OS: Robot Operating System

Comment: Associated to QU4LITY Reference Architecture: Factory Network/ Field and Proximity Network: Analog Inputs, Real-Time Ethernet, Digital IOs, Camera Link, P&P

Details: MT Connect: used to access real-time data from shop floor manufacturing equipment such as machine tools

Comment: Associated to QU4LITY Reference Architecture: Cloud

Details: Use of open souce plattform for messaging and data streaming.

Comment: Associated to QU4LITY Reference Architecture: Factory Network/ Field and Proximity Network: Analog Inputs, Real-Time Ethernet, Digital IOs, Camera Link, P&P

Details: Specification that standardizes the connection between cameras and frame grabbers and defines a complete interface (provisions for data transfer, camera timing, serial communications, and real time signaling to the camera)

Comment: Associated to QU4LITY Reference Architecture: Factory Network/ Field and Proximity Network: Analog Inputs, Real-Time Ethernet, Digital IOs, Camera Link, P&P

Details: API, Interoperability: global camera interface standard using the Gigabit Ethernet communication protocol to allow fast image transfer using low cost standard cables over very long lengths.

Comment: Associated to QU4LITY Reference Architecture: Corporate Network/ Production OT Access Network: Deterministic Ethernet (TSN), OPC-UA, IDS/NGSI-LD

Details: REST API: application of standards and specification for HTTP/REST APIs

Comment: Associated to QU4LITY Reference Architecture: Factory Network/ Field and Proximity Network: Analog Inputs, Real-Time Ethernet, Digital IOs, Camera Link, P&P

Details: Feldbus/Device level (EtheCAT, PROFINET): Generic concept of fieldbuse

Comment: Associated to QU4LITY Reference Architecture: IoT Automation Services

Details: Production system engineering; process quality and optimisation designer; distributed industrial-process measurement and control systems

Comment: Associated to QU4LITY Reference Architecture: Distributed Trustworthiness Layer

Details: Cyber Security in Industrial Environments

Comment:

Associated to QU4LITY Reference Architecture: Corporate Network/ Production OT Access Network: Deterministic Ethernet (TSN), OPC-UA, IDS/NGSI-LD

Details: OPC UA: an industrial M2M communication protocol for Interoperability; Information modelling
 

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: AAS, Interoperability

Comment: Associated to QU4LITY Reference Architecture: Digital Twin and Planning Services

Details: General principles of the Digital Factory framework

Comment: Associated to QU4LITY Reference Architecture: Network

Details: Network: Wireless local area networks

Comment: Associated to QU4LITY Reference Architecture: Network

Details: Connectivity requirements: Specifications (models) for low-data-rate wireless connectivity with fixed, portable, and moving devices with no battery or very limited battery consumption requirements

Comment: Associated to QU4LITY Reference Architecture: Network

Details: Communication: Protocols, procedures, and managed objects for the transport of timing over local area networks

Comment: Associated to QU4LITY Reference Architecture: Digital Twin and Planning Services

Details: Blockchain

Comment: Associated to QU4LITY Reference Architecture: Corporate Network/ Production OT Access Network: Deterministic Ethernet (TSN), OPC-UA, IDS/NGSI-LD

Details: AMQP

Comment: Associated to QU4LITY Reference Architecture: Corporate Network/ Production OT Access Network: Deterministic Ethernet (TSN), OPC-UA, IDS/NGSI-LD

Details: MQTT: an extremel lightweight publish/subscribe messaging transport protocol

Comment: Associated to QU4LITY Reference Architecture: IoT Hubs

Details: IoT, information exchange, peer-to-peer connectivity and seamless communication both between different IoT systems

Comment: Associated to QU4LITY Reference Architecture: …

Details: Applied to address performance analysisi and porcess optimisazion (i.e. allows to capture data that is required to do performance analysis of production facilities)

Comment: Associated to QU4LITY Reference Architecture: Control Service

Details: OS: Robot Operating System

Comment: Associated to QU4LITY Reference Architecture: Factory Network/ Field and Proximity Network: Analog Inputs, Real-Time Ethernet, Digital IOs, Camera Link, P&P

Details: Specification that standardizes the connection between cameras and frame grabbers and defines a complete interface (provisions for data transfer, camera timing, serial communications, and real time signaling to the camera)

Comment: Associated to QU4LITY Reference Architecture: Factory Network/ Field and Proximity Network: Analog Inputs, Real-Time Ethernet, Digital IOs, Camera Link, P&P

Details: Feldbus/Device level (EtheCAT, PROFINET): Generic concept of fieldbuse

Comment: Associated to QU4LITY Reference Architecture: Distributed Trustworthiness Layer

Details: Cyber Security in Industrial Environments

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: AAS, Interoperability

Comment: Associated to QU4LITY Reference Architecture: Digital Twin and Planning Services

Details: General principles of the Digital Factory framework

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: Information security management systems; Vocabulary

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: Vocabulary, Interoperability, used to define the schema and enable a publisher to describe datasets and data services in a catalog using a standard model and vocabulary that facilitates the consumption and aggregation of metadata from multiple catalogs.

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: Quality,; guidance for onfiguration management - activity that applies technical and administrative direction over the life cycle of a product and service, its configuration identification and status, and related product and service configuration information.

Comment: Associated to QU4LITY Reference Architecture: Cloud

Details: Cloud computing general overview and vocabulary

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: Big data vocabulary

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: IoT, Vocabulary

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: Cloud Compuitng, Terminology

Comment: Associated to QU4LITY Reference Architecture: Data-driven Modelling and Learning Service

Details: AI, foundational, concepts and terminology

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: Vocabulary, Information technology (IT) in general

Results:

There was a risk that other developments made within this pilot do not follow the reference architecture of IDS and thus are incompatible. This would cause that certain applications could not be deployed and run within in the proposed data space approach. 

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: AAS, Interoperability

Comment: Associated to QU4LITY Reference Architecture: Digital Twin and Planning Services

Details: IIRA

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: IoT, Vocabulary

Comment: Associated to QU4LITY Reference Architecture: IoT Hubs

Details: IoT, interoperability framework

Comment: Associated to QU4LITY Reference Architecture: Cross-cutting

Details: IoT, Use cases

Comment: Associated to QU4LITY Reference Architecture: Cross-cutting

Details: IoT RA

Comment: Associated to QU4LITY Reference Architecture: Edge/Fog

Details: Edge Computing

Comment: Associated to QU4LITY Reference Architecture: Cross-cutting

Details: Industrial IoT standards and roadmapping

Results:

An AI vision algorithm developed by TNO (WP3) seems to filter bad rated parts compared to installed algorithm. Advantage can be when product print is changing to catch-up development speed in traditional algorithm development.

For this trial, the acquired test data will be analyzed regarding quality classification. In every test a part could pass or fail. Failed parts must be reworked, if possible, and brought back to the process. Sometimes parts are classified as failed even if they are good (false positive). This effect will be analyzed by machine learning algorithms and, if necessary, adopted in classification parameterisation. Additionally, the fact of 100% testing, means every panel is tested automatically, with bottleneck in out of the line test stations will be addressed in setting up failure prediction models for quality forecast. This will be supported by data analysis of pre reflow AOI (automated optical inspection). 

With all these data analysis and process optimization activities economical evaluation will be included to support decisions in-process and configuration changes. For the development of these applications, the main steps are data availability/access, data processing, and model development. The developed applications should be deployed on Edge devices.

Comment: Associated to QU4LITY Reference Architecture: Data-driven Modelling and Learning Service

Details: AI, foundational, concepts and terminology

Comment: Associated to QU4LITY Reference Architecture: Data-driven Modelling and Learning Service

Details: AI, Framework for AI systems using ML

Comment: Associated to QU4LITY Reference Architecture: Cross-cutting

Details: AI, Use cases

Comment: Associated to QU4LITY Reference Architecture: Cross-cutting

Details: Industrial IoT standards and roadmapping

Results:

Through innovative algorithms and statistical methods, possible data sources for predictive quality control can be identified and evaluated. Moreover, by cooperation of all project partners, the realization of data access and acquisition along the whole process chain can be realized. With a focus on algorithms and methodology, a use case-specific algorithm is going to be implemented and validated to maintain high prediction accuracy.

Data availability is a challenge: Limited access to measurement data (due to limited access to third-party systems)

Results:

There seems to be relationship to predict torque with use of in-line data. Needs to be more explored

By applying sophisticated algorithms and methods on the acquired data, systematic failure root cause detection supported by data analytics can be implemented. In addition, improved knowledge of machine states/maintenance requirements for neuralgic points can be implemented through the desired solution path within this pilot.

Results:

Milling Digital Twin enables strategy design and quality control in milling processes with only SW tools, simulation and virtual optimisation

Results:

Cockpit optimiser software provides environment for intelligent design of an automated cell with the customer.

Results:

Solutions to facilitate the analytical thinking of the operator. The solution will help the operator with the correlation of quality and process parameters in order to make a decision upwards in the process.

Enable operators to work in a more complex environment while reducing the strain of administrative tasks and enabling easy production analytics by capturing information online instead of on paper. 

Shopfloor worker (operator – technical support group): From a shopfloor perspective new job profiles, or altered job profiles should be defined, however In essence the job profiles will remain the same, while the operators and Technical Support Groups need to understand & be able to work with these new technologies. This requires some basic knowledge on the (digitalized) systems, for the operators a lot can be captured in SOP’s (Standard Operating Procedures), but the technical support staff should also have some basic knowledge on the workings and the hardware/software side of the systems in order to be able to support the shopfloor where needed.

The end2end process supported by the overall architecture helps the operator and team leader in their daily activities in order to prevent and anticipate as much as possible quality issues on the product via the analysis of a huge amount of data linked together via the holistic semantic model.

There is a need of managing large Data Sets and Big Data, IA solutions for different Manufacturing Processes. Solutions need to support operators in decision-making

 

With the help of skilled production line workers, the data in the AI platform can be annotated and herewith produce the predictive models for ZDM autonomous quality inspection. The platform gives users the ability to monitor the AQ process (Autonomous Quality) and provide feedback for the ZDM.

To acquire quality data, all involved users and managers must understand some basic data science principles. Machine vision in modern times relies on large amount of consistent data. Data acquisition process begins with organized collection of samples, which should become an integral part of every standardized manufacturing process that involves automated quality inspection or ZDM.

Complete machine parameters correlation is realized, allowing machine operators to take into account all the assets from each workstation of the production line. It enhances its capacity in relation to conventional analytics methods.

The ZDM-Autononous Quality Solutions are used as systems that perform tasks in an autonomous/automated way, requiring the intervention of an operator only when an operational tie-breaker is needed.  When that is the case, the operator has to analyse the incident and provide for a solution to the AQL System, interacting with it via an HMI interface.

Cockpit optimiser and Milling Digital Twin with AI tools for accelerating current design and optimisation processes by operators

Comment:
  • Reference architecture and blueprints: Reference architecture, vocabularies, open data models, standards, best practices, SDOs/SSOs contribution, clustering activities, …
  • Digital enablers and Equipment solutions: digital enablers, ZDM platforms and equipment solutions, patents in autonomous manufacturing asset & control, autonomous digital twin MBSE platforms.
  • A Pan-European innovation ecosystem clustering activities (DMP Cluster, OPEN DEI Cluster, 4ZDM Cluster, Connected Factories) and the Digital Factory Alliance (DFA)
Comment:

New digital business models; e.g. outcome-based and product servitisation

Results:

Within Qu4lity use case, GHI with the collaboration of Innovalia and SQS, is building a ZDM scenario based on the development of a smart and connected hot stamping process with the ability to correlate the furnace operation parameters with the quality control of the stamped parts, extending in this way the product lifecycle control loop, making the operator more involved in the process thanks to the new platform developed.

The production line in Amberg has a highly automated process with several test stations along the path.

Kolektor's Qu4lity project is addressing the real-time injection moulding process monitoring-control. The scope of the pilot project is a production line where Kolektor produces one type of product. The aim of this pilot is to detect, possibly predict, and remove the cause of the process failure as soon as possible, ideally in real-time. Based on the collected data and by applying the control loops, advanced analytics, and artificial intelligence methods we are trying to better understand the moulding process, with the emphasis on detecting anomalies and failures as soon as possible.

Using the opportunities brought by the Qu4lity project, RiaStone with the collaboration of Synesis and IntraSoft, built a commercial grade ZDM implementation scenario, which brings to the ceramics industry the ability to implement Autonomous Quality Loops, which will add new approaches to production, promoting better and innovative defect management and production control methods, consistent with the integration of Zero defect Manufacturing processes, these being namely: in-line inspection technologies, and integration of ICT tools for autonomous, automatic, smart system decision taking

The POWDER BED Additive technology will be considered to test new edge devices for process control, towards a ZDM result, and to work on data management and analytics to implement the whole manufacturing process by a platform approach.

Data monitored from the machine tool and meta-information generated by different applications running at edge level will be collected and elaborated by the data analysis tool to extract useful information to be sent to the decision support system.

The objective of the pilot is to enable smart machines with autonomous diagnosis based on machine condition monitoring.

FAGOR ARRASATE as a leading manufacturer of forming machines it is obliged to proactive participate in projects like QU4LITY and led solutions to the customers to improve the availability, performance and quality of their installations and get an optimum cost per part ratio.

FAGOR ARRASATE has a long experience in delivering press machines as well as providing the building blocks of such lines. A press machine is the product par excellence of FAGOR ARRASATE. A typical press machine is composed by two rigid platforms (head and base), a bed, a ram, and a mechanism as well as all the other surrounding components that guarantee the full automation and process control.

Historically, machine tool manufacturers have not had any information of the machine behaviour once they were working at the customer facilities. Maintenance actions by the machine tool supplier, where mainly started by a customer’s call and where mainly related to corrective actions, once the failure had already happened.

Currently many condition issues on the machine are detected afterwards, they appear when a quality matter is detected on the forming parts or a machine component is damaged, causing even machine stoppage.  These problems are fixed by machine adjustment or changing programs or forming process parameters.

Consequently, the only way to avoid future problems is by preventive maintenance or machine adjustment actions. These are carried out either by the machine owner itself or external services which are sometimes delivered by FAGOR ARRASATE.

In QUALITY project, FAGOR ARRASATE will equip a press machine with a SMART CONNECT technology that provides data from the machine, to the owner and to the machine supplier. Within the context of Zero-Defect Manufacturing, FAGOR ARRASATE will develops Smart solutions that will anticipate and avoid failures, reduce downtimes and assure quality.

It has a great complexity from the point of view of the acquisition, measurement and transmission of the parameters and variables. The result that would be obtained from the QU4LITY project, would allow the customers of FAGOR ARRASATE to have total control of a zero defects manufacturing process at machine level and to know at any time how and under which conditions all the parts have been manufactured.

Results:

 Main technologies that will be adopted in this pilot:

  • Augmented reality (TTS)
  • Sensors system for process monitoring (FRAUNHOFER)
  • Digital twin (TTS)
  • Data analytics (SYNESIS)
  • Decision support system (ATLANTIS)
  • Secure updates sharing with Blockchain tech (EGINEERING)
Results:

The machines (OT) are connected to intelligence in the Edge and Cloud (IT) for generation of Zero Defect Manufacturing functions

For many years, and in the context of INDUSTRY 4.0, FAGOR ARRASATE is working together with IKERLAN  in smart platform for press machines and industrial processes. The platform goes from the sensitization of the machine’s critical elements to the remote monitoring of press conditions. The platform focuses on improvement of asset management and OEE (Overall Equipment Effectiveness) and allows FAGOR ARRASATE to increase quality of service for their clients.

Results:

We are building a connected environment through the industrial furnace smartization, but also implementing an IT solution that enables data gathering and transferring on real-time to GHI server, where then the data analysis is performed.

We are developing Sinapro IIoT MES/MOM cloud solution (part of the Kolektor Digital Platform) as the cornerstone of the MOM system which enables real-time collecting, evaluating, validating, filtering, checking, and storing of production data. The captured production data can be processed in real-time for the purpose of obtaining various production information, which enables immediate action. MOM function for production analyses with depth learning technology of AI gives users additional and high-quality information’s for fast decisions to achieve zero-defect goals in production. 

Results:

Operators are more involved in the whole process, but mainly receives valuable information of the furnace operation through the real-time monitoring interface of the platform. Also daily reports provide them valuable information. 

The Kolektor Digital Platform enables human involvement on various levels. Human operators can monitor, view and inspect created datasets. During the process of model training, the operator can monitor the current state and detailed information of the training process. The Kolektor Digital Platform opens a channel between a data scientist and a decision-making individual in the production line. It is desirable to have multiple people, each assigned to a specific task. The whole process could be split into subtasks - acquiring images on the production line, human expert labeling the images (classification, anomaly,..), data scientist training the model on the new dataset and at the end evaluation of the model and pushing the new (improved) model to the production line.

The RiaStone Qu4lity Pilot enables human involvement at various levels and production stages.

Human operators inspect, monitor, and view all created datasets. During the process of model training, the operator guides the ML algorithm through the image inspection training process.

This is performed after the image acquisition in the production line, through joint algorithm/human expert labeling of the images (new defect classification, conformant/non-conformant product), this allowing for the training of the algorithm in the existing defects dataset, and at evolving the algorithm and pushing the new (improved) model to the production line AQL platform

Operators are connected to the smart funcions the receive valuable information related to components condition that allow them to take decisions related to machine and process. Maintenance technitians and specialized engineers from the machine tool provider can also be included in the process.

Results:

The Amberg production line collects all the process and test data an a quality management system data base. This allows reporting and production KPI analysis as well as supply chain management.

The gathering of data from the machines during their lifetime allows  the generation of valuable information for the improvement, not only of the actual machine and process, but also the future machines and smart functions that are continously improved through data based engineering and design. 

FAGOR FA-Link MAP (Fagor Arrasate Link MAchine Platform) is a platform developed by FAGOR ARRASATE in collaboration with IKERLAN. This platform uses cutting edge technologies for big data processing and visualization.  This platform inputs the data from the press machine using the FAGOR (Data-Adquisition System) and provides to the different stakeholders an UI with a set of views to monitor and analyze the press machine performance. The UI is customizable by the end user and the data to be shown is manually configured as different views and alarms in FA-LINK visualization UI.

FAGOR’s monitoring platform, FA-Link machine platform, is being extended with tools focused on ZDM in this pilot and in the related packages of QU4LITY.

FA-Link is composed by two systems, the former that is executed in the manufacturing plant (on premise) and the latter that is executed in the cloud. The data captured in plant via on premise (local view), is uploaded and aggregated in the cloud part (global view).

Results:

The Beyond Platform also gathers data from other assets, not only from the furnace, so they can potentially optimised as well. On the other hand, there are aspects regarding the industrial furnace that mainly affects to the whole factory level, as for example, a reduction on the energy consumption or a reduction on the defective manufacturing that can be achieved thanks to the data analysis provided through this tools.

Analysing the test results the production process can be adapted and optimized.

Analyses Stations are equiped with Semantik Search Technology in order to generate consistent Data between standard failure catalogues and Teammember entries into Analyses Client. 

 

 

FAGOR’s monitoring platform, FA-Link machine platform, is being extended with tools focused on ZDM in this pilot and in the related packages of QU4LITY.

FA-Link is composed by two systems, the former that is executed in the manufacturing plant (on premise) and the latter that is executed in the cloud. The data captured in plant via on premise (local view), is uploaded and aggregated in the cloud part (global view).

Results:

The Beyond platform implemented provides valuable information with daily, monthly reports and deeper analysis in concrete operations that allows technicians to optimize the machine (industrial furnace) operation.

AI vision algorithm developed by TNO (WP3) seems to filter bad rated parts compared to installed algorithm. Advantage can be when product print is changing to catch-up development speed in traditional algorithm development. Test-case currently in progress.

Analyzing the test and process data, specific machine parameters can be adapted and optimized.

The Kolektor Digital Platform enables us to automatically collect the data from the shop floor. The Sinapro IIoT enables the connectivity of the pilot production line machines and related IoT devices for real-time production data acquisition and monitoring. The acquired data is afterward used in the off-line machine learning pipelines to produce machine vision predictive models to detect visual injection moulding defects. A pipeline for deploying such off-line machine learning to a HPC cluster is being developed at JSI within the scope of the Kolektor Pilot.

A correlation is realized within the production line between the overall process parameters and the product characteristics which are monitored at the end of the line in specific control modules.
With the help of INTRASOFT algorithm, several optimization are suggested for process parameters in order to optimize the final control workstation and to diminuate scraps and rework parts.

In addition to that, we can use CEA non-intrusive assets aquisition system to localize machine-oriented rootcauses (process deviation due to mechanical issue in the workstation for instance). This could lead to quickly identify a rootcause and to implement corrective actions effectively.
The non-intrusive notion comes from the fact that the asset monitoring (which can be a vibration from an accelerometer, a current, a temperature, ...) does not require any heavy integration, even the link with PLC is simplified with a standard exchange table for record triggers and the part informations.

The on-premise system is responsible of capturing the data from the different sensors and upload such information to the cloud. This task is performed by a software called FAGOR-DAS. Through FAGOR-DAS, data published by the sensors via PLCs using industrial protocols, such as OPC-UA, are sampled.  After this data is gathered, it is analysed and compacted locally (Edge computing). Such data can be visualized via a tool called Visual Stamp (local view of the manufacturing line). The same data is prepared to be sent to the cloud infrastructure.

Results:

With the quality management platform, optimization of production is enabled.

The optimization of quality process decision is taking place thank to a holistic view of the factors that influence the perception of the quality from the consumer prespective. The platform using a MPFQ driven data model is enabling a faster, more reliable and flexible visualization system and analytical approach.

There are two IoT platforms included in 2 manufacturaing lines for Automotive and Railway sector for MONDRAGON pilot. IoT platforms monitor 3 grinding machines from railway sector as well as press machine, stacker, Owen and Transfer from Automiotive customer. 

The variables monitored allow machine tool buider to know that the process exectuion is under treshold defined as well as enabling predictions about possible faillures. As a consequence there will be an increase of the quality production and the optimisation of the production. 

The interoperability layer between two IoT Platfroms has been achieved considering OPC-UA and AAS.

Collaboration with international partners such as ATLANTIS, VTT and FHG has allowed us to include IA algorithms, specific data analitics and data sharing connectors.In spte of the fact that IoT platfrom and interopoerability has been oriented to real time optimisation the analitycs and the IA algorithms have been carryed out off-line

 

FAGOR’s monitoring platform, FA-Link machine platform, is being extended with tools focused on ZDM in this pilot and in the related packages of QU4LITY.

Results:

The variables monitored in real time throght IoT platfroms  for 2 manufacturing lines enables the Zero Defect Manufuactiring goal.  Multiple industrial assets monitored force to have an strategic in terms of enchacement processes.

The implementation of modular architecture interconected  involving Cloud and Edge Systems, Data Modelling and Learning Service and Iot Hub produce top quality production. The introduction of Interoperability layer for gathering data from two different manufacturing lines together with OPC-UA and AAS is key for the goal.

 

In the RiaStone Qu4lity Pilot the ZDM-AQL is implemented in a modular architecture, which includes both in-factory data processing, Edge processing Systems, Cloud processing systems, and Machine Learning processing Services.

The RiaStone Qu4lity Pilot goal is to recognize, detect, and reconfigure the production process parameters as soon as a failure is detected in real-time. 

This process is based in the collected data, advanced analytics, machine learning image inspection methods

The ambition is to create a modular monitoring and control system that can be used with many different sensors and process models. The models need to be adaptable to the actual task, for a specific geometry or dedicated material processing conditions. Real-time process and machine signals need to be analysed in by machine-learning algorithms to find structures and pattern related to the required key quality indicators (critical defects per track, distortion, keeping of dimensions).The system will be also connected to a higher-level factory data interface which allows to exchange process information and reassign the production strategy based on additional factory conditions.

 

Solutions, methodologies, and tools that are being developed within different work packages of QU4LITY are being applied to this pilot in the task T7.2 of WP7. As is shown in the figure, different components of the FAGOR platform such as FA-LINK, IKCLOUD+ and IKSEC+ are being extended focusing in ZDM of press machines. FA-LINK platform has been completed with the following components:

  • ETL@FA-LINK is responsible to extract transform and load the data. Additionally to the sensor data, and with ZDM on mind, contextual data is also collected by the platform. This component prepares the data for the AI training process. This component is composed by an extended version of iKCloud that is being designed in WP3 and integrated in this pilot in WP7.
  • Then, TRAIN@FA-LINK uses the previously obtained and persisted data to create an AI model for ZDM. The model is generated using cutting edge machine learning technologies. This model is used to identify situations and generate suggestions to the users to increase performance and reduce defectives of press machine. The model training is performed by the extension of ikCloud+ developed in WP3/WP7 work packages of QU4LITY.
  • Next, the model is executed by EXECUTE@FA-LINK using the data obtained from the press machine. This way, ZDM related alarms, indicators and suggestions are obtained and persisted. This component uses FA-LINK and IK-Cloud+ solutions that are being implemented in WP3 and WP7.
  • Finally, the obtained results are shown to the users using different views of FA-LINK. A set of new views focused on ZDM and quality are being implemented in the WP7 of QU4LITY in this pilot. These new views provide the ability to the platform to show, suggest and guide the user to improve press machine performance and reduce downtimes and defectives.
Results:

Process Data from Manufacturing Data Lake is analysed online for suspicious outliers. A Webinterface enables online intervention by staff.

 

Part of the improved decision process enabled by the holistic platrom can be close looped into machine control parametes, allowing an autonomous quality management at factory level

MONDRAGON pilot is being developed considering 2 IoT platfrom and interoperability layer developed by MGEP together with OPC-UA and AAS. Real time process optimisation enables Autonomous quality outcomes and Zero Defect Manufacturing for Automotve (Fagor Arrasate )and railway (Danobat) manufacturing lines. The FA-LINK platfrom monitored industrial assets for Fagor Arrasate and SAVVY IoT platfrom for DANOBAT.

The introduction of IA algorithms by ATLANTIS and VTT are developed offline achieving high top optimisation production. On the other hand, the approach of Machine Learning approach should be further developed. The interaction of the operators, maintenance workers and R&D staff are stil crucial for Top high level Autonomous Manufacturing process Optmisation

 

Results:

1. Augmented Reality is improving supporting processes Change over,  Maintenance and Training. Partner PACE will apply their AR technology to avoid utilization of human resource in Maintenance documents handling. Instead Technologies like smart glasses and Holo lens will be applied. Virtual assistants will guide Maintenances staff through maintenance and repair processes instead. Same is targeted for Training.

2. Visualisation of machine and process data in realtime will enable immediate intervention in case of abnormal behaviour.

The acquired data is used in on-line prediction of defects. The predicted defects are used to adapt the visual quality inspection with an in-hand camera with a robot. The robot is guided to and between predetermined viewpoints associated with the predicted defects. The robot motion is generated autonomously on-line.

At a certain point of integration, the correlation between overall process parameters and the output product characteristics could be realized in real time in order to adjust, without any Human action, the different parameters along the production line.

This is the ideal towards which we wish to achieve with our future production lines.

The data acquired through computer vision, is processed through machine learning algorithms and compared to an existing database of ~10000+ images already noted by human operators

Inspection results are fed into the synesis-consortium machine control platform that decides necessary changes to the machine parameters driving production in both Business Processes (1&2)

Thanks to this new approach with modular adaptable signal processing system and a strong interaction with data space and simulation tools trough the platform, will be possible to detect anomaly and have anequipment condition reporting , reduce reject rate by application of data-driven process model that has been derived by AI algorithms, increase OEE by recommending process adjustments to the operator or directly change the parameters in real time, so to reduce also the operator costs.

Results:

As it is described on the Autonomous Smart Factories, in this use case, in addition to the tools that make possible to improve and optimize the manufacturing connected process, it also intends to highly reduce the defective manufacturing increasing the product lifecycle control loop, improving the manufacturing process thanks to the information provided through the product quality control process.

Results:

In this use case, GHI and Innovalia will share data in a trust manner through an IDS connector, so that GHI will be able to find correlation between the quality of the parts and the furnace operation parameters.

Results:

By the time this sharing data solution has been devise just for the use case, but also a business model in here can emerge as GHI is getting more restrictions with customers that do not want to give the governance of their process data.

Through the developments considered in this business case, the machine tool supplier obtains new digitally enabled solutions for the provision of smart services supported on digital platforms. 

Comment: Associated to QU4LITY Reference Architecture: Factory Network/ Field and Proximity Network: Analog Inputs, Real-Time Ethernet, Digital IOs, Camera Link, P&P

Details: Feldbus/Device level (EtheCAT, PROFINET): Generic concept of fieldbuse

Comment: Associated to QU4LITY Reference Architecture: IoT Automation Services

Details: Production system engineering; process quality and optimisation designer; distributed industrial-process measurement and control systems

Comment: Associated to QU4LITY Reference Architecture: Distributed Trustworthiness Layer

Details: Cyber Security in Industrial Environments

Comment:

Associated to QU4LITY Reference Architecture: Corporate Network/ Production OT Access Network: Deterministic Ethernet (TSN), OPC-UA, IDS/NGSI-LD

Details: OPC UA: an industrial M2M communication protocol for Interoperability; Information modelling
 

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: AAS, Interoperability

Comment: Associated to QU4LITY Reference Architecture: Digital Twin and Planning Services

Details: RAMI4.0 - Digital twin, process optimization run-time

Comment: Associated to QU4LITY Reference Architecture: Digital Twin and Planning Services

Details: General principles of the Digital Factory framework

Comment: Associated to QU4LITY Reference Architecture: Network

Details: Communication: Protocols, procedures, and managed objects for the transport of timing over local area networks

Comment: Associated to QU4LITY Reference Architecture: Network

Details: Network: Wireless local area networks

Comment: Associated to QU4LITY Reference Architecture: Network

Details: Connectivity requirements: Specifications (models) for low-data-rate wireless connectivity with fixed, portable, and moving devices with no battery or very limited battery consumption requirements

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: Data Models: proposes an overarching integrated conceptual model that describes interactions between the physical world, the user, and digital information, the context for AR-assisted learning and other parameters of the environment

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: Quality,; guidance for onfiguration management - activity that applies technical and administrative direction over the life cycle of a product and service, its configuration identification and status, and related product and service configuration information.

Comment: Associated to QU4LITY Reference Architecture: Control Service

Details: Quality, requirements for a quality management system

Comment: Associated to QU4LITY Reference Architecture: Digital Twin and Planning Services

Details: Blockchain

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: Information model (QIF) and data formed into XML instance files support the entire scope of model based definition manufacturing quality workflow

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: Qualit modelling and support of quality workflow scenarious

Comment: Associated to QU4LITY Reference Architecture: Distributed Trustworthiness Layer

Details: General concepts and principles of IT security evaluation; general model of evaluation

Comment: Associated to QU4LITY Reference Architecture: Distributed Trustworthiness Layer

Details: General concepts and principles of IT security evaluation; general model of evaluation

Comment: Associated to QU4LITY Reference Architecture: Cloud

Details: Cloud computing general overview and vocabulary

Comment: Associated to QU4LITY Reference Architecture: Distributed Trustworthiness Layer

Details: IT Security, requirements, evaluation criteria for IT security

Comment: Associated to QU4LITY Reference Architecture: Distributed Trustworthiness Layer

Details: IT Security, requirements, evaluation criteria for IT security

Comment: Associated to QU4LITY Reference Architecture: Corporate Network/ Production OT Access Network: Deterministic Ethernet (TSN), OPC-UA, IDS/NGSI-LD

Details: AMQP

Comment: Associated to QU4LITY Reference Architecture: Cloud

Details: Cloud computing, Fundamentalsn for Cloud services and devices: data flow, data categories and data use;

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: IoT, Vocabulary

Comment: Associated to QU4LITY Reference Architecture: Cross-cutting

Details: JSON

Comment: Associated to QU4LITY Reference Architecture: IoT Hubs

Details: IoT, interoperability framework

Comment: Associated to QU4LITY Reference Architecture: IoT Hubs

Details: IoT, information exchange, peer-to-peer connectivity and seamless communication both between different IoT systems

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: IoT, Semantic interoperability

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: Cloud Compuitng, Terminology

Comment: Associated to QU4LITY Reference Architecture: Cloud

Details: Cloud Computing, Concepts

Comment: Associated to QU4LITY Reference Architecture: Cross-cutting

Details: IoT, Use cases

Comment: Associated to QU4LITY Reference Architecture: Data-driven Modelling and Learning Service

Details: AI, foundational, concepts and terminology

Comment: Associated to QU4LITY Reference Architecture: Cross-cutting

Details: AI, Use cases

Comment: Associated to QU4LITY Reference Architecture: Distributed Trustworthiness Layer

Details: Security RA

Comment: Associated to QU4LITY Reference Architecture: Distributed Trustworthiness Layer

Details: Security RA

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: Information security management systems; Vocabulary

Comment: Associated to QU4LITY Reference Architecture: Collaboration, Business and Operation Service (CRM, ERP/MES, SCM, DSS)

Details: ISMS: Establishing, implementing, maintaining and continually improving an information security management system within the context of the organization (Information security)

Comment: Associated to QU4LITY Reference Architecture: Distributed Trustworthiness Layer

Details: Cybersecurity (basis standard, inkl. terms and concepts relating to cyber security and cyber risk management)

Comment: Associated to QU4LITY Reference Architecture: Distributed Trustworthiness Layer

Details: Cybersecurity (basis standard, inkl. terms and concepts relating to cyber security and cyber risk management)

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: Vocabulary, Information technology (IT) in general

Comment: Associated to QU4LITY Reference Architecture: Corporate Network/ Production OT Access Network: Deterministic Ethernet (TSN), OPC-UA, IDS/NGSI-LD

Details: MQTT: an extremel lightweight publish/subscribe messaging transport protocol

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: Big data vocabulary

Comment: Associated to QU4LITY Reference Architecture: Data-driven Modelling and Learning Service

Details: AI, Framework for AI systems using ML

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: Information security management systems; Vocabulary

Comment: Associated to QU4LITY Reference Architecture: Collaboration, Business and Operation Service (CRM, ERP/MES, SCM, DSS)

Details: ISMS: Establishing, implementing, maintaining and continually improving an information security management system within the context of the organization (Information security)

Comment: Associated to QU4LITY Reference Architecture: Cross-cutting

Details: IoT RA

Comment: Associated to QU4LITY Reference Architecture: Edge/Fog

Details: Edge Computing

Comment: Associated to QU4LITY Reference Architecture: Edge/Fog

Details: Edge Computing

Comment: Associated to QU4LITY Reference Architecture: Cross-cutting

Details: Industrial IoT standards and roadmapping

Comment: Associated to QU4LITY Reference Architecture: Cross-cutting

Details: RA/RM (basic standard) applied for QU4LITY-RA

Comment: Associated to QU4LITY Reference Architecture: Factory Network/ Field and Proximity Network: Analog Inputs, Real-Time Ethernet, Digital IOs, Camera Link, P&P

Details: MT Connect: used to access real-time data from shop floor manufacturing equipment such as machine tools

Comment: Associated to QU4LITY Reference Architecture: Corporate Network/Plant IT Service Network: 5G, Wireless, Wired Etherne

Details: Interoperability & Common APIs: straightforward integration of a machine tool into higher level IT systems

Comment: Associated to QU4LITY Reference Architecture: Distributed Trustworthiness Layer

Details: Cyber Security in Industrial Environments

Comment: Details: IoT Thing description, definition of protocol bindings

Comment: Associated to QU4LITY Reference Architecture: …

Details: Applied to address performance analysisi and porcess optimisazion (i.e. allows to capture data that is required to do performance analysis of production facilities)

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: Interoperable information model, vocabulary, and encoding mechanisms for representing statements about the usage of content and services.

Comment: Associated to QU4LITY Reference Architecture: Assets & Smart Products

Details: LCM: Enabling digital physical asset lifecycle management spanning plants, platforms and facilities; functional and interoperability requirements for Critical Infrastructure Management on a cross-sector basis

Comment: Associated to QU4LITY Reference Architecture: Digital Twin and Planning Services

Details: IIRA

Comment: Associated to QU4LITY Reference Architecture: Corporate Network/ Production OT Access Network: Deterministic Ethernet (TSN), OPC-UA, IDS/NGSI-LD

Details: REST API: application of standards and specification for HTTP/REST APIs

Comment: Associated to QU4LITY Reference Architecture: Factory Network/ Field and Proximity Network: Analog Inputs, Real-Time Ethernet, Digital IOs, Camera Link, P&P

Details: API, Interoperability: global camera interface standard using the Gigabit Ethernet communication protocol to allow fast image transfer using low cost standard cables over very long lengths.

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: Vocabulary, Interoperability, used to define the schema and enable a publisher to describe datasets and data services in a catalog using a standard model and vocabulary that facilitates the consumption and aggregation of metadata from multiple catalogs.

Comment: Associated to QU4LITY Reference Architecture: Factory Network/ Field and Proximity Network: Analog Inputs, Real-Time Ethernet, Digital IOs, Camera Link, P&P

Details: Specification that standardizes the connection between cameras and frame grabbers and defines a complete interface (provisions for data transfer, camera timing, serial communications, and real time signaling to the camera)

Comment: Associated to QU4LITY Reference Architecture: Cloud

Details: Use of open souce plattform for messaging and data streaming.

Comment: Associated to QU4LITY Reference Architecture: Control Service

Details: OS: Robot Operating System

Comment: Associated to QU4LITY Reference Architecture: Factory Network/ Field and Proximity Network: Analog Inputs, Real-Time Ethernet, Digital IOs, Camera Link, P&P

Details: Feldbus/Device level (EtheCAT, PROFINET): Generic concept of fieldbuse

Comment:

Associated to QU4LITY Reference Architecture: Corporate Network/ Production OT Access Network: Deterministic Ethernet (TSN), OPC-UA, IDS/NGSI-LD

Details: OPC UA: an industrial M2M communication protocol for Interoperability; Information modelling
 

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: AAS, Interoperability

Comment: Associated to QU4LITY Reference Architecture: Digital Twin and Planning Services

Details: RAMI4.0 - Digital twin, process optimization run-time

Comment: Associated to QU4LITY Reference Architecture: Digital Twin and Planning Services

Details: General principles of the Digital Factory framework

Comment: Associated to QU4LITY Reference Architecture: Digital Twin and Planning Services

Details: IIRA

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: Qualit modelling and support of quality workflow scenarious

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: Quality,; guidance for onfiguration management - activity that applies technical and administrative direction over the life cycle of a product and service, its configuration identification and status, and related product and service configuration information.

Comment: Associated to QU4LITY Reference Architecture: Control Service

Details: Quality, requirements for a quality management system

Comment: Associated to QU4LITY Reference Architecture: Distributed Trustworthiness Layer

Details: IT Security, requirements, evaluation criteria for IT security

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: Big data vocabulary

Comment: Associated to QU4LITY Reference Architecture: IoT Hubs

Details: IoT, interoperability framework

Comment: Associated to QU4LITY Reference Architecture: Data-driven Modelling and Learning Service

Details: AI, foundational, concepts and terminology

Comment: Associated to QU4LITY Reference Architecture: Data-driven Modelling and Learning Service

Details: AI, Framework for AI systems using ML

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: Information security management systems; Vocabulary

Comment: Associated to QU4LITY Reference Architecture: Cross-cutting

Details: Industrial IoT standards and roadmapping

Comment: Associated to QU4LITY Reference Architecture: Cloud

Details: Cloud Computing, Concepts

Comment: Associated to QU4LITY Reference Architecture: Distributed Trustworthiness Layer

Details: Security RA

Comment: Associated to QU4LITY Reference Architecture: Cross-cutting

Details: IoT, Use cases

Comment: Associated to QU4LITY Reference Architecture: Cross-cutting

Details: AI, Use cases

Comment: Associated to QU4LITY Reference Architecture: Edge/Fog

Details: Edge Computing

Comment: Associated to QU4LITY Reference Architecture: Distributed Trustworthiness Layer

Details: Cybersecurity (basis standard, inkl. terms and concepts relating to cyber security and cyber risk management)

Comment: Associated to QU4LITY Reference Architecture: Cross-cutting

Details: RA/RM (basic standard) applied for QU4LITY-RA

Comment: Associated to QU4LITY Reference Architecture: Control Service

Details: OS: Robot Operating System

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: Vocabulary, Interoperability, used to define the schema and enable a publisher to describe datasets and data services in a catalog using a standard model and vocabulary that facilitates the consumption and aggregation of metadata from multiple catalogs.

Comment: Associated to QU4LITY Reference Architecture: IoT Automation Services

Details: Production system engineering; process quality and optimisation designer; distributed industrial-process measurement and control systems

Comment: Associated to QU4LITY Reference Architecture: Cross-cutting

Details: JSON

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: IoT, Semantic interoperability

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: Interoperable information model, vocabulary, and encoding mechanisms for representing statements about the usage of content and services.

Comment: Associated to QU4LITY Reference Architecture: Corporate Network/Plant IT Service Network: 5G, Wireless, Wired Etherne

Details: Interoperability & Common APIs: straightforward integration of a machine tool into higher level IT systems

Comment: Associated to QU4LITY Reference Architecture: …

Details: Applied to address performance analysisi and porcess optimisazion (i.e. allows to capture data that is required to do performance analysis of production facilities)

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: Qualit modelling and support of quality workflow scenarious

Comment: Associated to QU4LITY Reference Architecture: Cloud

Details: Cloud computing, Fundamentalsn for Cloud services and devices: data flow, data categories and data use;

Comment: Associated to QU4LITY Reference Architecture: Factory Network/ Field and Proximity Network: Analog Inputs, Real-Time Ethernet, Digital IOs, Camera Link, P&P

Details: MT Connect: used to access real-time data from shop floor manufacturing equipment such as machine tools

Comment: Associated to QU4LITY Reference Architecture: Cloud

Details: Use of open souce plattform for messaging and data streaming.

Comment: Associated to QU4LITY Reference Architecture: Factory Network/ Field and Proximity Network: Analog Inputs, Real-Time Ethernet, Digital IOs, Camera Link, P&P

Details: Specification that standardizes the connection between cameras and frame grabbers and defines a complete interface (provisions for data transfer, camera timing, serial communications, and real time signaling to the camera)

Comment: Associated to QU4LITY Reference Architecture: Factory Network/ Field and Proximity Network: Analog Inputs, Real-Time Ethernet, Digital IOs, Camera Link, P&P

Details: API, Interoperability: global camera interface standard using the Gigabit Ethernet communication protocol to allow fast image transfer using low cost standard cables over very long lengths.

Comment: Associated to QU4LITY Reference Architecture: Corporate Network/ Production OT Access Network: Deterministic Ethernet (TSN), OPC-UA, IDS/NGSI-LD

Details: REST API: application of standards and specification for HTTP/REST APIs

Comment: Associated to QU4LITY Reference Architecture: Factory Network/ Field and Proximity Network: Analog Inputs, Real-Time Ethernet, Digital IOs, Camera Link, P&P

Details: Feldbus/Device level (EtheCAT, PROFINET): Generic concept of fieldbuse

Comment: Associated to QU4LITY Reference Architecture: IoT Automation Services

Details: Production system engineering; process quality and optimisation designer; distributed industrial-process measurement and control systems

Comment: Associated to QU4LITY Reference Architecture: Network

Details: Connectivity requirements: Specifications (models) for low-data-rate wireless connectivity with fixed, portable, and moving devices with no battery or very limited battery consumption requirements

Comment: Associated to QU4LITY Reference Architecture: Network

Details: Communication: Protocols, procedures, and managed objects for the transport of timing over local area networks

Comment: Associated to QU4LITY Reference Architecture: Corporate Network/ Production OT Access Network: Deterministic Ethernet (TSN), OPC-UA, IDS/NGSI-LD

Details: AMQP

Comment: Associated to QU4LITY Reference Architecture: Corporate Network/ Production OT Access Network: Deterministic Ethernet (TSN), OPC-UA, IDS/NGSI-LD

Details: MQTT: an extremel lightweight publish/subscribe messaging transport protocol

Comment: Associated to QU4LITY Reference Architecture: IoT Hubs

Details: IoT, information exchange, peer-to-peer connectivity and seamless communication both between different IoT systems

Comment: Associated to QU4LITY Reference Architecture: Assets & Smart Products

Details: LCM: Enabling digital physical asset lifecycle management spanning plants, platforms and facilities; functional and interoperability requirements for Critical Infrastructure Management on a cross-sector basis

Comment: Associated to QU4LITY Reference Architecture: …

Details: Applied to address performance analysisi and porcess optimisazion (i.e. allows to capture data that is required to do performance analysis of production facilities)

Comment: Associated to QU4LITY Reference Architecture: Distributed Trustworthiness Layer

Details: Cybersecurity (basis standard, inkl. terms and concepts relating to cyber security and cyber risk management)

Comment: Associated to QU4LITY Reference Architecture: …

Details: Applied to address performance analysisi and porcess optimisazion (i.e. allows to capture data that is required to do performance analysis of production facilities)

Comment: Associated to QU4LITY Reference Architecture: Factory Network/ Field and Proximity Network: Analog Inputs, Real-Time Ethernet, Digital IOs, Camera Link, P&P

Details: Feldbus/Device level (EtheCAT, PROFINET): Generic concept of fieldbuse

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: Qualit modelling and support of quality workflow scenarious

Comment: Associated to QU4LITY Reference Architecture: Distributed Trustworthiness Layer

Details: General concepts and principles of IT security evaluation; general model of evaluation

Comment: Associated to QU4LITY Reference Architecture: Assets & Smart Products

Details: LCM: Enabling digital physical asset lifecycle management spanning plants, platforms and facilities; functional and interoperability requirements for Critical Infrastructure Management on a cross-sector basis

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: Qualit modelling and support of quality workflow scenarious

Comment: Associated to QU4LITY Reference Architecture: Cloud

Details: Cloud computing, Fundamentalsn for Cloud services and devices: data flow, data categories and data use;

Comment: Associated to QU4LITY Reference Architecture: Control Service

Details: OS: Robot Operating System

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: Vocabulary, Interoperability, used to define the schema and enable a publisher to describe datasets and data services in a catalog using a standard model and vocabulary that facilitates the consumption and aggregation of metadata from multiple catalogs.

Comment: Associated to QU4LITY Reference Architecture: IoT Automation Services

Details: Production system engineering; process quality and optimisation designer; distributed industrial-process measurement and control systems

Comment: Associated to QU4LITY Reference Architecture: Cloud

Details: Cloud computing general overview and vocabulary

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: Big data vocabulary

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: Cloud Compuitng, Terminology

Comment: Associated to QU4LITY Reference Architecture: Data-driven Modelling and Learning Service

Details: AI, foundational, concepts and terminology

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: Vocabulary, Information technology (IT) in general

Comment: Associated to QU4LITY Reference Architecture: Digital models and Vocabularies

Details: Information security management systems; Vocabulary