Classification of causes for plastic product defects by machine learning and application for the training of workers

In recent years, with the maturing of society and advances in technology, consumers' demand for manufacturing has been increasing. Of particular note in demand is the appearance quality of the product. Under visual inspection at the time of manufacture, skilled workers have classified the c...

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Autores principales:	Tsuneo NAGANUMA, Koichi HASHIMOTO
Formato:	article
Lenguaje:	EN
Publicado:	The Japan Society of Mechanical Engineers 2021
Materias:	machine learning decision tree anomaly detection classification big data worker training Mechanical engineering and machinery TJ1-1570
Acceso en línea:	https://doaj.org/article/e396d4e2049745f6a5ee4fd2ba972446
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spelling	oai:doaj.org-article:e396d4e2049745f6a5ee4fd2ba9724462021-11-29T06:05:55ZClassification of causes for plastic product defects by machine learning and application for the training of workers2187-974510.1299/mej.20-00424https://doaj.org/article/e396d4e2049745f6a5ee4fd2ba9724462021-03-01T00:00:00Zhttps://www.jstage.jst.go.jp/article/mej/8/2/8_20-00424/_pdf/-char/enhttps://doaj.org/toc/2187-9745In recent years, with the maturing of society and advances in technology, consumers' demand for manufacturing has been increasing. Of particular note in demand is the appearance quality of the product. Under visual inspection at the time of manufacture, skilled workers have classified the causes of defects in the product's appearance according to their experience and have dealt with them quickly. Due to a serious shortage of workers and the aging of skilled workers, there are many opportunities for inexperienced workers, such as foreign technical interns, to take charge of the work at manufacturing sites. While introducing "automatic visual inspection by a camera," the authors have developed a system that can automatically classify the causes of defects. Also, when the classification work was carried out for the inexperienced workers, the application to the education was seen. The authors propose a classification method based on machine learning with skilled workers' knowledge. This paper analyzes the process of classifying production data into the causes of contaminated products (CP) by skilled workers. First, the occurrence interval of CP was divided into sparse or dense groups. Second, a decision tree learned from causes' labels with a skilled worker was developed as a group label classifier (GLC). When production data were used to validate the prediction capability, high accurate predictions were obtained. This indicates that even inexperienced workers can take measures according to the cause of product defects during production, which is useful for the education of field workers by the GLC.Tsuneo NAGANUMAKoichi HASHIMOTOThe Japan Society of Mechanical Engineersarticlemachine learningdecision treeanomaly detectionclassificationbig dataworker trainingMechanical engineering and machineryTJ1-1570ENMechanical Engineering Journal, Vol 8, Iss 2, Pp 20-00424-20-00424 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	machine learning decision tree anomaly detection classification big data worker training Mechanical engineering and machinery TJ1-1570
spellingShingle	machine learning decision tree anomaly detection classification big data worker training Mechanical engineering and machinery TJ1-1570 Tsuneo NAGANUMA Koichi HASHIMOTO Classification of causes for plastic product defects by machine learning and application for the training of workers
description	In recent years, with the maturing of society and advances in technology, consumers' demand for manufacturing has been increasing. Of particular note in demand is the appearance quality of the product. Under visual inspection at the time of manufacture, skilled workers have classified the causes of defects in the product's appearance according to their experience and have dealt with them quickly. Due to a serious shortage of workers and the aging of skilled workers, there are many opportunities for inexperienced workers, such as foreign technical interns, to take charge of the work at manufacturing sites. While introducing "automatic visual inspection by a camera," the authors have developed a system that can automatically classify the causes of defects. Also, when the classification work was carried out for the inexperienced workers, the application to the education was seen. The authors propose a classification method based on machine learning with skilled workers' knowledge. This paper analyzes the process of classifying production data into the causes of contaminated products (CP) by skilled workers. First, the occurrence interval of CP was divided into sparse or dense groups. Second, a decision tree learned from causes' labels with a skilled worker was developed as a group label classifier (GLC). When production data were used to validate the prediction capability, high accurate predictions were obtained. This indicates that even inexperienced workers can take measures according to the cause of product defects during production, which is useful for the education of field workers by the GLC.
format	article
author	Tsuneo NAGANUMA Koichi HASHIMOTO
author_facet	Tsuneo NAGANUMA Koichi HASHIMOTO
author_sort	Tsuneo NAGANUMA
title	Classification of causes for plastic product defects by machine learning and application for the training of workers
title_short	Classification of causes for plastic product defects by machine learning and application for the training of workers
title_full	Classification of causes for plastic product defects by machine learning and application for the training of workers
title_fullStr	Classification of causes for plastic product defects by machine learning and application for the training of workers
title_full_unstemmed	Classification of causes for plastic product defects by machine learning and application for the training of workers
title_sort	classification of causes for plastic product defects by machine learning and application for the training of workers
publisher	The Japan Society of Mechanical Engineers
publishDate	2021
url	https://doaj.org/article/e396d4e2049745f6a5ee4fd2ba972446
work_keys_str_mv	AT tsuneonaganuma classificationofcausesforplasticproductdefectsbymachinelearningandapplicationforthetrainingofworkers AT koichihashimoto classificationofcausesforplasticproductdefectsbymachinelearningandapplicationforthetrainingofworkers
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Classification of causes for plastic product defects by machine learning and application for the training of workers

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