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Using machine learning prediction models for quality control a case study from the automotive industry

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Bibliographic Details
Main Authors: Mohamed Kais, Msakni, Anders, Risan, Peter, Schütz
Format: Book
Language:English
Published: Springer 2023
Subjects:
LSTM
random forest.
Online Access:https://link.springer.com/article/10.1007/s10287-023-00448-0
https://dlib.phenikaa-uni.edu.vn/handle/PNK/8431
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spelling oai:localhost:PNK-84312023-05-10T01:19:05Z Using machine learning prediction models for quality control a case study from the automotive industry Mohamed Kais, Msakni Anders, Risan Peter, Schütz LSTM random forest. CC BY This paper studies a prediction problem using time series data and machine learning algorithms. The case study is related to the quality control of bumper beams in the automotive industry. These parts are milled during the production process, and the locations of the milled holes are subject to strict tolerance limits. Machine learning models are used to predict the location of milled holes in the next beam. By doing so, tolerance violations are detected at an early stage, and the production flow can be improved. A standard neural network, a long short term memory network (LSTM), and random forest algorithms are implemented and trained with historical data, including a time series of previous product measurements. Experiments indicate that all models have similar predictive capabilities with a slight dominance for the LSTM and random forest. 2023-05-10T01:19:05Z 2023-05-10T01:19:05Z 2023 Book https://link.springer.com/article/10.1007/s10287-023-00448-0 https://dlib.phenikaa-uni.edu.vn/handle/PNK/8431 en application/pdf Springer
institution Digital Phenikaa
collection Digital Phenikaa
language English
topic LSTM
random forest.
spellingShingle LSTM
random forest.
Mohamed Kais, Msakni
Anders, Risan
Peter, Schütz
Using machine learning prediction models for quality control a case study from the automotive industry
description CC BY
format Book
author Mohamed Kais, Msakni
Anders, Risan
Peter, Schütz
author_facet Mohamed Kais, Msakni
Anders, Risan
Peter, Schütz
author_sort Mohamed Kais, Msakni
title Using machine learning prediction models for quality control a case study from the automotive industry
title_short Using machine learning prediction models for quality control a case study from the automotive industry
title_full Using machine learning prediction models for quality control a case study from the automotive industry
title_fullStr Using machine learning prediction models for quality control a case study from the automotive industry
title_full_unstemmed Using machine learning prediction models for quality control a case study from the automotive industry
title_sort using machine learning prediction models for quality control a case study from the automotive industry
publisher Springer
publishDate 2023
url https://link.springer.com/article/10.1007/s10287-023-00448-0
https://dlib.phenikaa-uni.edu.vn/handle/PNK/8431
_version_ 1765626990354759680
score 8.891787

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