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Pre-compensation for continuous-path running trajectory error in high-speed machining of parts with varied curvature features

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Abstract

Parts with varied curvature features play increasingly critical roles in engineering, and are often machined under high-speed continuous-path running mode to ensure the machining efficiency. However, the continuous-path running trajectory error is significant during high-feed-speed machining, which seriously restricts the machining precision for such parts with varied curvature features. In order to reduce the continuous-path running trajectory error without sacrificing the machining efficiency, a pre-compensation method for the trajectory error is proposed. Based on the formation mechanism of the continuous-path running trajectory error analyzed, this error is estimated in advance by approximating the desired toolpath with spline curves. Then, an iterative error pre-compensation method is presented. By machining with the regenerated toolpath after pre-compensation instead of the uncompensated toolpath, the continuous-path running trajectory error can be effectively decreased without the reduction of the feed speed. To demonstrate the feasibility of the proposed pre-compensation method, a heart curve toolpath that possesses varied curvature features is employed. Experimental results indicate that compared with the uncompensated processing trajectory, the maximum and average machining errors for the pre-compensated processing trajectory are reduced by 67.19% and 82.30%, respectively. An easy to implement solution for high efficiency and high precision machining of the parts with varied curvature features is provided.

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Authors and Affiliations

  1. Key Laboratory for Precision and Non-traditional Machining Technology of Ministry of Education, Dalian University of Technology, Dalian, 116024, China

    Zhenyuan Jia, Dening Song, Jianwei Ma & Yuanyuan Gao

Authors
  1. Zhenyuan Jia
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  2. Dening Song
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  3. Jianwei Ma
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  4. Yuanyuan Gao
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Corresponding author

Correspondence to Jianwei Ma.

Additional information

Supported by National Natural Science Foundation of China(Grant Nos. 51575087, 51205041), Science Fund for Creative Research Groups(Grant No. 51321004), Basic Research Foundation of Key Laboratory of Liaoning Educational Committee, China(Grant No. LZ2014003), and Research Project of Ministry of Education of China(Grant No. 113018A)

JIA Zhenyuan, born in 1963, is currently a professor at Institute of Advanced Manufacturing Technology, Dalian University of Technology, China, the director at Key Laboratory for Precision and Non-traditional Machining Technology of Ministry of Education, China, and the vice-president of Dalian University of Technology, China. He received his PhD degree from School of Mechanical Engineering, Dalian University of Technology, China, in 1990. His research interests include precision machining, precision measurement, function material component and process detection.

SONG Dening, born in 1991, is currently a PhD candidate at School of Mechanical Engineering, Dalian University of Technology, China. His research interests include precision machining and numerical control technology.

MA Jianwei, born in 1984, is currently an associate professor at Key Laboratory for Precision and Non-traditional Machining Technology of Ministry of Education, School of Mechanical Engineering, Dalian University of technology, China. He received his PhD degree from School of Mechanical Engineering, Dalian University of technology, China, in 2011. His research interests focus on precision machining, machining process control, process detection and artificial intelligence.

GAO Yuanyuan, born in 1990, is currently a master candidate at School of Mechanical Engineering, Dalian University of Technology, China.

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Jia, Z., Song, D., Ma, J. et al. Pre-compensation for continuous-path running trajectory error in high-speed machining of parts with varied curvature features. Chin. J. Mech. Eng. 30, 37–45 (2017). https://doi.org/10.3901/CJME.2016.0127.015

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  • DOI: https://doi.org/10.3901/CJME.2016.0127.015

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