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 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.

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).

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.

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.

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.

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.

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.

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.

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.

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

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.

In the case of the negative automatic test, operator performs the manual check of the product comparing it with the reference images. The operator decisions with corresponding images are collected and stored to learn or extract the defects types and acceptance limits.

The ZDMP platform targets more the assembly line layer, than separate workstations. The idea is to aggregate the information coming from the workstations along the assembly line and provide the quality control and performance services.

Coordination of the activities within the construction plays a crucial role. Delays in materials shipping and materials quality issues may significantly affect the construction process. However, not all delays have the same impact. ZDMP platform provides the necessary assistance for delays’ impact assessment and enables agile information exchange among involved partners.

The quality control is important stage of every production process, as a defected part can significantly affect the product functionality. To be able to find possible defects at the earliest possible stage and minimizing the effect on the whole production process at the factory scale the X-Ray inspection machine in conjunction with ZDMP platform are utilized.

The assistance in quality control that is offered by ZDMP platform through the timely warning allows reduction of the amount of waste and increase in the number of quality products per meter of steel sheet.

Reduction of the scrap output of the production process, as well as automation of control check operations has significant impact on the quality of the products and leads to a more efficient use of resources.

The operation of machining equipment installed in production line can be optimised and improved through analysis of the process data acquired from the equipment. In some cases the optimisation process can be done by the platform. However, some cases might require involvement of engineers from equipment manufacturer to analyse data and provide recommendations on optimisation. The effectiveness of recommendations can be further assessed by the platform.

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.

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.

Machinery provided by PTM will be updated with ZDMP tools for assistance of the manufacturing process.

In order to indetify the wearing out of the cutting blades, the machine is equipped with additional sensor that can detect any deviations from the normal functioning.

The ZDMP platform provides an optimisation services for the quality check performed within the CONT assembly line, resulting in the reduction of false-positives during the automatic test, as well as creating the models based on operator decisions used for generation of acceptance patterns.

In this use-case ZDMP platform provides a middleware between the workstations and the database keeping the production process details. Moreover, it provides a set of services for the production process improvement.

ZDMP platform allows automated exchange of the critical information that can affect the schedule. Ability to adjust the activities regarding potential delays has significant impact on the construction consortia performance.

The ZDMP platform assists the process of quality inspection, while providing a library inspection programs for specific materials/components. Moreover, to understand the tendency over time current measurements can be compared with historical ones.

The process data required for anomaly detection and production process optimization are gathered from multiply sources both in MRHS and in FORD and aggregated to predict the quality of the product and a rejection probability. Based on the data gathered, the model for parameters’ optimization is generated to achieve a certain, user-defined, objective.

The ZDMP platform offers for both industrial partners an opportunity to improve the communication, through knowledge generation from the raw data. The platform also offers a service for the equipment optimisation to improve the production process.

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.

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.

In this use-case significant impact is made by the tools provided by ZDMP platform addressing the automation of the quality control process reducing the load on operators.

Utilization of ZDMP platform with corresponding services allows optimization of production process in terms of automation of production and natural defects and better use material through spatial mould optimization.