Description
Assembly systems are an integral part of the manufacturing industry, especially in the German automotive and mechanical engineering sectors. The focus of this work is on complex assembly process chains (MPK) in large-scale production. In these systems every single process step is defined with several parameters in narrow process windows and they are robustly located there. Nevertheless, serial defects occur which are very difficult to detect. On the one hand, all parameters along the MPK are within the limits and have a good to very good Cp value. On the other hand, there are events in MPK that lead to rejects, although each individual process and its parameters are excellently located within the tolerance limits.
The focus of this work is on the cross-process, data-driven analysis of MPK. In the context of E|ASY-Class and E|ASY-Pat, the discovery of knowledge in process curves is addressed and the process visualization E|ASY-Heat allows for a continuous change analysis of the process behavior. For the analysis of MPK, E|ASY-Inter visualizes complex interactions between assembly processes and the tested defective products in function fulfillment to identify the causes of faults. E|ASY[1]Event recognizes spontaneous changes with a damaging effect on the functional fulfillment of the assembled product by means of signal propagation along complex assembly systems. For detailed analysis, E|ASY-Curves supports the visual evaluation of process curves by means of 3D representation.
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