Journal
FACTORIES OF THE FUTURE IN THE DIGITAL ENVIRONMENT
Volume 57, Issue -, Pages 585-590Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.procir.2016.11.101
Keywords
Optical inspection; battery separator; classification; machine learning; data mining; knowledge discovery in databases; decision trees
Funding
- Federal Ministry of Education and Research (Bundesministerium fiir Bildung und Forschung)
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The growing demand and new fields of application compel battery manufacturers to higher product quality. Thus, defect-free battery separators are a prerequisite for safe lithium-ion cells. Hence, typical production faults have to be reliably detected. Therefore, a machine vision system was developed [1]. Besides the detection of anomalies, a key element is the distinction between defect classes in order to distinguish non quality related optical effects from faults using a machine learning approach for classification. Therefore, a method consisting of five phases is described. Starting with data analysis and preparation, expert knowledge is used to derive a database. On this basis models are generated by machine learning algorithms. The best model is chosen by a weighted evaluation of the target values using a preference matrix. Furthermore, the model gets evaluated by an analysis of the confusion matrix. Finally the implementation phase serves to transfer the results into the machine vision system and to monitor occurring process drifts. Last, the functionality of the method is proven within an application example. (C) 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
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