Skip to Main content Skip to Navigation
Conference papers

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.
Document type :
Conference papers
Complete list of metadatas

Cited literature [31 references]  Display  Hide  Download

https://hal.inria.fr/hal-01666171
Contributor : Hal Ifip <>
Submitted on : Monday, December 18, 2017 - 10:38:37 AM
Last modification on : Thursday, August 22, 2019 - 12:04:03 PM

File

456370_1_En_48_Chapter.pdf
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Toyosi Ademujimi, Michael Brundage, Vittaldas Prabhu. A Review of Current Machine Learning Techniques Used in Manufacturing Diagnosis. IFIP International Conference on Advances in Production Management Systems (APMS), Sep 2017, Hamburg, Germany. pp.407-415, ⟨10.1007/978-3-319-66923-6_48⟩. ⟨hal-01666171⟩

Share

Metrics

Record views

295

Files downloads

131