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SatisFactory | A collaborative and augmented-enabled ecosystem for increasing SATISfaction and working experience in smart FACTORY environments

01-01-2015

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31-12-2017

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Pathways
Autonomous Smart Factories Pathway
Comment:

Autonomy in factories is achieved by security systems that produce alerts and warnings, by training courses that does not require a trainer and by applications that signs daily jobs automatically to the most appropriate employees based on specific criteria.

Connected IT and OT
IoT enabled SCADA, MOM-MES, ERP (…) connectivity
Comment:

IoT enabled connection between the factory's applications.

Result items:
Middleware + Ontology Management
Middleware + Ontology Management

COMPOSITION | Ecosystem for Collaborative Manufacturing Processes _ Intra- and Interfactory Integration and Automation

01-09-2016

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31-08-2019

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Pathways
Autonomous Smart Factories Pathway
Comment:

The concept of the autonomous factories is approached in the intrafactory part of the project with connections between different links of the value chain. Agent marketplace and automated bidding process which enable automated negotiation and transaction.

Connected IT and OT
Result items:
Interoperability for M2M & HMI in factory environments II
Interoperability for M2M & HMI in factory environments II
IoT enabled SCADA, MOM-MES, ERP (…) connectivity
Comment:

IoT enabled connectivity with intrafactory systems. 

Result items:
Interoperability for M2M & HMI in factory environments II
Interoperability for M2M & HMI in factory environments II

UPTIME | UNIFIED PREDICTIVE MAINTENANCE SYSTEM

01-09-2017

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28-02-2021

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Pathways
Autonomous Smart Factories Pathway
Comment:

UPTIME will provide a unified predictive maintenance management framework and a smart predictive maintenance information system covering the whole prognostic lifecycle. It will contribute to improve smart predictive maintenance systems capable to integrate information from many different sources and of various types, in order to more accurately estimate the process performances and the remaining useful life.

Connected IT and OT
IoT enabled SCADA, MOM-MES, ERP (…) connectivity

QU4LITY | Digital Reality in Zero Defect Manufacturing

01-01-2019

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31-07-2022

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Pathways
Autonomous Smart Factories Pathway
Result items:
D2.11 Reference Architecture and Blueprints (Version 1)
D2.11 Reference Architecture and Blueprints (Version 1)
Pilot - Danobat Digital Machine for zero-defects at high precision cutting/grinding
Pilot - Danobat Digital Machine for zero-defects at high precision cutting/grinding

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

Pilot - FAGOR Zero-Defects Manufacturing Digital Press Machine
Pilot - FAGOR Zero-Defects Manufacturing Digital Press Machine

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.

Pilot - GHI Real-time cognitive hot stamping furnace 4.0
Pilot - GHI Real-time cognitive hot stamping furnace 4.0

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.

Pilot - KOL’s Real-time injection moulding process monitoring-control
Pilot - KOL’s Real-time injection moulding process monitoring-control

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.

Pilot - PRIMA Additive Manufacturing Pilot Adaptive Control Technology
Pilot - PRIMA Additive Manufacturing Pilot Adaptive Control Technology

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.

Pilot - RiaStone Autonomous Quality ZDM for “Ceramic tableware Single-firing”
Pilot - RiaStone Autonomous Quality ZDM for “Ceramic tableware Single-firing”

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

Pilot - SIEMENS SIMATIC Products Quality Improvements
Pilot - SIEMENS SIMATIC Products Quality Improvements

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

Pilot - THYS Quality Management of Steering Gear based on Acoustic control
Pilot - THYS Quality Management of Steering Gear based on Acoustic control
Pilot - WHR Dryer Factory Holistic Quality Platform
Pilot - WHR Dryer Factory Holistic Quality Platform
Connected IT and OT
Result items:
Pilot - Danobat Digital Machine for zero-defects at high precision cutting/grinding
Pilot - Danobat Digital Machine for zero-defects at high precision cutting/grinding

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

Pilot - FAGOR Zero-Defects Manufacturing Digital Press Machine
Pilot - FAGOR Zero-Defects Manufacturing Digital Press Machine

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.

Pilot - KOL’s Real-time injection moulding process monitoring-control
Pilot - KOL’s Real-time injection moulding process monitoring-control
IoT enabled SCADA, MOM-MES, ERP (…) connectivity
Result items:
Pilot - GHI Real-time cognitive hot stamping furnace 4.0
Pilot - GHI Real-time cognitive hot stamping furnace 4.0

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.

Pilot - KOL’s Real-time injection moulding process monitoring-control
Pilot - KOL’s Real-time injection moulding process monitoring-control

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. 

ZDMP | Zero Defect Manufacturing Platform

01-01-2019

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30-06-2023

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Pathways
Autonomous Smart Factories Pathway
Result items:
Assembly line: AI-supported optical defects detection
Assembly line: AI-supported optical defects detection
Assembly line: monitoring and control system
Assembly line: monitoring and control system
Construction supply chain: quality control at construction site and quality traceability
Construction supply chain: quality control at construction site and quality traceability
Electronic products manufacturing: Component inspection
Electronic products manufacturing: Component inspection
Engine block manufacturing: Defects detection and prediction in aluminium injection and machining operations
Engine block manufacturing: Defects detection and prediction in aluminium injection and machining operations
Engine block manufacturing: Defects reduction by the optimization of the machining process
Engine block manufacturing: Defects reduction by the optimization of the machining process
Moulds manufacturing: Process alert system for machine tool failure prevention and Smart process parameter tuning
Moulds manufacturing: Process alert system for machine tool failure prevention and Smart process parameter tuning
Steel tubes: production monitor
Steel tubes: production monitor
Stone tiles: equipment wear detection
Stone tiles: equipment wear detection
Connected IT and OT
Result items:
Construction supply chain: quality control at construction site and quality traceability
Construction supply chain: quality control at construction site and quality traceability
Engine block manufacturing: Defects detection and prediction in aluminium injection and machining operations
Engine block manufacturing: Defects detection and prediction in aluminium injection and machining operations
Steel tubes: production monitor
Steel tubes: production monitor
Stone tiles: equipment wear detection
Stone tiles: equipment wear detection
IoT enabled SCADA, MOM-MES, ERP (…) connectivity
Result items:
Assembly line: AI-supported optical defects detection
Assembly line: AI-supported optical defects detection

The test check stations along the assembly line equipped with the cameras serving the goal of optical quality control. Data in the form of images taken within these check stations is a valuable resource that is used not only to check the quality of product, but also to improve the efficiency of quality testing programs. The images taken allow detecting, for instance, defects related to the shape of the product.

Assembly line: monitoring and control system
Assembly line: monitoring and control system

Usually the assembling of electronic components within the CONT is performed using 6-11 working stations. AS the workstations can be from different manufacturers and have no direct connection, the goal of ZDMP platform is to provide a needed middleware and services for centralized assembly line control by acquiring data from different workstations.

Electronic products manufacturing: Component inspection
Electronic products manufacturing: Component inspection

The X-Ray machine will be deployed at the CONT factory for quality analysis improvement and in-time defects detection. The analysis will be applied to materials and components used within the production process. Before the process start, machine requests the inspection program from ZDMP platform, if one is available, the process starts automatically.

Engine block manufacturing: Defects detection and prediction in aluminium injection and machining operations
Engine block manufacturing: Defects detection and prediction in aluminium injection and machining operations

To be able to make prediction and automated quality assessment, process data need to be gathered and presented in the form suitable for processing. Process data are gathered from various sensors and smart meters, as well as from PLCs at MRHS and automatically uploaded to the database. As the production cycle takes around 2 minutes, subsequently data are uploaded every 2 minutes. The ultimate goal is to receive the anomaly warnings close to real-time.

Engine block manufacturing: Defects reduction by the optimization of the machining process
Engine block manufacturing: Defects reduction by the optimization of the machining process

The machine centres operating within the plant are equipped with sensors (e.g. controlling vibrations, power consumption, etc.) supplying the process data. On the other hand, industrial computers controlling the machine also provide additional information about production process, such as process times, machine status and cylinder block type in production. All these data are captured and stored within the database to be further analysed on abnormalities and to provide recommendations on changing of certain parameters to recover production process.

Moulds manufacturing: Process alert system for machine tool failure prevention and Smart process parameter tuning
Moulds manufacturing: Process alert system for machine tool failure prevention and Smart process parameter tuning

The sensors deployed on the FORM side are used to aquire the process and the equipment data. These data are sent and stored on the ZDMP platform that is used to detect the abnormalities and failures right after they occur and immediately inform the operator, but also to be able to predict and avoid further malfunctions. The components of the ZDMP platform are used to detect any deviations from the normal production process.

The parameters of each manufacturing operation are reported to the ZDMP platform. Within ZDMP platform the parameters are analysed to identify, if selected parameters will result in the good quality and if not, how the parameters can be changed.

Moulds manufacturing: in-line 3D modelling
Moulds manufacturing: in-line 3D modelling

he collision avoidance software relies on the 3D models acquired by scanning of the working area. However, before the 3D model can be built the scanning results, also called “cloud of points”, are cleaned and processed.

Steel tubes: production monitor
Steel tubes: production monitor

The quality assurance process will be supported by the ZDMP services for steel width detection, tube shape and horizontal and vertical weld of the steel sheet quality control.

Stone tiles: equipment wear detection
Stone tiles: equipment wear detection

ZDMP platform has the goal to improve and automate the quality check on every stage of the stone slabs and tiles production. Reduce, where possible, the human involvement in the quality check to minimum, e.g. control of the wearing out of the cutting blades. Both the data about equipment performance, as well as material scanning data are utilized. Moreover, CEI machines also provide the data from cameras and projectors used to optimize the cutting process and save material.

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