IJAT Vol.18 No.3 pp. 342-351
doi: 10.20965/ijat.2024.p0342

Research Paper:

Practical Method for Identifying Model Parameters for Machining Error Simulation in End Milling Through Sensor-Less Monitoring and On-Machine Measurement

Kazuki Kaneko, Arisa Kudo, Takanori Waizumi, Jun Shimizu, Libo Zhou, Hirotaka Ojima, and Teppei Onuki

Ibaraki University
4-12-1 Nakanarusawa-cho, Hitachi, Ibaraki 316-8511, Japan

Corresponding author

September 28, 2023
February 28, 2024
May 5, 2024
end milling, machining error simulation, parameter identification, tool stiffness, on-machine measurement

Depending on cutting conditions, unacceptable machining errors are caused by tool deflection in end milling operations. Many studies have proposed methods for predicting the machining error owing to the tool deflection to achieve the theoretical optimization of the cutting conditions. However, the conventional machining error simulation is not practically utilized to determine the optimal cutting conditions. Tool system stiffness parameters and cutting coefficients must be identified in advance to simulate machining errors. However, dynamometers and displacement sensors are required for parameter identification. Therefore, it is impossible to identify the required parameters in typical factories, which do not possess such special equipment. In this study, a practical method was developed to identify the stiffness parameters that can be determined in factories. The proposed method employs on-machine measurement and sensor-less cutting force monitoring to achieve practical parameter identification. In the proposed method, the profile milling is first conducted. During the milling operation, the cutting force and cutting torque are monitored through a controller based on the sensor-less monitoring technique. After the operation, the machining error distribution on the machined surface is measured on machine using a touch probe. The required parameters are identified by minimizing the differences between the measured and theoretical forces, torques, and machining error distributions.

Cite this article as:
K. Kaneko, A. Kudo, T. Waizumi, J. Shimizu, L. Zhou, H. Ojima, and T. Onuki, “Practical Method for Identifying Model Parameters for Machining Error Simulation in End Milling Through Sensor-Less Monitoring and On-Machine Measurement,” Int. J. Automation Technol., Vol.18 No.3, pp. 342-351, 2024.
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Last updated on May. 19, 2024