Through visual analytics

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.

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

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.

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.

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

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

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

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

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.

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.

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

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

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. 

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 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.

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.

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.

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 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.

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.

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).

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.

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.

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.

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

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

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.

IMR IIoT Toolkit developed for factory deployment.  High voltage power components separated in custom enclosures.

It includes a range of sensors, IIoT edge components which perform data collection and aggregation at the edge of the network and which then sends the data to IMR’s IIoT Platform which can either be installed locally, be based in the cloud or even both. 

IIoT Toolkit software connectors allow interfaces to be established with operational technologies such as BMS over BACnet or EMS over HTTP.

Over 700 data points from heating, cooling and ventilation systems are supplied to Building Management System via BACnet controllers.

IMR are using our IIoT Platform installed on-site to read this data from the BACnet controllers.  We supplement it with data from IMR sensors (cleanroom occupancy, particle counts, PIRs, door sensors).

This data is then sent in real-time to a containerised cloud-based IIoT Platform where it can be accessed by the Energy Team and the Data Analytics Team.

Integration of each subsystem and subsystem components is done via AAS data models and applications. Each individual inspection station component is represented by its own AAS which provides relevant information about asset as well as the control interface (where applicable). AAS application can communicate to each other via OPC-UA communication protocol.

The main goal of the station is to demonstrate how the AAS architecture could be implemented, what are the benefits, what still needs to be addressed.

Siemens Mindsphere will be used to collect and analyse data from the shop floor; cloud-based.

Amazon Web Services – Currently used to host cloud data and machine learning algorithms.

Siemens PLM XML  - Data standard used to define process data from Teamcenter and send to the SCADA and to PLCs.

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.

If the machine suffers major or unexpected failure, the machine is likely to be stopped. However, some other problems, such as components wearing, can lead to significant degradation in performance. In this regard, an early diagnosis of the defects and early detection of degradation signs reduces production process time. Moreover, introduction of preventive measures, in terms, for instance, of parameter adjustment, allows quality improvement and reduction of defected parts.

The ZDMP platform which is deployed outside of the FORM facility, allows for FORM to reduce the maintenance and investment costs for an internal platform that is important for SME. Moreover, ZDMP platform, as data and knowledge aggregator can be utilized by all industrial partners in order to optimise the production process.

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.

The operator is involved in the working area scanning process. Afterwards the scanning results can be automatically sent to the ZDMP platform to be further processed and converted into the .stl format.

Besides the anomalies caused, for instance, by equipment degradation, this use-case targets the human error called collision. Some common collisions identified by the industrial partners are: movement of the milling head crashing into workpiece or machine itself or the CAD/CAM model defines paths involving movements that cause a crash. Collision avoidance is critical for machine damage prevention, as well as product quality maintenance.

The ZDMP platform which is deployed outside of FORM, allows for FORM to simplify the process for data acquisition and processing enabling quick and effortless way of anti-collision system utilization.