Dynamic disturbance response-oriented parallel multi-objective machining parameter optimization method

Abstract

The invention discloses a dynamic disturbance response-oriented parallel multi-objective machining parameter optimization method. The method comprises the following steps that: step 1, when a numerical control machine tool performs multi-objective machining, a to-be-machined feature set distributed to the current numerical control machine tool is loaded, and the feature set is divided into a plurality of feature subsets; step 2, whether emergency workpiece insertion disturbance exists is judged, and the feature subsets are divided into a plurality of feature sub-subsets; step 3, a machining energy consumption model, a machining time model and a cutter abrasion model are established, a parallel multi-objective double-archive evolutionary algorithm is adopted to adaptively allocate optimization objectives according to a result indicating whether disturbance occurs in a current machining process, and meanwhile, optimal machining parameters are searched for the plurality of feature sub-subsets obtained in the step 2, and are written into the Gcode program of corresponding features; and step 4, the Gcode program is delivered to the numerical control machine tool so as to be executed. According to the dynamic disturbance response-oriented parallel multi-objective machining parameter optimization method of the invention, on the basis of the self-adaptive allocation of the optimization objectives, machining parameter optimization for machining process dynamic disturbance response is realized through the parallel multi-objective double-archive evolutionary algorithm.

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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