Machine learning based defect detection in a low automated assembly environment

被引：3

作者：

Schuh, G. ^{[1
]}

Guetzlaff, A. ^{[1
]}

Thomas, K. ^{[1
]}

Welsing, M. ^{[1
]}

机构：

[1] Rhein Westfal TH Aachen, Lab Machine Tools & Prod Engn WZL, D-52074 Aachen, Nrw, Germany

来源：

54TH CIRP CONFERENCE ON MANUFACTURING SYSTEMS 2021-TOWARDS DIGITALIZED MANUFACTURING 4.0, CMS 2021 | 2021年 / 104卷

关键词：

Production Systems; Assembly; Machine learning; Defect detection; Prediction methods; Quality management;

D O I：

10.1016/j.procir.2021.11.045

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Machine learning algorithms offer great potential for quality control in manufacturing. A great share of manufacturing operations are assembly tasks, which are often performed manually. Unlike automatic processes, manual assembly offers a limited amount of recorded data, that can be used as an input for a machine learning model. This study investigates, if the assembly times that are recorded by the conveyor technology, are enough to train machine learning models and how different models perform on this limited data input. To conduct this investigation, the study utilizes a real data set from the automotive industry.

引用

页码：265 / 270

页数：6

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