The research about micromachining accuracy control based on fuzzy evaluation algorithm

Abstract

SummaryThe calculation method of nonlinear artificial neural network based on fuzzy evaluation algorithm is introduced. This method is used to evaluate the precision of micro machined machining and to find the influence factors of the precision of micromachining. The traditional machining precision control mainly adopts the linear control strategy, but the micromachining involves the mechanical characteristics of the micro field, and many linear machine adding precision control methods cannot effectively control the precision of the micromachining. Using fuzzy evaluation method, large data cluster training method can be used to fit the control curve of micromachining accuracy. The nonlinear mapping of the fuzzy evaluation algorithm is realized by the large sample training method, and the objective and effective parameter evaluation and precision control of the machining precision in the micro machined field are realized. It is proved that the fuzzy evaluation algorithm can accurately evaluate the precision of micro machined machining. Learning through large data samples can effectively improve the topology practicability and rationality of the algorithm.

Designing a digital twin for micromanufacturing processes

“…There seem to be two categories of data driven models; the first one is mostly based on non-relational data and uses Machine Learning techniques, while the second one mostly refers to time series and concerns Auto-Regressive-Moving-average with exogenous noise (ARMAX) models. The use of Machine Learning models is without a doubt extremely popular for Quality assessment, as in the case of using Neural Networks for the quality assessment for micromachining 42 or for optimization such as in the case of using particle swarm optimization of process parameters in laser micromachining. 43 Their use can be categorized into several main groups; classification, clustering, association rules, feature selection 44 as well as regression.…”