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首页> 外文期刊>Journal of Manufacturing Processes >Vision and sound fusion-based material removal rate monitoring for abrasive belt grinding using improved LightGBM algorithm
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Vision and sound fusion-based material removal rate monitoring for abrasive belt grinding using improved LightGBM algorithm

机译：采用改进的LightGBM算法对磨料带研磨的视觉和声音融合材料去除率监测

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摘要

Accurate material removal modeling is the basis for optimizing the surface quality and improving the performance of equipment components. In this study, a multi-sensor fusion method of vision and sound is used to monitor in-process grinding material removal rate (MRR). First, belt grinding experiments are conducted using different grinding parameters, and vision and sound signals are captured by industrial CCD cameras and an omnidirectional condenser microphone, respectively. Second, the features of the captured grinding spark images are extracted based on two aspects: color and texture, and those of the grinding sound are investigated in the time, frequency, and time-frequency domains. Moreover, the complementarity between the vision and sound signals and their sensitivity to different grinding parameters are discussed. Finally, based on feature-level fusion strategies, the Pearson correlation coefficient and the sequential backward selection algorithms are jointly used to select the optimal feature subsets. MRR prediction models are established using the selected feature subsets and an improved light gradient boosting machine (LightGBM) algorithm. The test results show that the error in the MRR prediction model of same-specification abrasive belts is less than 3 %, and the coefficient, R2, is as high as 99.2 %. The proposed method can be used to predict the MRR resulting from a single grinding parameter and multiple ones, using the same-specification abrasive belts. Compared to other prediction models, the improved LightGBM model is superior in terms of the time factor without reduction in the accuracy of the model.
机译：准确的材料去除模型是优化表面质量和提高设备部件性能的基础。在该研究中，使用多传感器融合方法的视觉和声音来监测过程中的研磨材料去除率（MRR）。首先，使用不同的研磨参数进行皮带磨削实验，并且通过工业CCD摄像头和全向电容麦克风捕获视觉和声音信号。其次，基于两个方面提取捕获的研磨火花图像的特征：颜色和纹理，并且在时间，频率和时频域中研究研磨声音。此外，讨论了视觉和声音信号之间的互补性及其对不同研磨参数的敏感性。最后，基于特征级融合策略，Pearson相关系数和顺序向后选择算法共同使用来选择最佳特征子集。使用所选特征子集和改进的光梯度升压机（LightGBM）算法建立MRR预测模型。测试结果表明，相同规格磨料带的MRR预测模型中的误差小于3％，系数R2高达99.2％。所提出的方法可用于预测使用相同规格磨料带的单个研磨参数和多个研磨参数和多个磨削的MRR。与其他预测模型相比，改进的LightGBM模型在时间因数方面优越，而不降低模型的准确性。

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