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国际标准刊号:ISSN 1000-582X
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基于改进人工蜂群算法和极限学习机的刀具磨损监测
郭一君, 周杰, 王时龙, 易力力, 康玲, 高波
重庆大学 机械工程学院, 重庆 400044
摘要:
为了提高机械切削加工中刀具磨损量的实时监测精度,运用极限学习机建立刀具磨损监测模型,提出一种引入虚拟蜂的改进人工蜂群算法,对极限学习机随机产生的输入层权值和隐含层阈值进行优化。采用时域分析和经验模态分解,提取铣削加工中的切削力信号、振动信号以及声发射信号的时域特征和内禀模态能量比,从中选出对刀具磨损敏感的特征作为监测特征。利用建立的监测模型计算得到刀具磨损值,实验结果表明,优化后的极限学习机能够准确地预测刀具磨损值,且具有更简单的网络结构,同时改进后的蜂群算法也表现出了更好的寻优能力。
关键词:  刀具磨损  虚拟蜂  极限学习机  特征提取
DOI:10.11835/j.issn.1000-582X.2018.06.001
分类号:
基金项目:国家科技支撑计划资助项目(2015BAF02B02)。
Tool wearmonitoring based on improved artificial bee colony algorithm and extreme learning machine
GUO Yijun, ZHOU Jie, WANG Shilong, YI Lili, KANG Ling, GAO Bo
College of Mechanical Engineering, Chongqing University, Chongqing 400044, P. R. China
Abstract:
To improve the real-time monitoring accuracy of the actual wear of cutting tools, Extreme Learning Machine (ELM) algorithm was applied to establish the tool wear model. An Improved Artificial Bee Colony (IABC) algorithm with a virtual bee was proposed to tune the input layer weights as well as the hidden layer biases in ELM. Time-domain analysis and Empirical Mode Decomposition (EMD) were used to extract the time-domain features and energy ratio of Intrinsic Mode Function (IMF) from cutting force signal, vibration signal and acoustic emission signal, and the features sensitive to tool wear were selected as the monitoring features. Then the tool wear value could be calculated through the trained model. The result show that the optimized ELM can predict the tool wear value accurately, and has a simpler network structure. Meanwhile, the IABC algorithm also shows a better searching ability.
Key words:  tool wear  extreme learning machine  virtual bee  feature extraction
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