| LIU Dewei,XU Zhiling,LI Changhe,QIN Aiguo,LIU Bo,ZHANG Yanbin,YUSUF Suleiman，Dambatta,AN Qinglong.#$NPMathematical Model and Experimental Verification of Workpiece Surface Roughness in Face Milling[J],53(4):125-139 |
| #$NPMathematical Model and Experimental Verification of Workpiece Surface Roughness in Face Milling |
| Received:November 03, 2023 Revised:December 06, 2023 |
| View Full Text View/Add Comment Download reader |
| DOI：10.16490/j.cnki.issn.1001-3660.2024.04.012 |
| KeyWord:milling formation mechanism of profiles surface roughness milling force tool runout convolutional neural network |
| Author | Institution |
| LIU Dewei |
School of Mechanical and Automotive Engineering, Qingdao University of Technology, Shandong Qingdao , China |
| XU Zhiling |
Qingdao Haikong Pressure Vessel Sales Co., Ltd., Shandong Qingdao , China |
| LI Changhe |
School of Mechanical and Automotive Engineering, Qingdao University of Technology, Shandong Qingdao , China |
| QIN Aiguo |
Qingdao Kaws Intelligent Manufacturing Co., Ltd., Shandong Qingdao , China |
| LIU Bo |
Sichuan New Aviation Ta Technology Co., Ltd., Sichuan Shifang , China |
| ZHANG Yanbin |
State Key Laboratory of Ultra-precision Machining Technology, Hong Kong Polytechnic University, Hong Kong , China |
| YUSUF Suleiman，Dambatta |
School of Mechanical and Automotive Engineering, Qingdao University of Technology, Shandong Qingdao , China;Mechanical Engineering Department, Ahmadu Bello University, Kaduna , Nigeria |
| AN Qinglong |
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai , China |
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| Abstract: |
| Surface roughness has a significant impact on the wear resistance, corrosion resistance, and reliability of components. Accurate prediction of surface roughness can effectively control the manufacturing process and optimize processing parameters. However, the coupling effects of various influencing factors on surface roughness obscure the formation mechanism of profiles, leading to technical challenges in the insufficient predictive accuracy of mathematical models for surface roughness intelligent control in industrial applications. This study established a mathematical model for surface roughness in face milling to address this issue. Firstly, the surface profile forming mechanism of face milling workpiece was analyzed and the surface profile model along the tool feed direction was established based on tool geometry and machining kinematics, taking into consideration the tool runout boundary conditions. The mapping between dynamic factors (tool wear, tool vibration, elastic recovery) and surface roughness was established through the milling force. The compensation function of profile height deviation about milling force was established and resolved by a convolutional neural network (CNN) which contained 5 convolutional layers and 3 fully connected layers. Next, the mathematical model for surface roughness in face milling was developed, with Ra serving as a characterization parameter for surface roughness. Finally, the face milling ZG32MnMo experiment was carried out to collect the profile data and milling force signals respectively, which used indexable face milling cutters. The milling data set was established with cutting force as input and profile height deviation data as output. The CNN was trained and the profile height compensation values was analyzed. CNN training results showed RMSE of 0.81 μm and 0.84 μm for the training and test sets, respectively. Through CNN, compensation values for profile height were analyzed to enhance the prediction accuracy of the mathematical model for surface roughness. The surface roughness mathematical model accuracy was validated. Surface roughness mathematical model considering tool runout and surface roughness mathematical model considering tool runout optimized by CNN were compared in terms of accuracy. The results showed that the surface roughness mathematical model considering tool runout optimized by CNN and the surface roughness mathematical model considering tool runout predicted Rsm and Rmr(c) close to the experimental values in the overlapping and non-overlapping along the feed direction, and the surface roughness mathematical model considering tool runout optimized by CNN exhibited higher predictive accuracy for Rmr(c) compared with the surface roughness mathematical model considering tool runout. The surface roughness mathematical model considering tool runout optimized by CNN predicted errors for Ra in the overlapping and non-overlapping along the tool feed direction to be 18.71% and 14.14%, respectively, while the surface roughness mathematical model considering tool runout predicted errors of 29.32% and 46.97% in the same regions. The surface roughness mathematical model considering tool runout optimized by CNN improved the accuracy by 10.61% and 32.83%, respectively, compared with the surface roughness mathematical model considering tool runout. The surface roughness mathematical model established by considering the boundary conditions of tool runout and dynamic milling force coupling can effectively characterize the surface roughness of face milling and provide reference for its application in quality control engineering. |
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