These use-cases come from the machine tools domain encompassing three industrial partners: (i) HSD – the electro spindle manufacturer, (ii) FIDIA – the manufacturer of high-speed milling systems and flexible manufacturing systems using the HSD spindles in their products, and (iii) FORM – enterprise for maintenance and modification of the of the large plastic injection moulds utilizing machines from FIDIA. The goal is to collect the equipment and machining process data to detect the abnormalities in the production process and inform the operator on the FORM side. In the case, if the quality control detects a scrap part the machine tools user (FORM) can ask FIDIA for to identify the cause of the defect, while FIDIA, in its turn, can acquire help from the HSD, if the problem is with the electro spindle. Another goal is to be able to adjust the parameters of the production process to achieve the optimal quality results.
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.
The quality assessment of the products is performed by the FORM industrial partner that utilizes equipment delivered by FIDIA. Degradation in the equipment performance can cause significant quality drop on the FORM side. In order to avoid this, ZDMP platform aggregates the process and equipment data and performs analysis to identify the defects occurring, as well as to detect possible equipment degradation. Analysis of equipment degradation is preformed in cooperation with equipment suppliers.
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.
The main data supplier is the FORM industrial partner. After the data are gathered, they are sent and stored within the ZDMP platform deployed outside of FORM. The data are critical to feed ZDMP platform providing the quality control services. Additionally, data can be shared with FIDIA equipment manufacturer to assist with equipment-related quality problems.
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.
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.