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<p>With the continuous development of manufacturing industry, machine tools, as the basis of processing, also play an indispensable role in the industry, and their processing accuracy directly affects the quality of workpieces. To optimize the accuracy of machine tool machining and compensate for errors generated during the machining process, an improved backpropagation neural network algorithm based on cosine similarity is proposed. Cosine similarity measures the directional similarity of vectors, improves the feature extraction ability of backpropagation neural networks, and evaluates whether the output values of backpropagation neural networks meet expectations. The number of input nodes in the backpropagation neural network is 6, the number of output nodes is 1, and the number of hidden layer nodes is 6. A machine tool with a longitudinal stroke of 1300&#xa0;mm, a vertical stroke of 1000&#xa0;mm, a horizontal stroke of 1100&#xa0;mm, and a spindle drive motor speed and power of 6000 r/min and 22&#xa0;kW, respectively, was used as the experimental object. The experimental results show that the algorithm proposed by the research institute can effectively predict the geometric error of the machine tool’s linear axis and the thermal error data of the spindle, with a high degree of fitting to the true values. At the same time, as for the error compensation, the average mean square error was 0.177, and the average error value of thermal error was reduced by 4.82. Moreover, the overall convergence speed of the algorithm was fast and the stability was high. Overall, the algorithm has a good effect on compensating machine tool machining errors, and can achieve intelligent machine tool machining error compensation.</p>

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Intelligent Machine Tool Machining Error and Compensation Model Integrating Backpropagation Neural Network and Cosine Similarity

  • 期刊论文

  • 发表日期：

    2025年12月5日

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  • Juan Zhang

摘要

With the continuous development of manufacturing industry, machine tools, as the basis of processing, also play an indispensable role in the industry, and their processing accuracy directly affects the quality of workpieces. To optimize the accuracy of machine tool machining and compensate for errors generated during the machining process, an improved backpropagation neural network algorithm based on cosine similarity is proposed. Cosine similarity measures the directional similarity of vectors, improves the feature extraction ability of backpropagation neural networks, and evaluates whether the output values of backpropagation neural networks meet expectations. The number of input nodes in the backpropagation neural network is 6, the number of output nodes is 1, and the number of hidden layer nodes is 6. A machine tool with a longitudinal stroke of 1300 mm, a vertical stroke of 1000 mm, a horizontal stroke of 1100 mm, and a spindle drive motor speed and power of 6000 r/min and 22 kW, respectively, was used as the experimental object. The experimental results show that the algorithm proposed by the research institute can effectively predict the geometric error of the machine tool’s linear axis and the thermal error data of the spindle, with a high degree of fitting to the true values. At the same time, as for the error compensation, the average mean square error was 0.177, and the average error value of thermal error was reduced by 4.82. Moreover, the overall convergence speed of the algorithm was fast and the stability was high. Overall, the algorithm has a good effect on compensating machine tool machining errors, and can achieve intelligent machine tool machining error compensation.