Machine learning techniques for quality control in high conformance manufacturing environment
In today’s highly competitive global market, winning requires near-perfect quality. Although most mature organizations operate their processes at very low defects per million opportunities, customers expect completely defect-free products. Therefore, the prompt detection of rare quality events has b...
Main Authors: | , |
---|---|
Format: | Article |
Language: | English |
Published: |
SAGE Publishing
2018-02-01
|
Series: | Advances in Mechanical Engineering |
Online Access: | https://doi.org/10.1177/1687814018755519 |
id |
doaj-df2dddc82d8945dbbdf1174d891aa693 |
---|---|
record_format |
Article |
spelling |
doaj-df2dddc82d8945dbbdf1174d891aa6932020-11-25T01:20:26ZengSAGE PublishingAdvances in Mechanical Engineering1687-81402018-02-011010.1177/1687814018755519Machine learning techniques for quality control in high conformance manufacturing environmentCarlos A Escobar0Ruben Morales-Menendez1Dean of Graduate Studies, Tecnológico de Monterrey, Monterrey, MéxicoDean of Graduate Studies, Tecnológico de Monterrey, Monterrey, MéxicoIn today’s highly competitive global market, winning requires near-perfect quality. Although most mature organizations operate their processes at very low defects per million opportunities, customers expect completely defect-free products. Therefore, the prompt detection of rare quality events has become an issue of paramount importance and an opportunity for manufacturing companies to move quality standards forward. This article presents the learning process and pattern recognition strategy for a knowledge-based intelligent supervisory system, in which the main goal is the detection of rare quality events. Defect detection is formulated as a binary classification problem. The l 1 -regularized logistic regression is used as the learning algorithm for the classification task and to select the features that contain the most relevant information about the quality of the process. The proposed strategy is supported by the novelty of a hybrid feature elimination algorithm and optimal classification threshold search algorithm. According to experimental results, 100% of defects can be detected effectively.https://doi.org/10.1177/1687814018755519 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Carlos A Escobar Ruben Morales-Menendez |
spellingShingle |
Carlos A Escobar Ruben Morales-Menendez Machine learning techniques for quality control in high conformance manufacturing environment Advances in Mechanical Engineering |
author_facet |
Carlos A Escobar Ruben Morales-Menendez |
author_sort |
Carlos A Escobar |
title |
Machine learning techniques for quality control in high conformance manufacturing environment |
title_short |
Machine learning techniques for quality control in high conformance manufacturing environment |
title_full |
Machine learning techniques for quality control in high conformance manufacturing environment |
title_fullStr |
Machine learning techniques for quality control in high conformance manufacturing environment |
title_full_unstemmed |
Machine learning techniques for quality control in high conformance manufacturing environment |
title_sort |
machine learning techniques for quality control in high conformance manufacturing environment |
publisher |
SAGE Publishing |
series |
Advances in Mechanical Engineering |
issn |
1687-8140 |
publishDate |
2018-02-01 |
description |
In today’s highly competitive global market, winning requires near-perfect quality. Although most mature organizations operate their processes at very low defects per million opportunities, customers expect completely defect-free products. Therefore, the prompt detection of rare quality events has become an issue of paramount importance and an opportunity for manufacturing companies to move quality standards forward. This article presents the learning process and pattern recognition strategy for a knowledge-based intelligent supervisory system, in which the main goal is the detection of rare quality events. Defect detection is formulated as a binary classification problem. The l 1 -regularized logistic regression is used as the learning algorithm for the classification task and to select the features that contain the most relevant information about the quality of the process. The proposed strategy is supported by the novelty of a hybrid feature elimination algorithm and optimal classification threshold search algorithm. According to experimental results, 100% of defects can be detected effectively. |
url |
https://doi.org/10.1177/1687814018755519 |
work_keys_str_mv |
AT carlosaescobar machinelearningtechniquesforqualitycontrolinhighconformancemanufacturingenvironment AT rubenmoralesmenendez machinelearningtechniquesforqualitycontrolinhighconformancemanufacturingenvironment |
_version_ |
1725134100926300160 |