Journal of the Korean Society for Precision Engineering

Techniques for Tool Life Prediction and Autonomous Tool Change Using Real-time Process Monitoring Data: Seong Hun Ha, Min-Suk Park, Hoon-Hee Lee; J. Korean Soc. Precis. Eng. 2025;42(11):949-958.
Published online November 1, 2025; DOI: https://doi.org/10.7736/JKSPE.025.077

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Materials such as titanium alloys, nickel alloys, and stainless steels are difficult to machine due to low thermal conductivity, work hardening, and built-up edge formation, which accelerate tool wear. Frequent tool changes are required, often relying on operator experience, leading to inefficient tool use. While modern machine tools include intelligent tool replacement systems, many legacy machines remain in service, creating a need for practical alternatives. This study proposes a method to autonomously determine tool replacement timing by monitoring machining process signals in real time, enabling automatic tool changes even on conventional machines. Tool wear is evaluated using current and vibration sensors, with the replacement threshold estimated from the maximum current observed in an initial user-defined interval. When real-time signals exceed this threshold, the system updates controller variables to trigger tool changes. Results show vibration data are more sensitive to wear, whereas current data provide greater stability. These findings indicate that a hybrid strategy combining both sensors can enhance accuracy and reliability of tool change decisions, improving machining efficiency for difficult-to-cut materials.

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Development of a Regression-Based Tool Life Prediction Model in Manufacturing Environments: Hyun Chul Kim; J. Korean Soc. Precis. Eng. 2025;42(3):247-252.
Published online March 1, 2025; DOI: https://doi.org/10.7736/JKSPE.024.131

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This study aimed to develop a regression-based model for predicting tool life in manufacturing environments, with goals of enhancing productivity and reducing costs. In machining operations, particularly roughing processes, high cutting forces can accelerate tool wear, often leading to process interruptions and increased defect rates. Previous research on tool life prediction has frequently relied on empirical models and statistical methods, which face limitations in reliability across diverse machining conditions. To address this issue, we proposed a data-driven approach that could collects tool wear data under varying machining conditions (such as cutting speed, feed rate, and depth of cut) and applied regression models to predict tool life effectively. The model’s performance was validated under multiple conditions to assess its predictive accuracy. This study offers a practical tool life management solution for manufacturing settings, optimizing tool usage and enhancing operational efficiency.

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Tool Wear Monitoring System based on Real-Time Cutting Coefficient Identification: Young Jae Choi, Ki Hyeong Song, Jae Hyeok Kim, and Gu Seon; J. Korean Soc. Precis. Eng. 2022;39(12):891-898.
Published online December 1, 2022; DOI: https://doi.org/10.7736/JKSPE.022.111

Abstract
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Among the monitoring technologies in the metal-cutting process, tool wear is the most critical monitoring factor in real machining sites. Extensive studies have been conducted to monitor equipment breakdown in real-time. For example, tool wear prediction studies using cutting force signals and deducting force coefficient values from the cutting process. However, due to many limitations, those wearable monitoring technologies have not been directly adopted in the field. This paper proposes a novel tool wear predictor using the cutting force coefficient with various cutting tools, and its validity evaluates through cutting tests. Tool wear prediction from the cutting force coefficient should conduct in real-time for adoption in real machining sites. Therefore, a real-time calculation algorithm of the cutting force coefficient and a tool wear estimation method proposes, and they compare with actual tool wear in cutting experiments for validation. Validation cutting tests are conducted with carbon steel and titanium, the most commonly used materials in real cutting sites. In future work, validation will be conducted with different materials and cutting tools, considering the application in real machining sites.

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A Review of Intelligent Machining Process in CNC Machine Tool Systems
Joo Sung Yoon, Il-ha Park, Dong Yoon Lee
International Journal of Precision Engineering and Manufacturing.2025; 26(9): 2243. CrossRef

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Comparative Analysis and Monitoring of Tool Wear in Carbon Fiber Reinforced Plastics Drilling: Kyeong Bin Kim, Jang Hoon Seo, Tae-Gon Kim, Martin Byung-Guk Jun, Young Hun Jeong; J. Korean Soc. Precis. Eng. 2020;37(11):813-818.
Published online November 1, 2020; DOI: https://doi.org/10.7736/JKSPE.020.091

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Recently, carbon fiber-reinforced plastic (CFRP) has been attracting much attention in various industries because of its beneficial properties such as excellent strength, modulus per unit density, and anti-corrosion properties. However, there are several issues in its application to various fields. Severe tool wear issues in its machining have been noted as one of the most serious problems because it induces various serious machining failures such as delamination and splintering. In this regard, timely tool replacement is essential for reducing the influence of tool wear. In this study, tool wear, especially flank wear, in the CFRP drilling was investigated and monitored. First, the reproducibility of tool wear under the same machining condition was experimentally evaluated. And it is demonstrated that tool wear may remarkably differ even though the same machining condition is applied to the tools. Then, tool wear monitoring based on the feed motor torque was applied to the detection of tool life ending in the CFRP drilling process. Consequently, it was demonstrated that the average and maximum detection error of the tool life end were less than 7 and 14%, respectively.

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Experimental research on multi-structural parameter optimization of rhombic tooth endmill based on DOE in CFRP milling
Xiaochen Zuo, Junxue Ren, Tiejun Song, Tao Zeng, Mengliu Zhang, Hexuan Liu
Journal of Materials Research and Technology.2025; 38: 2892. CrossRef
Laser Drilling of Micro-Hole Array on CFRP Using Nanosecond Pulsed Fiber Laser
Do Kwan Chung
Journal of the Korean Society of Manufacturing Process Engineers.2024; 23(5): 92. CrossRef
Laser EDM Hybrid Micro Machining of CFRP
Do Kwan Chung, Chan Ho Han, Yu Jin Choi, Jun Seo Park
Journal of the Korean Society for Precision Engineering.2023; 40(2): 99. CrossRef
Comparison of TiAlN DLC and PCD Tool Wear in CFRP Drilling
Jong-Hyun Baek, Su-Jin Kim
Journal of the Korean Society of Manufacturing Process Engineers.2022; 21(5): 77. CrossRef

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Mechanical Cutting Process Trends for Difficult-to-Cut Materials - A Review -: Myeong Gu Gang, Gyuho Kim, Kangwoo Shin, Anmok Jeong, Hyo-Young Kim, Cheol-Ho Kim, Seok-Woo Lee, Tae-Gon Kim; J. Korean Soc. Precis. Eng. 2018;35(3):253-267.
Published online March 1, 2018; DOI: https://doi.org/10.7736/KSPE.2018.35.3.253

Abstract
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Lightweight parts are necessary to improve fuel efficiency and reduce environmental impacts in transportation industry. As a result, there has been a shift away from using conventional metals toward using lighter materials with superior mechanical strength. These new materials typically include titanium alloys, nickel alloys, carbon fiber reinforced plastics (CFRPs), and CFRP-metal stacks, which are classified as advanced materials. However, due to the unique properties of these materials (e.g., high strength, low thermal conductivity, carbon fiber-induced hardness, etc.), the cutting process can be difficult. As a result, various manufacturing issues can occur during the cutting process, such as high tool wear, surface quality deterioration, delamination of the CFRP layer, fiber pull-out, and thermal deformation. In this paper, difficult-to-cut advanced materials were reviewed with regard to the influence of the physical properties of the materials and various defect issues that can occur during the mechanical cutting process. In addition, various approaches to improve the cutting process are introduced, including protecting tools with coatings, altering tool features, using high pressure or cryogenic cooling, extending tool life via ultrasonic vibration machining, and improving product quality and machinability.

Citations

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Laser Drilling of Micro-Hole Array on CFRP Using Nanosecond Pulsed Fiber Laser
Do Kwan Chung
Journal of the Korean Society of Manufacturing Process Engineers.2024; 23(5): 92. CrossRef
Abrasive belt grinding force and its influence on surface integrity
Yun Huang, Gang Liu, Guijian Xiao, Jiayu Xu
Materials and Manufacturing Processes.2023; 38(7): 888. CrossRef
Laser EDM Hybrid Micro Machining of CFRP
Do Kwan Chung, Chan Ho Han, Yu Jin Choi, Jun Seo Park
Journal of the Korean Society for Precision Engineering.2023; 40(2): 99. CrossRef
Ultrasonic Unit Design for Drilling
An Mok Jeong
Journal of the Korean Society of Manufacturing Technology Engineers.2022; 31(6): 409. CrossRef
A study on the process efficiency of laser-assisted machining investigating energy consumption
Won-Jung Oh, Choon-Man Lee
The International Journal of Advanced Manufacturing Technology.2021; 113(3-4): 867. CrossRef
Development of adhesion force evaluation equipment for nano diamond coated tool using shear method
Jinghua Li, SoJin Lee, HyunKyu Kweon
Measurement and Control.2021; 54(1-2): 3. CrossRef
Cutting Characteristics and Deformation Analysis for Chord and Side Fitting Parts in an Aircraft Bulkhead
Do Hyeog Kim, Yoon Gyo Jung, Yong-Seon Mo, Young Tae Cho
Journal of the Korean Society of Manufacturing Technology Engineers.2020; 29(1): 74. CrossRef
Micro Machining of CFRP Using Nanosecond Pulsed Fiber Laser
Do Kwan Chung, Jin Sung Park, Ki Hun Kim
Journal of the Korean Society for Precision Engineering.2019; 36(9): 783. CrossRef

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Vision Based On-Machine Measurement of Flank Wear in Drill Tool for Smart Machine Tool: Tae-Gon Kim, Kangwoo Shin, Seok-Woo Lee; J. Korean Soc. Precis. Eng. 2018;35(2):145-149.
Published online February 1, 2018; DOI: https://doi.org/10.7736/KSPE.2018.35.2.145

Abstract
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Tool wear is an essential parameter in determining tool life, machining quality and productivity. Current or power signals from motor drivers in machine have been used to estimate tool wear. However, accuracy of tool wear estimation was not enough to measure the amount of tool wear. In this study, flank wear of a drill tool was measured using vision sensor module which has zoom lens, CCD camera and image processing technique. The vision module was set up in the machine tool. Therefore, the image was acquired without ejecting the tool from the machine. Image processing techniques were used to define the cutting edge shape, tool diameter, and the wear edge on cutting rips with the proposed measuring algorithm. The automatically calculated wear value was compared with a manually measured value. As a result, the difference between the manual and the automatic methods was below 4.7%. The proposed method has an advantage to decrease the measuring time and improve measuring repeatability because the tool is measured holding chuck in a spindle.