Szczegóły publikacji

Opis bibliograficzny

Application of basic machine-learning classifiers for automatic anomaly detection in Shewhart control charts / Aleksander Woźniak, Klaudia Krawiec, Roger KSIĄŻEK // Decision Making in Manufacturing and Services ; ISSN 1896-8325. — 2024 — vol. 18, s. 83–98. — Bibliogr. s. 95-98, Abstr. — Publikacja dostępna online od: 2024-12-12

Autorzy (3)

Słowa kluczowe

car wheelAImachine learningquality controlartificial intelligencequality metricsclassifiersSPCstatistical process controlPython programmingquality issues

Dane bibliometryczne

ID BaDAP158708
Data dodania do BaDAP2025-04-09
Tekst źródłowyURL
DOI10.7494/dmms.2024.18.6345
Rok publikacji2024
Typ publikacjiartykuł w czasopiśmie
Otwarty dostęptak
Creative Commons
Czasopismo/seriaDecision Making in Manufacturing and Services

Abstract

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 evaluation of 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.

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