Abstract
Objectives:With the rapid development of industrial automation and intelligent manufacturing,the applica-tion of industrial robots in the stone processing industry has garnered increasing attention.However,compared to other advanced manufacturing sectors,the mechanization,automation and intelligence of the stone processing industry re-main relatively underdeveloped.This study aims to explore the optimal processing methods for stone industrial robots'grinding operations using BP neural networks and genetic algorithms,taking the processing of sandstone as an example.Methods:Taking the KUKA KR60L30HA industrial robot equipped with a brazed flat grinding head as the representat-ive,the effects of different grinding process parameters on grinding force signals were systematically analyzed by the orthogonal test method.Firstly,the grinding force signal data were collected using different grinding test settings.Sub-sequently,a three-layer grinding force prediction model based on a BP neural network was established,and linear re-gression analysis was conducted using the orthogonal experimental data as samples to compare the predicted values with the experimental values.Finally,the genetic algorithm was applied to optimize grinding process parameters with materi-al removal rates as the indicator.Results:The grinding process parameters have significant effects on grinding forces,but the order of major and secondary effects of different parameters varies with grinding force components.The order of influence on tangential grinding force is the axial cutting depth ap,followed by radial cutting depth ae,the feed rate vw and the spindle speed n,while the order of influence on normal grinding force is ap,vw,ae and n.In contrast,the order of influence of axial grinding force is n,ae,vw and ap,while the total grinding force is most affected by vw,in the order of vw,ae,n and ap.Additionally,all components of grinding force generally increase with the rise of ae,ap and vw,and de-crease with the increase of spindle speed n.As the radial cutting depth ae increases,the ratio of normal to tangential grinding force shows a continuous downward trend.As the axial cutting depth ap increases,the grinding force ratio fluc-tuates within a certain range.As the spindle speed n increases,the grinding force ratio first increases,then decreases,and then slightly increases.When the feed rate vw increases,the grinding force ratio shows an initial decrease followed by an increasing trend.After training and predicting using the BP neural network model,the predicted values of tangen-tial,normal and axial grinding forces are compared with the experimental data.The maximum absolute relative error of the axial grinding force is 7.84%,and the correlation coefficient of the model is as high as 0.998 09,indicating signific-ant prediction accuracy of the mpdel.The optimal process parameter combination determined through genetic algorithm optimization is a radial cutting depth of 2.28 mm,an axial cutting depth of 2.98 mm,a spindle speed of 9 586.65 r/min and a feed rate of 2 207.67 mm/min.Under the optimal process parameter combination,the predicted material removal rate of the workpiece is 14 999.79 mm3/min,with a relative error of-5.37%compared to the actual experimental value of 14 194.44 mm3/min.This further demonstrates the effectiveness of the proposed optimization strategy.Conclusions:The constructed grinding force prediction and process parameter optimization model has achieved system-atic analysis and optimization of grinding force in robot sandstone processing.This model can clearly reveal the role of various grinding process parameters in machining and reflect their importance in improving machining efficiency and material removal rate.The changing trend of grinding force varies with different processing conditions,and there are significant differences in the influences of different parameter combinations on grinding forces,especially the influ-ences of axial cutting depth and the feed rate,which are particularly significant.The optimal process parameter combin-ation for material removal rate in stone processing is determined through BP neural network and genetic algorithm,and the relative error between the predicted value and the experimental value is relatively small.关键词
机器人加工/正交试验/BP神经网络/遗传算法/工艺参数优化Key words
robot processing/orthogonal test/BP neural network/genetic algorithm/optimization of process paramet-ers分类
金属材料
