Machining process parameters optimization using soft computing technique

Abstract

This work introduces an approach for optimization machinability measures of power consumption, machining time, and the surface roughness (PMS). This approach is starting with market customer’s demands, passing by optimizing the machinability measures (PMS), and ending by the optimized cutting conditions. The fuzzy logic was used to define the weights of each of required machinability measurement using method through expert rules depending on factory requirements. Genetic algorithm was formulated for giving optimum output values based on the customer’s demands. A neural network was designed for controlling the input cutting conditions with the PMS output parameters. The proposed soft computing technique creates reasonable results compared to experimental results and gives rich investigations for optimizing the output parameters not only for increasing productivity and quality demands but also for saving power consumed. The variation of consumed power, machining time, and surface roughness was calculated based on different customer demand levels. When the machining time and power consumed importance increased, the proposed technique reduced them by about 20% and 10% for the testes case.

Fuzzy information recognition and translation processing in English interpretation based on a generalized maximum likelihood ratio algorithm

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Fuzzy Information Recognition and Translation Processing in English Interpretation based on Artificial Intelligence Recognition Technology

“…where is the confidence distance, the greater the , the lower the level of support between the two interpretation languages, and the higher the contrary. Then, the fusion function of the two interpretation languages is defined as (10) Therefore, if the i and j interpretation languages can be expressed as fusion functions, then the fusion matrix can be expressed as (11) Now, given a fusion vector to represent the interpretation language that can be recognized by all other interpretation languages, in order to ensure the maximum reliability, it is necessary to determine the minimum amount of fusion between other interpretation languages and the i-th interpretation language, i.e.…”

Optimization of Production Process Parameters for Polishing Machine Tools in Crankshaft Abrasive Belt based on BP Neural Network and NSGA-II

“…In the field of process parameter optimization, some scholars integrate predictive models with optimization models, establishing optimization models with the predictive model as the objective function [18][19][20][21][22][23]. Drawing on the work of these authors, this paper plans to use the NSGA-II algorithm with the predictive results of the BP neural network as the fitness value to explore the optimal production process parameters for the crankshaft abrasive belt polishing machine.…”