Open AccessResearch on Complex Product Parts Matching by using Improved Taguchi Method
Author(s) -
Fengque Pei,
Yifei Tong,
Minghai Yuan,
Song Haojie
Publication year - 2021
Publication title -
mechanika
Language(s) - English
Resource type - Journals
eISSN - 2029-6983
pISSN - 1392-1207
DOI - 10.5755/j02.mech.28182
Subject(s) - taguchi methods , dimension (graph theory) , matching (statistics) , function (biology) , product (mathematics) , convergence (economics) , genetic algorithm , mathematical optimization , engineering , fitness function , key (lock) , computer science , industrial engineering , mathematics , machine learning , statistics , geometry , computer security , evolutionary biology , pure mathematics , economics , biology , economic growth
With the development of intelligent manufacturing, the key strategic of complex equipment is becoming more and more obvious. How to realize the assembly of complex products has become the focus of intelligent manufacturing. This paper puts forward the improved Taguchi method to dimension chains measures, by using different quality loss function to different dimension chains, the cores are the Nominal-is-best, non-core is measured with the improved Smaller-is-better to improve convergence perusal and increase matching rate; General adopt Smaller-is-better to enhance assembly accuracy, reduce interference fit and assembly cost. Then the dimension chains quantitative model of complicated product assembly by using the signal-to-noise ratio and different weights is built up. The model contains modeling assumption, the objective function and the matching model. And this model is regard as the fitness function of genetic algorithm. Finally, the feasibility and efficiency of the scheme are verified by the case study.