| 引用本文: | 沈智军,张明德,谢乐.航空发动机叶片铣削工艺调整方法研究(J/M/D/N,J:杂志，M：书，D：论文，N：报纸).期刊名称,2021，38（5）：1- |
| CHEN X. Adap tive slidingmode contr ol for discrete2ti me multi2inputmulti2 out put systems[ J ]. Aut omatica, 2006, 42(6): 4272-435 |
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| 摘要: |
| 为提高航空发动机叶片精密铣削加工的型面质量，提出了一种基于材料去除量的铣削加工工艺参数调整方法；首先，进行叶片铣削工艺参数调整总体方案设计，制定了叶片的铣削加工总体方案以及工艺参数调整方案；其次，基于神经网络算法建立了叶片铣削材料去除量预估模型，用于评价在不同工艺参数组合实验的铣削稳定性；然后，基于材料去除量对工艺参数进行调整来提高铣削过程的稳定性，并选取最优工艺参数组合进行叶片铣削实验；最后，运用三坐标测量机对铣削后的叶片型面进行检测，实验结果表明：铣削后的叶片叶盆、叶背加工余量分布在±0.05 mm，满足工艺要求，验证了材料去除量模型的稳定性以及所提出的工艺参数调整方法的正确性。 |
| 关键词: 航空发动机叶片 表面质量 神经网络 材料去除量 |
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| Study on Process Adjustment Method of Aero-engine Blade Milling |
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SHEN Zhi-jun，ZHANG Ming-de，XIE Le1,2
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1.School of Mechanical Engineering, Chongqing University of Technology, Chongqing 400054, China;2.Chongqing Miao Qi Feng Technology Co.,Ltd., Chongqing 400054, China
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| Abstract: |
| In order to improve the profile quality of precision milling of aero-engine blades, a milling process parameter adjustment method based on material removal was proposed.First of all, this paper designs the blade milling process parameter adjustment overall scheme, the development of the blade milling processing overall scheme and process parameter adjustment scheme.Secondly, based on the neural network algorithm, a model for predicting the material removal amount of blade milling was established to evaluate the milling stability of different process parameters.Then, the process parameters were adjusted based on the material removal amount to improve the stability of the milling process, and the optimal combination of process parameters was selected for the blade milling experiment.Finally, CMM was used to detect the blade profile after milling. The experimental results showed that the machining allowance of the blade basin and blade back after milling was distributed within ±005 mm, which met the process requirements. The stability of the material removal model and the correctness of the proposed process parameter adjustment method were verified. |
| Key words: aero-engine blade surface quality neural network material removal amount |
