Prediction and optimization of robot processing technology based on neural network and genetic algorithm

ObjectivesWith the rapid development of industrial automation and intelligent manufacturing, the application 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 remain 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.MethodsTaking the KUKA KR60L30HA industrial robot equipped with a brazed flat grinding head as the representative, 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. Subsequently, a three-layer grinding force prediction model based on a BP neural network was established, and linear regression 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 material removal rates as the indicator.ResultsThe 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 decrease 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 fluctuates 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 tangential, 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 significant 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.ConclusionsThe constructed grinding force prediction and process parameter optimization model has achieved systematic 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 influences of axial cutting depth and the feed rate, which are particularly significant. The optimal process parameter combination 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.