TY - JOUR
T1 - Development of an intelligent tool condition monitoring system to identify manufacturing tradeoffs and optimal machining conditions
AU - Lee, Wo Jae
AU - Mendis, Gamini P.
AU - Sutherland, John W.
N1 - Funding Information:
Authors Lee and Mendis are supported by the Indiana Next Generation Manufacturing Competitiveness Center (IN-MaC). The authors would like to acknowledge the NASA repository for publishing and hosting the milling data.
Publisher Copyright:
© 2019 The Authors. Published by Elsevier B.V.
PY - 2019
Y1 - 2019
N2 - Smart manufacturing has leveraged the evolution of a sensor-based and data-driven platform to improve manufacturing outcomes. As a result of increased use of sensors and networked machines in manufacturing operations, artificial intelligence techniques play a key role to derive meaningful value from big data infrastructure. These techniques can inform decision making and can enable the implementation of more sustainable practices in the manufacturing industry. In machining processes, a considerable amount of waste (scrap) is generated as a result of failure to monitor a tool condition. Therefore, an intelligent tool condition monitoring system is developed in this paper to identify sustainability-related manufacturing tradeoffs and a set of optimal machining conditions by monitoring the status of the machine tool. An evolutionary algorithm-based multi-objective optimization is used to find the optimal operating conditions, and the solutions are visualized using a Pareto optimal front.
AB - Smart manufacturing has leveraged the evolution of a sensor-based and data-driven platform to improve manufacturing outcomes. As a result of increased use of sensors and networked machines in manufacturing operations, artificial intelligence techniques play a key role to derive meaningful value from big data infrastructure. These techniques can inform decision making and can enable the implementation of more sustainable practices in the manufacturing industry. In machining processes, a considerable amount of waste (scrap) is generated as a result of failure to monitor a tool condition. Therefore, an intelligent tool condition monitoring system is developed in this paper to identify sustainability-related manufacturing tradeoffs and a set of optimal machining conditions by monitoring the status of the machine tool. An evolutionary algorithm-based multi-objective optimization is used to find the optimal operating conditions, and the solutions are visualized using a Pareto optimal front.
UR - http://www.scopus.com/inward/record.url?scp=85068584165&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85068584165&partnerID=8YFLogxK
U2 - 10.1016/j.promfg.2019.04.031
DO - 10.1016/j.promfg.2019.04.031
M3 - Conference article
AN - SCOPUS:85068584165
SN - 2351-9789
VL - 33
SP - 256
EP - 263
JO - Procedia Manufacturing
JF - Procedia Manufacturing
T2 - 16th Global Conference on Sustainable Manufacturing, GCSM 2018
Y2 - 2 October 2018 through 4 October 2018
ER -