%0 Conference Proceedings
%T A Review of Current Machine Learning Techniques Used in Manufacturing Diagnosis
%+ Pennsylvania State University (Penn State)
%+ National Institute of Standards and Technology [Gaithersburg] (NIST)
%A Ademujimi, Toyosi, Toriola
%A Brundage, Michael, P.
%A Prabhu, Vittaldas, V.
%Z Part 6: Intelligent Diagnostics and Maintenance Solutions
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B IFIP International Conference on Advances in Production Management Systems (APMS)
%C Hamburg, Germany
%Y Hermann Lödding
%Y Ralph Riedel
%Y Klaus-Dieter Thoben
%Y Gregor von Cieminski
%Y Dimitris Kiritsis
%I Springer International Publishing
%3 Advances in Production Management Systems. The Path to Intelligent, Collaborative and Sustainable Manufacturing
%V AICT-513
%N Part I
%P 407-415
%8 2017-09-03
%D 2017
%R 10.1007/978-3-319-66923-6_48
%K Artificial intelligence
%K Machine learning
%K Manufacturing diagnosis
%K Fault Detection
%K Intelligent maintenance
%K Industrie 4.0
%Z Computer Science [cs]Conference papers
%X 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.
%G English
%Z TC 5
%Z WG 5.7
%2 https://inria.hal.science/hal-01666171/document
%2 https://inria.hal.science/hal-01666171/file/456370_1_En_48_Chapter.pdf
%L hal-01666171
%U https://inria.hal.science/hal-01666171
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-TC5
%~ IFIP-WG
%~ IFIP-APMS
%~ IFIP-WG5-7
%~ IFIP-AICT-513