Cutting Parameter Recommendation System Based On MATLAB GUI In Multi-objective Scenarios

The correct selection of cutting parameters is a difficult problem that machining companies face every day.Traditional processing optimization methods have been limited to objective functions related to cost or productivity,but the more critical role of optimization is to optimize various processing performance indicators,such as Tool life,surface roughness,cutting force,cutting temperature,etc.Appropriate cutting parameters can make full use of various automatic processing machine tools,CNC machine tools,machining centers and other machine tools in FMS.It is of great significance for expensive equipment to improve production efficiency,reduce processing costs,and increase the utilization of equipment.But suitable cutting parameters are not easy to find.Database technology and artificial intelligence algorithms are the main methods to solve this problem.However,there are still some problems,such as: the case retrieval method is complicated,and the retrieved cutting parameters have multiple groups or range values.When multiple sets of cutting parameters or cutting parameters are in range,it will affect the user to make a correct decision.Based on the three factors(cutting speed,feed rate,and depth of cut)based on the experimental data of four-level turning of 45 steel,this paper studied the discrete value and continuous value in two multi-objective scenarios(cutting force,cutting temperature,and workpiece surface roughness).,Processing efficiency)cutting parameter recommendation algorithm,using milling as an example to study database screening technology,and using computer technology to establish a cutting parameter recommendation system.The main research contents of this article are as follows:(1)Analysis of turning 45 steel experimental data.Based on the experimental data and the range analysis,this paper analyzes the influence of cutting parameters(turning speed,feed amount,back feed amount)on the experimental results(cutting force,cutting temperature,workpiece surface roughness).It laid the foundation for the allocation of cutting parameter weights in cutting parameter recommendation.(2)Measures of similarity between cases in the database.This article builds a case library of cutting parameter recommendation system based on AI technology-case reasoning technology,and calculates the similarity between the current processing problem and the data instance by using gray relational analysis(GRA).This paper quantifies the non-numerical attributes of the problem description part into numerical data,and unifies the calculation method of similarity.After the application of the example,grey correlation analysis can select the cases that are most similar to the problem to be solved from the case library.It verifies the feasibility of the GRA method in case retrieval and the rationality of the quantified attribute values.(3)Recommendation of discrete cutting parameters in multi-objective scenarios.In this paper,the weights of cutting parameters under different targets(cutting force,cutting temperature,workpiece surface roughness,and machining efficiency)are determined based on the range values obtained from range analysis.Based on TOPSIS,the coefficient of closeness between the cutting parameters in the case library and the most positive solution and the most negative ideal solution.Through the application of the example,the method based on TOPSIS can optimize a set of cutting parameters that meets the user's goals from a large number of cutting parameters.It verifies the feasibility of TOPSIS to optimize the cutting parameters.(4)Recommendation of continuous cutting parameters in multi-objective scenarios.In this paper,the continuous cutting parameters are discretized into discrete values through scientific experimental design.Based on the experimental data,a quadratic regression model of cutting force,cutting temperature,and workpiece surface roughness is established based on response surface method(RSM).According to the regression model,a multi-objective(cutting force,cutting temperature,workpiece surface roughness,machining efficiency)optimization function is established,and the cutting parameters are optimized globally based on the genetic optimization algorithm.Through the application of the example,it verifies the fitting accuracy of the quadratic regression model and the correctness of the multi-objective optimization function.(5)Implementation of cutting parameter recommendation system.The system interface is developed based on MATLAB GUI,the database is based on SQL Server2008,and the use and function of the system are demonstrated through examples.