A Review of Current Machine Learning Techniques Used in Manufacturing Diagnosis

Abstract : Artificial intelligence applications are increasing due to advances in data collection systems, algorithms, and affordability of computing power. Within the manufacturing industry, machine learning algorithms are often used for improving manufacturing system fault diagnosis. This study focuses on a review of recent fault diagnosis applications in manufacturing that are based on several prominent machine learning algorithms. Papers published from 2007 to 2017 were reviewed and keywords were used to identify 20 articles spanning the most prominent machine learning algorithms. Most articles reviewed consisted of training data obtained from sensors attached to the equipment. The training of the machine learning algorithm consisted of designed experiments to simulate different faulty and normal processing conditions. The areas of application varied from wear of cutting tool in computer numeric control (CNC) machine, surface roughness fault, to wafer etching process in semiconductor manufacturing. In all cases, high fault classification rates were obtained. As the interest in smart manufacturing increases, this review serves to address one of the cornerstones of emerging production systems.
Type de document :
Communication dans un congrès
Hermann Lödding; Ralph Riedel; Klaus-Dieter Thoben; Gregor von Cieminski; Dimitris Kiritsis. IFIP International Conference on Advances in Production Management Systems (APMS), Sep 2017, Hamburg, Germany. Springer International Publishing, IFIP Advances in Information and Communication Technology, AICT-513 (Part I), pp.407-415, 2017, Advances in Production Management Systems. The Path to Intelligent, Collaborative and Sustainable Manufacturing 〈10.1007/978-3-319-66923-6_48〉
Liste complète des métadonnées

Littérature citée [32 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-01666171
Contributeur : Hal Ifip <>
Soumis le : lundi 18 décembre 2017 - 10:38:37
Dernière modification le : lundi 18 décembre 2017 - 11:02:25

Fichier

 Accès restreint
Fichier visible le : 2020-01-01

Connectez-vous pour demander l'accès au fichier

Licence


Distributed under a Creative Commons Paternité 4.0 International License

Identifiants

Citation

Toyosi Ademujimi, Michael Brundage, Vittaldas Prabhu. A Review of Current Machine Learning Techniques Used in Manufacturing Diagnosis. Hermann Lödding; Ralph Riedel; Klaus-Dieter Thoben; Gregor von Cieminski; Dimitris Kiritsis. IFIP International Conference on Advances in Production Management Systems (APMS), Sep 2017, Hamburg, Germany. Springer International Publishing, IFIP Advances in Information and Communication Technology, AICT-513 (Part I), pp.407-415, 2017, Advances in Production Management Systems. The Path to Intelligent, Collaborative and Sustainable Manufacturing 〈10.1007/978-3-319-66923-6_48〉. 〈hal-01666171〉

Partager

Métriques

Consultations de la notice

65