Optimization of laser CNC machining errors using PO-ELM algorithm

Le résumé fourni par la source

Abstract By studying the problems of large machining errors and poor precision in the actual processing of laser numerical control machine tools, in order to improve machining accuracy and production quality, a method of using the American Lion optimizer to optimize the principle of a limit learning machine to control laser numerical control machining errors is proposed. The laser numerical control machining module is developed using an open numerical control system. By simulating non-uniform rational B-spline curves, the input coordinates and output displacements of each servo axis are collected as training samples for neural network data organization and analysis. A PO-ELM algorithm model is proposed to train and test the dataset, analyze the machining error status, and achieve the goal of optimizing laser numerical control machining errors. The experimental results show that the PO-ELM algorithm model can effectively optimize the problem of processing errors in laser CNC machine tools, significantly reduce the root mean square error, reduce the processing error of laser CNC machine tools, achieve high-precision processing, and have good control performance.

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