Designing a Digital Shadow for Efficient, Low-Delay Analysis of Production Quality Risk

Abstract

Manufacturers in the automotive industry extensively rely on iterative process Failure Mode and Effects Analysis (FMEA) in their quality management. Process FMEAs model technical impacts based on engineering artifacts. However, traditional process FMEA methods are typically document-centric and do not integrate feedback from the shop floor without significant delay. In this paper, we introduce the Digital Shadow for Production Quality Analysis (DS-PQA) method and system design that integrates feedback from machine components on the shop floor. To this end, the method leverages FMEA and production system engineering models to configure OPC Unified Architecture (OPC UA)-based data acquisition. Based on that data acquisition, the DS-PQA system analyzes data from the shop floor to (i) inform operators on likely causes of a production defect, and (ii) alert FMEA experts about FMEA causes that occur more frequently than expected and should be re-validated. In a feasibility study, we evaluated the effectiveness and efficiency of the DS-PQA method and system on a welding cell that manufactures automotive parts. The study results indicate that the DS-PQA method and system are feasible, more efficient, and can substantially lower the latency for analyzing production quality risk compared to a traditional approach.