Manufacturing know-how feedback as a key means of process optimization
The enrichment of path planning through process knowledge which is collected in the production environment optimizes the NC programming from AFP systems sustainable in the age of Industrial IoT. With this credo, SWMS in collaboration with the Institute of Production Engineering and Machine Tools (IFW) provides an architecture for information feedback and the automatic, iterative optimization of path planning based on production knowledge and online monitoring results. These technologies are bundled in the CAESA® Composites Software (TapeStation).
Due to the increasing complexity of geometric component surfaces and the high structural requirements for CFRP laminates, the AFP system programming is still difficultly performed under strict rules, thus manually and with extremely long time periods. Once programming is completed, additional time consuming test methods are needed between the individual production steps which are then used to localize and classify the defects in the laminate. Components that do not successfully complete the test after they go through many steps of the process chain and cause additional costs without adding any value. The problem is the formation of defects and the time it requires to detect the defect locations.
An approach to avoid such problems is the implementation of an online inspection with an automated adaptation of the path programming. A feasible form of inspection is the thermographic monitoring during the AFP process. Possible defects are detected and classified during storage. Through the TCP mapping on the component, the location of the defects that occur can also be determined directly.
The second optimization step is to customize the path planning. Hereby, a path planning algorithm is used, which produces automated paths via an iterative process for manufacturing the laminate with respect to the intended manufacturing requirements. In one ply, the courses are aligned so that all the gaps and overlaps are contained within the specified tolerances and to prevent undulations in the tow at the same time. As a learning process, the algorithm derives additional rules from the experience of the online monitoring to also achieve results for new components that meets the structural requirements of the aerospace and automotive industries.
The implementation of the information flow between the physical and virtual system was carried out by SWMS in cooperation with the IFW Hannover. Here, the knowledge of the production engineers in the form of quality requirements for the component and automatically evaluated data from the thermographic monitoring is very helpful. Thereby, this positively affects the “time to market” for the CFRP components in the industrial IoT context of the system.
Figure 1: Learning path planning based on recovered production results