Numerical control machining programs are typically generated by computer-aided manufacturing systems,guiding numerical control machine tools in linear interpolation motion by approximating curves with tiny linear segments.With the rising complexity and precision demands of the process,the volume of data in numerical control machining programs has seen a drastic increase.This not on-ly complicates data storage and transmission but also leads to frequent speed adjustments during the operation of the machine tool.In response to these challenges,an improved Douglas-Peucker three-dimensional numerical control machining trajectory compression method that integrates deep learning has been proposed.This method takes into account the geometric characteristics of the data point sequence in the machining trajectory by introducing hyperparameters of curvature and distance tolerance.It dynamically optimizes the hyperparameters within the algorithm using a deep neural network model,thereby achieving a higher compression efficiency.Addition-ally,the algorithm employs a KD tree structure to refine error calculations,ensuring that the compressed data can precisely represent the characteristics of the original data within a given tolerance range.Experiments have demonstrated that this algorithm can signifi-cantly reduce the volume of data and guarantee that the compressed data accurately reflects the attributes of the original data.
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