Multi-target cutting data generation algorithm and cutting parameter optimization method of numerical control machine tool

Abstract

The present invention relates to a multi-target cutting data generation algorithm and cutting parameter optimization method of a numerical control machine tool. The core algorithms of a database comprise calculation of cutting processing states such as cutting force and dynamic cutting vibration, and calculation of load conditions such as cutting power and main shaft torque, processing quality such as processing precision and surface roughness, processing efficiency information such as processing time and material removal rate and benefit assessment such as cutter wearing and production cost.Cutting parameters are optimized according to a calculation result to improve the usage rate and part processing efficiency of a machine tool. A cutting database algorithm developed aiming at a numerical control lathe and numerical control turning center is integrated on a domestic I5 intelligent numerical control system platform to allow machine tool users to conveniently call and select cuttingparameters according to concrete processing demands and to predetermine cutting effects, which are likely to generate, before actual cutting.

Description

technical field [0001] The invention relates to a data generation algorithm and a parameter optimization method, in particular to a multi-target cutting data generation algorithm and a cutting parameter optimization method of a numerically controlled 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 among the existing foreign algorithms. This method takes into account the minimum processing cost, the shortest time and the final processing surface roughness at the same time. Their disadvantages are It requires a long learning process and targeted consideration of the algorithm. In 1992, someone proposed a mathematical algorithm to optimize the multi-process processing system of the pipeline. This algorithm takes into account the limitation of the performance of the machine tool in the processing system, and optimizes the cutting parameters of each ...

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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