A new method for optimizing process parameters of active measurement grinding based on grey target decision making

Abstract

Active measurement technology is used widely in the modern precision-grinding process; currently, however, it is tedious and time consuming to adjust the process parameters of grinding process. This process usually depends on the experience of operators, which directly affects the processing efficiency and intelligent control level. Aiming to optimize these grinding-process parameters, we propose a grey target decision-making method based on the uniform effect measure to realize the intelligent optimization of process parameters. First, we obtain the experimental data of process parameters and evaluation parameters using the orthogonal experimental method, and then we calculate and analyze the degree of relation between process parameters and evaluation parameters based on grey system theory. Then we can obtain the process parameters that have the greatest influence on the workpiece surface roughness, workpiece roundness, and grinding process time. Second, through the proposed grey target decision-making method based on the uniform effect measure, and combined with grey relation analysis, we assign the weights of different evaluation parameters. By optimizing the calculation of multigroup evaluation parameters, we obtain the group of evaluation parameters with the best synthetic effect and identify the optimal combination of parameters to guide production. Through a large number of experimental verifications and analyses, we find that the proposed grinding process parameters optimization method can obtain the best combination of process parameters and can effectively improve processing quality and processing efficiency. The research results provide a theoretical basis for the optimization of grinding process parameters and enhance the intelligent degree of precision grinding.

Multi-Objective Optimization of the Process Parameters of a Grinding Robot Using LSTM-MLP-NSGAII

“…The constant term in the formula obtained by integrating the substitutions is integrated as C o in Equation (7), and the static variable representing the structural property is integrated as Q o in Equation (8). The simplified surface roughness Equation ( 6) is obtained by combining the constant C o and the structural property term Q o , obtaining a mathematical model for the relationship between the PPGR and the quality of Ra.…”

“…In order to obviate the current reliance on manual empirical judgement and to quantify more accurately the influence of PPGR on grinding quality, researchers have proposed to use artificial intelligence reinforcement learning to predict the grinding quality [7]. Based on the prediction model of grinding quality based on the grinding parameters, the grey target decision-making method [8], the response surface methodology [9] and other methods have been used to optimize the process parameters in order to improve the grinding quality. Xie et al [10] optimized the polishing parameters of the polishing process for mold steel by using the response surface methodology to optimize the polishing pressure, feed speed and rotating speed of the tool, aiming to reduce the surface roughness of the workpiece.…”

Optimization of MWCNTs/Al nanocomposite fabrication process parameters for mass density and hardness

“…The grey relational technique was used by Zheng et al to increase the processing efficiency and quality of the grinding process. 37 Krishnan and Subramaniam used Taguchi coupled with grey relational technique to evaluate the optimal parameters of the friction stir welding process to improve the mechanical properties of dissimilar aluminum alloy joints. They found that welding speed was the most influential parameter that affected multi-response characteristics.…”

Investigation of drilling properties of AA7075/Al2O3 functionally graded materials using gray relational analysis

“…20 Gray relational analysis (GRA) is one of the effective multiresponse optimization methods used to evaluate the effects of factors and their levels. 21 GRA creates optimized response input parameters, which have different weights of evaluation. 22 Ranking of process parameters can also be accomplished by considering the effect of all response parameters.…”