TY - GEN
T1 - A Neural Network Approach to Tool Wear Detection via Infrared Sensor Monitoring
AU - Xia, Jiahua
AU - Song, Rui
AU - Teng, Lavianna
AU - Ma, Zepei
AU - P. P. Abdul Majeed, Anwar
AU - Chen, Yi
AU - Luo, Yang
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - The modern manufacturing sector encounters substantial financial setbacks due to unforeseen equipment downtime, hence mandating the implementation of more proactive maintenance strategies. The present study explores the potential of utilising AI-driven thermal imaging to monitor tool conditions in the context of predictive maintenance. In the context of the milling process, infrared camera technology was implemented to observe and assess tool wear and surface finishes. The analysis of key features derived from thermal imaging was conducted using two distinct methodologies: statistical analysis and polynomial feature extraction. Subsequently, a neural network was trained to categorise tools as either “fresh” or “worn”. This study provides a comparative examination of feature extraction techniques, focusing on the significant contributions of neural network and thermal imaging in enhancing predictive maintenance in the manufacturing sector. The study demonstrates that both statistical and polynomial feature extraction methods are effective for tool condition monitoring, with statistical features showing marginally higher success rates across various regions of interest, underscoring their reliability in predictive maintenance applications.
AB - The modern manufacturing sector encounters substantial financial setbacks due to unforeseen equipment downtime, hence mandating the implementation of more proactive maintenance strategies. The present study explores the potential of utilising AI-driven thermal imaging to monitor tool conditions in the context of predictive maintenance. In the context of the milling process, infrared camera technology was implemented to observe and assess tool wear and surface finishes. The analysis of key features derived from thermal imaging was conducted using two distinct methodologies: statistical analysis and polynomial feature extraction. Subsequently, a neural network was trained to categorise tools as either “fresh” or “worn”. This study provides a comparative examination of feature extraction techniques, focusing on the significant contributions of neural network and thermal imaging in enhancing predictive maintenance in the manufacturing sector. The study demonstrates that both statistical and polynomial feature extraction methods are effective for tool condition monitoring, with statistical features showing marginally higher success rates across various regions of interest, underscoring their reliability in predictive maintenance applications.
KW - artificial intelligence
KW - machine learning
KW - neural networks
KW - Thermal imaging
UR - http://www.scopus.com/inward/record.url?scp=85210887710&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-70684-4_11
DO - 10.1007/978-3-031-70684-4_11
M3 - Conference Proceeding
AN - SCOPUS:85210887710
SN - 9783031706837
T3 - Lecture Notes in Networks and Systems
SP - 125
EP - 133
BT - Robot Intelligence Technology and Applications 8 - Results from the 11th International Conference on Robot Intelligence Technology and Applications
A2 - Abdul Majeed, Anwar P.P.
A2 - Yap, Eng Hwa
A2 - Liu, Pengcheng
A2 - Huang, Xiaowei
A2 - Nguyen, Anh
A2 - Chen, Wei
A2 - Kim, Ue-Hwan
PB - Springer Science and Business Media Deutschland GmbH
T2 - 11th International Conference on Robot Intelligence Technology and Applications, RiTA 2023
Y2 - 6 December 2023 through 8 December 2023
ER -