Multi-objective cutting data generation algorithm and cutting parameter optimization method for CNC machine tools

Abstract

The invention relates to a multi-objective cutting data generation algorithm and a cutting parameter optimization method of a numerically controlled machine tool. The core algorithm of the database includes the calculation of cutting processing states such as cutting force and dynamic cutting vibration, as well as load conditions such as cutting power and spindle torque, processing accuracy, surface Processing quality such as roughness, processing efficiency information such as processing time and material removal rate, calculation of benefit evaluation such as tool wear and production cost. Optimize the cutting parameters according to the calculation results in order to improve the utilization rate of the machine tool and the processing efficiency of the parts. And the cutting database algorithm developed for CNC lathes and CNC turning centers is integrated on the domestic I5 intelligent CNC system platform, which is convenient for machine tool users to call and select cutting parameters according to specific processing needs, and can predict the possibility of setting parameters before actual cutting resulting cutting effect.

Description

Technical field [0001] The invention relates to a data generation algorithm and a parameter optimization method, in particular to a multi-objective cutting data generation algorithm and a cutting parameter optimization method of a numerical control machine tool. Background technique [0002] In this field, there are methods of applying genetic algorithm and neural network algorithm to determine the optimal cutting parameters in the existing foreign algorithms. This method also considers the minimum processing cost and the shortest time as well as the final processing surface roughness. Their shortcomings are It requires a long learning process and specific consideration of the algorithm. In 1992, someone proposed a mathematical algorithm for optimizing the assembly line multi-process machining system. The algorithm took into account the limitations of the machine tool performance in the machining system, and optimized the cutting parameters of each process within a given processi...

AI Technical Summary

Problems solved by technology

[0005] 1. The comprehensiveness of the existing cutting database algorithm is not strong, and the optimization target of cutting parameters is not comprehensive

The existing commercial cutting database algorithm has a relatively broad field of application, and is less targeted to specific fields and specific users; the database established by users is only suitable for their own enterprises or related enterprises; the database provided by tool manufacturers is mostly aimed at tool applications, and lacks the inclusion of machine tools-parts -Process considerations such as fixtures, among the factors considered in the parameters provided, the machine tool factors are less considered, which is not conducive to improving the processing efficiency of the machine tool

[0006] 2. The existing cutting database algorithm and cutting parameter optimization are the cutting parameters recommended by users, or do not provide users with more priority choices

[0007] 3. The evaluation of the processing effect that the recommended cutting parameters can achieve is not provided

[0008] 4. The algorithm and cutting parameter optimization of the existing cutting database lack the experimental data of the machine tool, and it cannot be effectively integrated with the CNC machine tool or the CNC system.

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