Advanced Process Defect Monitoring Model and Prediction Improvement by Artificial Neural Network in Kitchen Manufacturing Industry: a Case of Study

Abstract

This paper analyzes a methodological approach based on p control charts able to control defects into a new production line of kitchen manufacturing and to monitor the proportion of nonconforming units of the specific cutting process. The control is enabled by measuring the defects of different samples of pieces of kitchens. The proposed approach is improved by artificial neural networks -ANN- predicting defect entity by defining a new model to improve quality and production control. The ANN algorithm is suitable for predictive maintenance of kitchen production lines and for product quality prediction. The study is completed by the correlation matrix analysis, thus providing an alternative approach to interpret defect causes. The proposed model has been developed within the framework of a research industry project.

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