Cutting process parameter optimization method and system, computer equipment and storage medium

Abstract

The invention provides a cutting process parameter optimization method and system, computer equipment and a storage medium. The cutting process parameter optimization method comprises the following steps that an original machining program of machining equipment is obtained; replacing the original data of each process parameter in the original processing program with the corresponding macro-variable, and updating the original processing program into an optimized processing program; taking the original data of each process parameter as initial processing data, and optimizing the initial processing data to obtain optimized data; assigning the data after each optimization to a corresponding macro variable in the optimization processing program, and processing the workpiece to obtain a processed product; whether optimization is finished or not is judged according to the quality of the machined product and whether equipment operation data meets constraint conditions or not when the workpiece is machined through the optimized machining program.

Description

technical field [0001] The present invention relates to the field of mechanical processing, in particular to a method, system, computer equipment and storage medium for optimizing cutting process parameters. Background technique [0002] With the development of society, the degree of automation is getting higher and higher, and CNC lathes have become the main choice for machining with their automated and refined processing methods. In the field of CNC machining, how to optimize the machining parameters to achieve the best machining efficiency is a problem that needs to be solved in the industry. At present, there are mainly the following solutions in the industry: [0003] 1. According to CAD drawings, workpiece parameters and tool parameters, use or develop software similar to CAM (computerAided Manufacturing, computer-aided manufacturing), and recommend NC (Numerical Control, digital control) programs according to the software, containing relatively reasonable process par...

AI Technical Summary

Problems solved by technology

This method requires CAD drawings or 3D models, as well as more workpiece parameters, tool parameters and other parameters that determine the processing technology, and only a recommended processing program can be given, and professional technicians are still required to further modify and adjust according to the actual situation. Test knife, further optimize the process parameters in the program by artificial, and only use the most original modification method to directly modify the parameter value

[0004] 2. Based on machine tools, cutting tools, materials, cutting parameter databases or establishing a cutting process parameter management system; as a process parameter optimization recommendation, a more comprehensive database and recommendation model are required. The parameters are relatively accurate, and a large amount of parameter information is required, making it difficult to implement Large, and it is a one-time recommendation, which cannot guarantee comprehensive consideration and further optimization

Although the recommended process parameters are better than Method 1, most of the recommended processing programs and process parameters are modified by manually operating the machine tool to modify the program, which is complicated

[0005] 3. The iSESOLBOX (Intelligent Magic Box) product of Intelligent Yunke optimizes the processing parameters on the basis of modifying the PLC (programmable logic controller) program and NC program, but this system divides each processing section of the NC program into Optimized section and non-optimized section add a lot of subroutines, the modification is complicated, the operation is difficult, requires high professional skills, and the modification time is long

[0006] Based on the complexity of the existing metal cutting process that relies on CAM software to compile NC programs, the recommended process parameters are difficult to achieve better adaptation, resulting in poor optimization results

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