Machine Learning Methods for Quality Prediction in Production

Sidharth Sankhye and Guiping Hu
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Sidharth Sankhye: Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, IA 50011, USA
Guiping Hu: Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, IA 50011, USA

Logistics, 2020, vol. 4, issue 4, 1-19

Abstract: The rising popularity of smart factories and Industry 4.0 has made it possible to collect large amounts of data from production stages. Thus, supervised machine learning methods such as classification can viably predict product compliance quality using manufacturing data collected during production. Elimination of uncertainty via accurate prediction provides significant benefits at any stage in a supply chain. Thus, early knowledge of product batch quality can save costs associated with recalls, packaging, and transportation. While there has been thorough research on predicting the quality of specific manufacturing processes, the adoption of classification methods to predict the overall compliance of production batches has not been extensively investigated. This paper aims to design machine learning based classification methods for quality compliance and validate the models via case study of a multi-model appliance production line. The proposed classification model could achieve an accuracy of 0.99 and Cohen’s Kappa of 0.91 for the compliance quality of unit batches. Thus, the proposed method would enable implementation of a predictive model for compliance quality. The case study also highlights the importance of feature construction and dataset knowledge in training classification models.

Keywords: machine learning; quality; classification (search for similar items in EconPapers)
JEL-codes: L8 L80 L81 L86 L87 L9 L90 L91 L92 L93 L98 L99 M1 M10 M11 M16 M19 R4 R40 R41 R49 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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