Description

• Title:
  Application of Basic Machine-Learning Classifiers for Automatic Anomaly Detection in Shewhart Control Charts
• Creator:
  Woźniak, Aleksander;
  Krawiec, Klaudia;
  Książek, Roger
• Description:
  In today’s dynamic technological environment, innovation plays a crucial role – especially for manufacturing enterprises that constantly strive to improve the quality of their products. This article examines the quality-management issue in a company producing car rims. It was identified that real-time quality control can sometimes be unreliable due to controller fatigue, leading to erroneous data interpretation or delayed responses to deviations in the production process. The study aimed to investigate the possibility of eliminating or significantly reducing these errors by employing a tool that is based on artificial intelligence. The article covers the preparation of training data, the training of classifiers, and the evaluationof their effectiveness in analyzing control charts in real time. The adopted hypothesis assumes that machine-learning classifiers can be effective methods of support for quality controllers. The research began with collecting measurement data from the machine and dividing it into training and test sets. The obtained results were evaluated using standard quality measures for machine-learning models. The results showed that the use of artificial intelligence can bring significant benefits in improving quality supervision in the production process of car rims.
• Object availability:
  free access
• Rights:
  Copyright (c) 2024 Decision Making in Manufacturing and Services
• Date:
  2024.12.12
• Type:
  info:eu-repo/semantics/article;
  info:eu-repo/semantics/publishedVersion
• Language:
  English
• Source:
  Decision Making in Manufacturing and Services; Vol. 18 (2024); 83-98;
  2300-7087;
  1896-8325
• Format:
  application/pdf
• Publisher:
  AGH University of Krakow Press
• Relation: https://journals.agh.edu.pl/dmms/article/view/6345/3093
• Subject:
  Machine Learning, Artificial Intelligence, AI, Statistical Process Control, SPC, Quality Control, Classifiers, Quality Metrics, Python Programming, Car Wheel, Quality Issues
• Identifier: https://journals.agh.edu.pl/dmms/article/view/6345 ;
  10.7494/dmms.2024.18.6345
• Data provider:
  AGH University of Science and Technology Journals
• Can I use it?:
  seek permission
• Type:
  text