中国科学院长春光学精密机械与物理研究所

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	High-efficiency abrasive water jet milling of aspheric RB-SiC surface based on BP neural network depth control models
	H. X. Deng, P. Yao, K. Hai, S. M. Yu, C. Z. Huang, H. T. Zhu and D. Liu
	2023
发表期刊	International Journal of Advanced Manufacturing Technology
ISSN	0268-3768
页码	16
摘要	For large processing allowance of large diameter RB-SiC mirror blanks and low efficiency of grinding, an abrasive water jet milling is used to quickly remove the processing allowance. In this article, a single kerf profile processed by abrasive water jet milling was effectively fitted to the Gaussian curve. By superimposing Gaussian curves linearly, the surface waviness of superimposed curve was gradually reduced as step-over distance decreased. The surface waviness induced by abrasive water jet milling can be effectively reduced when step-over distance is controlled to less than 1.8 sigma. BP neural network models between step-over distance, traverse speed, and milling depth were established. The prediction error of milling depth can be controlled at about 5% of the total depth, with a maximum error less than 7%. The aspherical RB-SiC surface was generated by abrasive water jet milling with a processing path composed of 20 spiral segments. Different milling depths were obtained by setting different levels of traverse speed and step-over distance for each spiral segment. The processed aspherical surface was highly fitted to the design aspherical surface with a maximum error about 10% of the total depth. The error curves float at the zero line, and the error curves were controlled at 20% of the total depth. By this method, the processing allowance of large diameter RB-SiC mirror blanks can be effectively reduced.
DOI	10.1007/s00170-023-11275-7
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收录类别	sci
语种	英语
引用统计
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/67428
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	H. X. Deng, P. Yao, K. Hai, S. M. Yu, C. Z. Huang, H. T. Zhu and D. Liu. High-efficiency abrasive water jet milling of aspheric RB-SiC surface based on BP neural network depth control models[J]. International Journal of Advanced Manufacturing Technology,2023:16.
APA	H. X. Deng, P. Yao, K. Hai, S. M. Yu, C. Z. Huang, H. T. Zhu and D. Liu.(2023).High-efficiency abrasive water jet milling of aspheric RB-SiC surface based on BP neural network depth control models.International Journal of Advanced Manufacturing Technology,16.
MLA	H. X. Deng, P. Yao, K. Hai, S. M. Yu, C. Z. Huang, H. T. Zhu and D. Liu."High-efficiency abrasive water jet milling of aspheric RB-SiC surface based on BP neural network depth control models".International Journal of Advanced Manufacturing Technology (2023):16.

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