Monitoring technology of machining operations has a long history since unmanned machining was introduced. Lots of research papers were presented and some of them has been commercialized and applied to shop floor. Despite the long history, many researchers have presented new approaches continuously in this area. This paper presents current state of monitoring technology of machining operations. The objectives of monitoring are shortly summarized, and the monitoring methods and the unique sensor technologies are reviewed. The main objective of the monitoring technology remains same; tool condition monitoring (TCM). The general approaches also remain similar; signal processing and decision making. But, the innovative methods for every step of process monitoring are being provided to improve the performance. More powerful computing is lowering the wall of much more data from more sensors by fast calculation. This technology also introduces the novel decision making strategies such as Artificial Intelligent. New materials and new communication technologies are breaking the limitation of sensor positions. Virtual machining technology which estimates the machining physics is being integrated with monitoring technology.
Citations
Citations to this article as recorded by
Reducing the Loss Cost by Setting the Optimal Replacement Cycle for Cutting Tools using FOM-Tool Monitoring Jae Hoon Jang, Seon Jun Jang, Su Young Kim Journal of the Korean Society of Manufacturing Technology Engineers.2023; 32(3): 169. CrossRef
Tool Wear Monitoring System based on Real-Time Cutting Coefficient Identification Young Jae Choi, Ki Hyeong Song, Jae Hyeok Kim, Gu Seon Kang Journal of the Korean Society for Precision Engineering.2022; 39(12): 891. CrossRef
Prediction of Drill Bit Breakage Using an Infrared Sensor Min-Jae Jeong, Sang-Woo Lee, Woong-Ki Jang, Hyung-Jin Kim, Young-Ho Seo, Byeong-Hee Kim Sensors.2021; 21(8): 2808. CrossRef
Recent Developments and Challenges on Machining of Carbon Fiber Reinforced Polymer Composite Laminates Jaewoo Seo, Do Young Kim, Dong Chan Kim, Hyung Wook Park International Journal of Precision Engineering and Manufacturing.2021; 22(12): 2027. CrossRef
Cutting Force Estimation Using Feed Motor Drive Current in Cutting Process Monitoring Ki Hyeong Song, Dong Yoon Lee, Kyung Hee Park, Jae Hyeok Kim, Young Jae Choi Journal of the Korean Society for Precision Engineering.2020; 37(11): 803. CrossRef
Evaluation of the Grinding Performance of an Engine Block Honing Stone through Monitoring of Workload and Heat Generation Jang-Woo Yun, Sang-Beom Kim Journal of the Korean Society of Manufacturing Process Engineers.2019; 18(4): 69. CrossRef
Implementation of Wireless Condition Monitoring System in a Cutting Tool Using Accelerometer Yong Tae Kim, Yoo Su Kang, Hyung Jin Kim, Young Ho Seo, Byeong Hee Kim Journal of the Korean Society of Manufacturing Technology Engineers.2019; 28(3): 198. CrossRef
FEM (Finite Element Method)-based numerical analysis model, which is known as CAE (Computer Aided Engineering) technology, has been adopted for the visual/mechanical analysis of machining process. The essential models for the FEM analytical model are the plasticity model of workpieces, friction model, and wear rate model. Usually, the outputs of the FEM analytical model are the cutting force, the cutting temperature, and chip formation. Based on these outputs, the machining performance can be virtually evaluated without experiments. Nowadays, there are emerging machining technologies, such as cryogenic assisted machining and CFRP machining. Therefore, FEM technique can be one of the good candidate to virtually evaluate emerging developed machining technologies.
Citations
Citations to this article as recorded by
Post-machining Deformation Analysis for Virtual Machining of Thin Aluminium Alloy Parts Soo-Hyun Park, Eunseok Nam, Myeong Gu Gang, Byung-Kwon Min International Journal of Precision Engineering and Manufacturing.2019; 20(4): 687. CrossRef
First
Prev
