Optimizing a batch manufacturing process through interpretable data mining models

In this paper, we present a data mining based methodology for optimizing the outcome of a batch manufacturing process. Predictive datamining techniques are applied to a multi-year set of manufacturing data with the purpose of reducing the variation of a crystal manufacturing process, which suffers from frequent fluctuations of the average outgoing yield. Our study is focused on specific defects that are the most common causes for scraping a manufactured crystal. A set of probabilistic rules explaining the likelihood of each defect as a function of interaction between the controllable variables are induced using the single-target and the multi-target Information Network algorithms. The rules clearly define the worst and the best conditions for the manufacturing process, also providing a complete explanation of all major fluctuations in the outgoing quality observed over the recent years. In addition, we show that an early detection of nearly the same predictive model was possible almost two years before the end of the data collection period, which could save many of the flawed crystals. The paper provides a detailed description of the optimization process, including the decisions taken at various stages and their outcomes. Conclusions applicable to similar engineering tasks are also outlined.