The machines of ETXE industrial partner are installed on the FORD plant as a part of cylinder block production line. The raw process data are collected about the equipment state, as well as about the production process itself (e.g. type of cylinder blocks being produced at the moment). These data are utilized in order to detect anomalies/abnormalities and to develop a mitigation plan to return to the efficient production state. In mitigation plan development the ETXE equipment supplier has one of the leading roles. The ZDMP platform, in this case, has the goal to assist in the fulfilment of this task, through knowledge generation and automation of abnormalities detection.
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.
During the production process, various deviations from the normal functioning of the machining equipment can arise in the given use-case. It is important that ETXE equipment manufacturer could provide a service addressing the whole life cycle of the product (machining equipment) supported by ZDMP platform. The service has the aim of keeping the high efficiency of the machines through detection of abnormalities and quick recovery to the efficient state.
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.
In this use-case the main data supplier is the FORD manufacturing facility. The other industrial partner provides the services around the life cycle of the product (machining equipment) deployed within the FORD facility. These services are only possible under the extensive data exchange process between industrial partners (assistance from ZDMP platform), so that the ETXE – equipment manufacturer/supplier, can assist in terms of maintaining the quality of equipment operation.
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.
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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.