The prediction of surface roughness of PCBN turning GH4169 based on adaptive genetic algorithm
The prediction of surface roughness of PCBN turning GH4169 based on adaptive genetic algorithm
复制标题
基于自适应遗传算法的PCBN车削GH4169表面粗糙度预测
DOI：
10.1080/10584587.2017.1338881
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发表时间：
2017-01-01
影响因子：
0.7
通讯作者：
Yu, Yongxin
中科院分区：
文献类型：
--
作者：
Li, Lubin;Wu, Mingyang;Yu, Yongxin
高温合金广泛应用于航空航天、船舶、石油化工等领域，其加工表面质量对恶劣工况下的性能起着关键作用。表面粗糙度是衡量表面质量的重要指标，建立表面粗糙度的预测模型具有重要意义。针对GH4169表面粗糙度预测模型，提出了一种自适应遗传算法。理论分析表明，采用自适应遗传算法建立的预测模型能有效地预测GH4169车削加工的表面粗糙度。该预测模型可以优化车削工艺参数，提高表面质量。
ABSTRACT Super alloy is widely used in aerospace, ships, petrochemical, etc. Processed surface quality of super alloy plays a key role in the performances in poor working conditions. Surface roughness is significant in evaluating surface quality, so it is important to establish the prediction model of surface roughness. An adaptive genetic algorithm is proposed for the prediction model of GH4169 surface roughness. According to the theoretical analysis, the prediction model established with adaptive genetic algorithm could effectively predict the surface roughness of turning process of GH4169. Moreover, the prediction model could optimize the turning parameters and to improve the surface quality.