Explainable Predictive Quality Inspection using Deep Learning in Electronics Manufacturing

The linkage of machines in the context of Industry 4.0 through information and communication technology (ICT) to cyber-physical systems with the aim of monitoring, controlling, and optimizing complex production systems, enables real-time capable approaches for data acquisition, analysis, and process...

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Bibliographic Details
Published in:Procedia CIRP 2022, Vol.107, p.594-599
Main Authors: Saadallah, Amal, Büscher, Jan, Abdulaaty, Omar, Panusch, Thorben, Deuse, Jochen, Morik, Katharina
Format: Article
Language:English
Subjects:
Deep Learning
Edge Cloud Computing
Electronics Manufacturing
Explanations
Heat-maps
Process Quality Prediction
Sensor Data
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Summary:The linkage of machines in the context of Industry 4.0 through information and communication technology (ICT) to cyber-physical systems with the aim of monitoring, controlling, and optimizing complex production systems, enables real-time capable approaches for data acquisition, analysis, and process knowledge generation. In this context, surface mount technology (SMT) in electronics manufacturing is increasingly enhanced by digitalizing the process. This allows the collection and analysis of sensor data to predict the process quality in real-time. Process control interventions can then be derived in a timely manner based on quality predictions. To further support decision-making for process control by domain experts, explanations for the model-based quality predictions should be supplemented in addition. More specifically, we employ a 1D-convolutional neural network for quality prediction of well-defined Fields Of Views (FOVs) of Printed Circuit Boards (PCBs). Explanations for the model’s predictions are provided under various perspectives using a heat-mapping-based technique to highlight the contribution of both local and global PCBs’ characterizing features to the quality predictions. This helps to reveal the most decisive features for a given quality assignment and understand which process parts are the most responsible for such decision. Finally, the deployment of the model-based predictive and parts of the prescriptive analytics supported by the provided explanations, are achieved using Edge Cloud Computing technology.
ISSN:2212-8271
2212-8271
DOI:10.1016/j.procir.2022.05.031