A Parallel Multi-objective Machining Parameter Optimization Method Responding to Dynamic Disturbance

Abstract

The invention discloses a parallel multi-objective processing parameter optimization method in response to dynamic disturbance, comprising: step 1, when a numerical control machine tool performs multi-objective processing, load the feature set to be processed allocated to the current numerical control machine tool, and divide the feature set into multiple feature subset; step 2, judge whether there is an emergency workpiece insertion disturbance, and divide the feature subset into multiple feature subsets; step 3, establish a processing energy consumption model, a processing time model and a tool wear model, according to the current Whether the processing process is disturbed, the parallel multi-objective dual-archive evolutionary algorithm is used to adaptively deploy the optimization target, and at the same time find the optimal processing parameters for the multiple feature sub-subsets obtained in step 2, and write them into the Gcode program of the corresponding feature Middle; step 4, delivering the Gcode program to the CNC machine tool for execution. The invention realizes the optimization of processing parameters in response to dynamic disturbances in the processing process through a parallel multi-objective double-archive evolutionary algorithm on the basis of self-adaptive allocation of optimization targets.

Description

technical field [0001] The invention relates to the field of sustainable manufacturing, in particular to a parallel multi-objective processing parameter optimization method responding to dynamic disturbances. Background technique [0002] In the NC machining process, the selection of machining parameters will have a great impact on the machining quality, machining efficiency, energy consumption of the machining process, and the service life of the tool. Therefore, the problem of processing parameter optimization has attracted extensive attention of experts and scholars, and a variety of processing parameter optimization methods have been produced. The overall optimization process of these methods mainly includes the following three steps: [0003] (1) Determine the optimization goal. The work at this level aims to select the optimization target that meets the actual processing conditions and determine the processing parameters. [0004] (2) Experimental design. The work a...

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Problems solved by technology

[0007] Most of the existing research work on the optimization of machining parameters is based on the specific conditions of the workpiece before machining. The above method is used to find the optimal cutting parameters only once before the first machining, and then the optimal cutting parameters found before are used in the entire machining process. Optimal cutting parameters completely ignore possible disturbances in the machining process, such as urgent order, urgent workpiece insertion tool wear, etc.; there are also a small amount of work that considers dynamic disturbance events from inside the machine during machine processing (for example, tool wear) , but ignores dynamic disturbance events from outside the machine (e.g., order rush, urgent workpiece insertion, etc.)

In addition, although there are some dynamic multi-objective optimization algorithms, these algorithms are used to solve continuous function optimization problems that do not require high real-time performance of the algorithm, and are not suitable for processing parameter optimization problems that require real-time feedback on dynamic disturbances

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