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Machine learning-based control charts for monitoring fraction nonconforming product in smart manufacturing

Simone Massulini Acosta (Department of Eletronics, Universidade Tecnologica Federal do Parana, Curitiba, Brazil)
Angelo Marcio Oliveira Sant'Anna (Polytechnic School, Federal University of Bahia, Salvador, Brazil)

International Journal of Quality & Reliability Management

ISSN: 0265-671X

Article publication date: 31 January 2022

Issue publication date: 24 February 2023

605

Abstract

Purpose

Process monitoring is a way to manage the quality characteristics of products in manufacturing processes. Several process monitoring based on machine learning algorithms have been proposed in the literature and have gained the attention of many researchers. In this paper, the authors developed machine learning-based control charts for monitoring fraction non-conforming products in smart manufacturing. This study proposed a relevance vector machine using Bayesian sparse kernel optimized by differential evolution algorithm for efficient monitoring in manufacturing.

Design/methodology/approach

A new approach was carried out about data analysis, modelling and monitoring in the manufacturing industry. This study developed a relevance vector machine using Bayesian sparse kernel technique to improve the support vector machine used to both regression and classification problems. The authors compared the performance of proposed relevance vector machine with other machine learning algorithms, such as support vector machine, artificial neural network and beta regression model. The proposed approach was evaluated by different shift scenarios of average run length using Monte Carlo simulation.

Findings

The authors analyse a real case study in a manufacturing company, based on best machine learning algorithms. The results indicate that proposed relevance vector machine-based process monitoring are excellent quality tools for monitoring defective products in manufacturing process. A comparative analysis with four machine learning models is used to evaluate the performance of the proposed approach. The relevance vector machine has slightly better performance than support vector machine, artificial neural network and beta models.

Originality/value

This research is different from the others by providing approaches for monitoring defective products. Machine learning-based control charts are used to monitor product failures in smart manufacturing process. Besides, the key contribution of this study is to develop different models for fault detection and to identify any change point in the manufacturing process. Moreover, the authors’ research indicates that machine learning models are adequate tools for the modelling and monitoring of the fraction non-conforming product in the industrial process.

Keywords

Acknowledgements

The authors thank the financial support from the National Council of Scientific and Technological Development (CNPq), (309812/2021-6) and the anonymous reviewers for their valuable suggestions which contributed to improving the paper quality.

Disclosure statement: The authors declare that no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Conflict of interest: The authors declare that no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Citation

Acosta, S.M. and Oliveira Sant'Anna, A.M. (2023), "Machine learning-based control charts for monitoring fraction nonconforming product in smart manufacturing", International Journal of Quality & Reliability Management, Vol. 40 No. 3, pp. 727-751. https://doi.org/10.1108/IJQRM-07-2021-0210

Publisher

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Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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