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Explicit correlation model of multi-source constraints for Re-design parts with complex curved surface

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Abstract

In precision machining of complex curved surface parts with high performance, geometry accuracy is not the only constraint, but the performance should also be met. Performance of this kind of parts is closely related to the geometrical and physical parameters, so the final actual size and shape are affected by multiple source constraints, such as geometry, physics, and performance. These parts are rather difficult to be manufactured and new manufacturing method according to performance requirement is urgently needed. Based on performance and manufacturing requirements for complex curved surface parts, a new classification method is proposed, which divided the complex curved surface parts into two categories: surface re-design complex curved surface parts with multi-source constraints(PRCS) and surface unique complex curved surface parts with pure geometric constraints(PUCS). A correlation model is constructed between the performance and multi-source constraints for PRCS, which reveals the correlation between the performance and multi-source constraints. A re-design method is also developed. Through solving the correlation model of the typical part’s performance-associated surface, the mapping relation between the performance-associated surface and the related removal amount is obtained. The explicit correlation model and the method for the corresponding related removal amount of the performance-associated surface are built based on the classification of surface re-design complex curved surface parts with multi-source constraints. Research results have been used in the actual processing of the typical parts such as radome, common bottom components, nozzle, et al., which shows improved efficiency and accuracy of the precision machining for the surface re-design parts with complex curved surface.

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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, 116023, China

    Zhenyuan Jia, Fuji Wang, Yongqing Wang & Dongming Guo

Authors
  1. Zhenyuan Jia
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  2. Fuji Wang
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  3. Yongqing Wang
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  4. Dongming Guo
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Corresponding author

Correspondence to Fuji Wang.

Additional information

This project is supported by Key Program of National Natural Science Foundation of China(Grant No. 50835001), and Program for New Century Excellent Talents in University, China(Grant No. NCET-13-0081)

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

WANG Fuji, born in 1974, is currently an associate professor at Dalian University of Technology, China. He received his PhD degree from Dalian University of Technology, China, in 2005. His research interests include precision machining, precision measurement, function material component and artificial intelligence.

WANG Yongqing, born in 1969, a professor at Dalian University of Technology, China. He received his PhD degree from Dalian University of Technology, China, in 2002. His research interests include numerical control technology, digital manufacturing, and precision measurement.

GUO Dongming, born in 1959, is currently an academician of China Engineering Academy, the executive vice president of Dalian University of Technology, China. His main research interests include special machining, precision/ultra-precision machining technology.

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Jia, Z., Wang, F., Wang, Y. et al. Explicit correlation model of multi-source constraints for Re-design parts with complex curved surface. Chin. J. Mech. Eng. 27, 385–391 (2014). https://doi.org/10.3901/CJME.2014.02.385

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  • DOI: https://doi.org/10.3901/CJME.2014.02.385

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