基于在线学习的数控加工刀具寿命动态预测方法
|
摘要
预测刀具寿命对保证零件质量和控制加工成本意义重大,但刀具磨损过程复杂多变,刀具剩余寿命受工况影响难以准确预测。针对以上问题,提出了一种基于在线学习的刀具寿命动态预测方法,以长短时记忆网络为基础模型,融合在线学习模块,使得模型能够在加工过程中自动更新参数,实现变工况下刀具寿命的精确预测。进行了铣削加工试验,结果表明,刀具寿命动态预测方法可以有效提升刀具寿命预测精度。
The prediction of tool life is of great significance to ensure the quality of parts and control the cost of machining. However, the tool wear process is complex and changeable, and it is difficult to accurately predict the residual life of the cutting tools affected by machining conditions. To solve the above problems, this paper presents a dynamic prediction method of tool life based on online learning. Using long-short term memory as base model and integrating the online learning module, the final model can automatically update the parameters during the machining process, and the accurate prediction of tool life under variable working conditions can be realized. The milling experiment was carried out, and the experimental results show that the dynamic prediction method of tool life can effectively improve the precision of tool life prediction.
The prediction of tool life is of great significance to ensure the quality of parts and control the cost of machining. However, the tool wear process is complex and changeable, and it is difficult to accurately predict the residual life of the cutting tools affected by machining conditions. To solve the above problems, this paper presents a dynamic prediction method of tool life based on online learning. Using long-short term memory as base model and integrating the online learning module, the final model can automatically update the parameters during the machining process, and the accurate prediction of tool life under variable working conditions can be realized. The milling experiment was carried out, and the experimental results show that the dynamic prediction method of tool life can effectively improve the precision of tool life prediction.
引文
[1]DOWNEY J,O?LEARY P,RAGHAVENDRA R.Comparison and analysis of audible sound energy emissions during single point machining of HSTS with PVD TiCN cutter insert across full tool life[J].Wear,2014,313(1-2):53-62.
[2]詹华西,彭超雄.积算加工时间实现数控机床刀具寿命管理[J].制造技术与机床,2008(8):112-114.ZHAN Huaxi,PENG Chaoxiong.NCtools life management based on cutting time calculation[J].Manufacturing Technology&Machine Tool,2008(8):112-114.
[3]ARAMESH M,ATTIAM H,KISHAWY H A,et al.Estimating the remaining useful tool life of worn tools under different cutting parameters:a survival life analysis during turning of titanium metal matrix composites(Ti-MMCs)[J].Cirp Journal of Manufacturing Science&Technology,2016,12:35-43.
[4]WANG P,GAO R X.Stochastic tool wear prediction for sustainable manufacturing[J].Procedia Cirp,2016,48:236-241.
[5]GRIGORIEV S N,GURIN V D,VOLOSOVA M A,et al.Development of residual cutting tool life prediction algorithm by processing on CNC machine tool[J].Materialwissenschaft Und Werkstofftechnik,2013,44(9):790-796.
[6]SI X S,WANG W,HU C H,et al.Remaining useful life estimation-A review on the statistical data driven approaches[J].European Journal of Operational Research,2011,213(1):1-14.
[7]SIDDHPURA A,PAUROBALLYR.A review of flank wear prediction methods for tool condition monitoring in a turning process[J].The International Journal of Advanced Manufacturing Technology,2013,65(1):371-393.
[8]KARAM S,CENTO BELLIP,D’ADDONA D M,et al.Online prediction of cutting tool life in turning via cognitive decision making[J].Procedia Cirp,2016,41:927-932.
[9]BENKEDJOUH T,MEDJAHER K,ZERHOUNI N,et al.Health assessment and life prediction of cutting tools based on support vector regression[J].Journal of Intelligent Manufacturing,2015,26(2):213-223.
[10]YU J,LIANG S,TANG D,et al.A weighted hidden Markov model approach for continuous-state tool wear monitoring and tool life prediction[J].International Journal of Advanced Manufacturing Technology,2016,91(1-4):1-11.
[11]WIKLUND H.Bayesian and regression approaches to on‐line prediction of residual tool life[J].Quality&Reliability Engineering International,2015,14(5):303-309.
[12]WU D,JENNINGS C,TERPENNYJ,et al.A Comparative study on machine learning algorithms for smart manufacturing:application of random forests to tool wear prediction[J].Journal of Manufacturing Science&Engineering,2017,139(7):71018(1-9).
[2]詹华西,彭超雄.积算加工时间实现数控机床刀具寿命管理[J].制造技术与机床,2008(8):112-114.ZHAN Huaxi,PENG Chaoxiong.NCtools life management based on cutting time calculation[J].Manufacturing Technology&Machine Tool,2008(8):112-114.
[3]ARAMESH M,ATTIAM H,KISHAWY H A,et al.Estimating the remaining useful tool life of worn tools under different cutting parameters:a survival life analysis during turning of titanium metal matrix composites(Ti-MMCs)[J].Cirp Journal of Manufacturing Science&Technology,2016,12:35-43.
[4]WANG P,GAO R X.Stochastic tool wear prediction for sustainable manufacturing[J].Procedia Cirp,2016,48:236-241.
[5]GRIGORIEV S N,GURIN V D,VOLOSOVA M A,et al.Development of residual cutting tool life prediction algorithm by processing on CNC machine tool[J].Materialwissenschaft Und Werkstofftechnik,2013,44(9):790-796.
[6]SI X S,WANG W,HU C H,et al.Remaining useful life estimation-A review on the statistical data driven approaches[J].European Journal of Operational Research,2011,213(1):1-14.
[7]SIDDHPURA A,PAUROBALLYR.A review of flank wear prediction methods for tool condition monitoring in a turning process[J].The International Journal of Advanced Manufacturing Technology,2013,65(1):371-393.
[8]KARAM S,CENTO BELLIP,D’ADDONA D M,et al.Online prediction of cutting tool life in turning via cognitive decision making[J].Procedia Cirp,2016,41:927-932.
[9]BENKEDJOUH T,MEDJAHER K,ZERHOUNI N,et al.Health assessment and life prediction of cutting tools based on support vector regression[J].Journal of Intelligent Manufacturing,2015,26(2):213-223.
[10]YU J,LIANG S,TANG D,et al.A weighted hidden Markov model approach for continuous-state tool wear monitoring and tool life prediction[J].International Journal of Advanced Manufacturing Technology,2016,91(1-4):1-11.
[11]WIKLUND H.Bayesian and regression approaches to on‐line prediction of residual tool life[J].Quality&Reliability Engineering International,2015,14(5):303-309.
[12]WU D,JENNINGS C,TERPENNYJ,et al.A Comparative study on machine learning algorithms for smart manufacturing:application of random forests to tool wear prediction[J].Journal of Manufacturing Science&Engineering,2017,139(7):71018(1-9).