TY - GEN
T1 - A Review of Current Machine Learning Techniques Used in Manufacturing Diagnosis
AU - Ademujimi, Toyosi Toriola
AU - Brundage, Michael P.
AU - Prabhu, Vittaldas V.
N1 - Publisher Copyright:
© IFIP International Federation for Information Processing 2017.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85029375359&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85029375359&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-66923-6_48
DO - 10.1007/978-3-319-66923-6_48
M3 - Conference contribution
AN - SCOPUS:85029375359
SN - 9783319669229
T3 - IFIP Advances in Information and Communication Technology
SP - 407
EP - 415
BT - Advances in Production Management Systems
A2 - Riedel, Ralph
A2 - Thoben, Klaus-Dieter
A2 - Kiritsis, Dimitris
A2 - von Cieminski, Gregor
A2 - Lodding, Hermann
PB - Springer New York LLC
T2 - IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2017
Y2 - 3 September 2017 through 7 September 2017
ER -