摘要：In multi-stage manufacturing environments, reworking at an early stage can prevent costly defects at later stages. One goal of the EU research project ZAero (Zero-defect manufacturing of composite parts in the aerospace industry) is to generate feature data by implementing an inline quality control for the manufacturing process of carbon fibre components of aircraft. However, for each feature detected by ZAero’s inline quality control, operators must decide whether or not to rework that feature. Additional rework of non defects can reduce expensive defects in later stages, but the increased effort should not have a significant impact on production. To help operators make the right decisions, an extensible hybrid decision support system (DSS) is proposed, which combines a software application that visualizes 3D-based process-specific feature data and supports the execution of rework decisions with web-based business analytics dashboards. The dashboards visualize data generated by part flow simulation experiments for various rework strategies, as well as data from a manufacturing execution system (MES). The proposed DSS can be easily customized to integrate additional data treasures from the ever-increasing amount of data in the industrial sector.
关键词：KeywordsDecision Support SystemsAerospace EngineeringEdge ComputingFog-Based AnalyticsDiscrete Event SimulationIndustrial Internet of ThingsBusiness IntelligenceDigital TwinZero-Defect Manufacturing
