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.

In this case, the FORM industrial partner gets support from the ZDMP platform and the FIDIA equipment manufacturer. Thus, ZDMP platform provides FORM with the analytical tools for defects detection and prediction/prevention. On the other hand, FIDIA comes into play when non-trivial problems related to equipment behaviour occur.

The ZDMP platform acquires the process and equipment data from FORM and provides a set of innovative services, namely: (i) parameter adjustment, (ii) operator alerting in case of defect, (iii) correlation among various parameters related to equipment and parameters selected. In the case, when equipment manufacturers need to be involved in the problem solving, data can be securely shared through ZDMP platform.

ZDMP platform quality control services allows for FORM to timely react on the deviations within the manufacturing process. Depending on the data acquisition frequency and number of machine components the data size can reach up to 10MB per production hour. The recommendations generated by ZDMP platform can be utilized for actions planning towards quality improvement.

ZDMP platform, acquiring data from FORM, provides predictive services related to, for instance, equipment degradation dynamics (especially moving parts of the machine) to be able to timely react and replace or fix the corresponding part.

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.

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.

After the inspection process finished, a report is produced and if the product or material corresponds to the specifications the production process continues, but if some deviations are detected, report is sent to the operator for detailed check.

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 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 usage of ZDMP platform in conjunction with the X-Ray machine allows to automate the quality check process and to improve the inspection scope (as before only few items from each lot of materials/components are checked on compliance with specification due to time constraints). The new approach allows extending the number of inspected parts, only requiring operator involvement, when the deviations from specifications are detected.

The reports or feedback generated as result of the quality inspection is shared with material/component suppliers. Based on these reports further actions towards quality improvement can be undertaken.

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.

The assembly process is mostly automated. However, some manufacturing stages, as well as some quality operations are performed manually. In this regard, the production process can be improved when quality and performance details can be delivered in time and to the right person.

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.

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.

All workstations are connected to the driver layer allowing equipment to communicate with server line layer. The data extracted from the equipment are stored, used by services and made ready for visualization.

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.

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.

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 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.

ZDMP platform assists the internal quality check system. The quality check stations along the assembly line provide the images for optical quality control. These data along with quality test results are accumulated and analysed by ZDMP platform, and the feedback is generated.

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.

In some cases, FORD production engineer has to contact machine builder to get the recommendations on improving the machining process, while sending the process data to the equipment manufacturer. On the EXTE side the data undergo further analysis to provide recommendation on production process improvement. The recommended actions are manually introduced into the ZDMP platform. Afterward, the platform can assess the effectiveness of provided recommendations and improve its knowledge base.

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.

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 data generated within the FORD facility are shared with the equipment supplier ETXE with assistance of the ZDMP platform. The local instance of the ZDMP platform is hosted on the FORD side ensuring data protection and confidentiality, while the communication with ETXE is granted. The raw data, as well as aggregated knowledge by ZDMP platform are shared with ETXE for to introduce the recommendations on production process optimization.

ZDMP platform intends to provide a secure and confident environment, where the industrial partners can exchange their data. The cylinder block manufacturer (FORD) provides the process data to the equipment supplier ETXE, the last, in its turn, provides recommendations on optimization of the production process (e.g. adjustment of parameters, etc.). Moreover, ZDMP platform aims at performing some reasoning actions to simplify the data exchange between the industrial partners.

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.

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.

In UC 1.1 / UC 1.2 use-cases, both industrial partners exchange the data from the various end devices (sensors) in order to achieve the cumulative effect in terms of defects reduction. Thus, the MRHS – supplier of aluminium cylinder blocks, can benefit from the data sharing between the MRHS and FORD to improve the quality of products needed for the engines assembly.

The service proposed doesn’t require big hardware installation, but rather makes use of the data gathered from already installed field level devices (sensors, smart meters, cameras) having the supportive or supplementary role in the production process. However, the added value is in reduction of the number of defected parts or products through extensive utilization of the data-rich environment.

ZDMP platform supports the industrial partners by collecting the process data from MRHS and FORD and providing reasoning to be able to detect and predict anomalies and provide some basic recommendation on improvement of the manufacturing process on the MRHS side. Both companies make use from this service, as they are long-term value-chain partners and interested in the costs and scrap output reduction resulting in the improved manufacturing process.

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.

One of the goals of the use-case is to minimize the human involvement in the quality assessment process. However, it is not always feasible, as for instance to detect natural defects of material (stone), but still operator can get significant assistance from ZDMP platform and corresponding services to automatically detect some defects.

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.

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.

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.

ZDMP platform makes use of variety of data acquired about machines operation and quality of material. In this way, it is possible to improve the efficiency of production process and reduce the scrap output that can be caused by degradation of equipment parts

The stages of production process are tightly interconnected. If ZDMP platform detects the wearing out of the cutting blades, the production process has to be stopped not to possibly waste the material. Or, if some natural defect of material is being detected, it can be still possible to use the material through adjustment of the moulds and avoid the defected part.

The material manufacturer stores in the platform the information concerning the production of a specific lot, including production quality control information. The work contractor is informed about the type and amount of material that are shipped to the construction side with estimated time of arrival. If some delays occur, the corresponding application running on ZDMP platform provides an assessment of the delay’s impact on the schedule and suggestions/recommendation for rescheduling.

Each user will have different levels of interaction with the ZDMP platform. Both contractor and supervisor should have access to the construction schedule, but their own task schedules should only be accessible to each of them. Similarly, the Supplier will not have access to the Supervisor or Works Contractor’s areas and vice-versa.

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.

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 material manufacturer stores in the platform the information concerning the production of a specific lot. This information is automatically generated by the ZDMP platform applications or updated manually. The volume of data is considerable and, for construction materials, needs to be stored for the certain period of time defined by the legal prescriptions.

This use-case only includes only 3 industrial partners. However, the model is scalable and can be applied, to the case with more partners, for instance, the material suppliers. Moreover, different access levels and roles allow effectively regulate type and amount of information one or another partner can get access to.

The material suppliers provide the product quality data, as well as data about material types and amount to be delivered. Thus, it is possible for construction to react in agile way and reschedule in the case of unexpected accidents.

It is important, not only for the construction company, but also for the material suppliers to track the amount of shipped materials and quality, and, if needed, coordinate and adjust activities.

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.

Through utilization of ZDMP platform, the operator will get a notification, if a defect is detected. This releases the quality operator from their cursory monitoring task, and it is moved into a reactive role. Before, the operator was in charge for manual detection of possible defects. In its turn, ZDMP platform has the goal to reduce the load on operator and make the production process and quality control more self-reliant.

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.

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

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.

The utilization of ZDMP services by machinery that is used in the steel tubes production lines allows improving the quality check process and improving the product capabilities (machinery).

Based on the state of the cells and the robot's current pose an algorithm calculates  the next task.  The task is decomposed into a set of robot actions, navigation, manipulation or material transfer to a production cell. 

There are four production cells:

• Wash cell -  Wash  printed parts
• Sort cell - Sorts parts into trays
• Cure cell - Human loads curing oven
• 3D Print cell - prints parts on plates

The assembly line's current state can be viewed in and altered in real time using an on-line user interface