Experimental Investigation of Designed Parameters on Dimension Shrinkage of Injection Molded Thin-Wall Part by Integrated Response Surface Methodology and Genetic Algorithm: A Case Study

2011 ◽  
Vol 26 (3) ◽  
pp. 534-540 ◽  
Author(s):  
Chih-Cherng Chen ◽  
Pao-Lin Su ◽  
Chung-Biau Chiou ◽  
Ko-Ta Chiang
Keyword(s):  
Genetic Algorithm ◽  
Experimental Investigation ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Thin Wall ◽  
Injection Molded ◽  
Integrated Response ◽  
Thin Wall Part

Optimization of machining fixture layout using integrated response surface methodology and evolutionary techniques

2015 ◽  
Vol 230 (13) ◽  
pp. 2245-2259 ◽  
Author(s):  
KA Sundararaman ◽  
KP Padmanaban ◽  
M Sabareeswaran
Keyword(s):  
Genetic Algorithm ◽  
Response Surface Methodology ◽  
Response Surface ◽  
End Milling ◽  
Machining Accuracy ◽  
Swarm Optimization ◽  
Maximum Deformation ◽  
Fixture Layout ◽  
Integrated Response ◽  
The Relationship

Fixtures are the work-holding devices, widely used in manufacturing, to completely immobilize the workpiece during machining. The position of fixture elements around the workpiece strongly influences the workpiece deformation which in-turn affects the machining accuracy. The workpiece deformation can be minimized by finding the appropriate position for the locators and clamps. Thus, it is necessary to model the complex behavioral relationship that exists in the fixture–workpiece system. In this research paper, response surface methodology is used to model the relationship between position of locators and clamps and maximum deformation of the workpiece during end-milling, and then the developed model has been optimized by genetic algorithm and particle swarm optimization. As the predictive model is being developed by response surface methodology, a huge reduction in computational complexity and time is achieved during the optimization of machining fixture layout. Also, it is evident that the approach which integrates response surface methodology and particle swam optimization produces better results.

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