Parameter Recognition and Optimization of Residual Stress Prediction Model Based on Genetic Algorithm

Hai Tao Liu; Ya Zhou Sun; De Bin Shan

doi:10.4028/www.scientific.net/AMR.690-693.2535

Paper Titles

The Influence of Variable-Speed Waveforms on Vibration Suppression in Time-Varying Speed Cutting
p.2514

Drilling Simulation of Carbon Fiber Reinforced Plastic Composites Based on Finite Element Method
p.2519

Experimental Investigation of Effect of Gear Milling Parameters of Al-B₄C Composite Gears on Surface Roughness
p.2523

Investigation on Delamination Factor in Drilling Medium Density Fiberboards with Variable Feed Pressure Using Multi-Spindle Drill
p.2529

Parameter Recognition and Optimization of Residual Stress Prediction Model Based on Genetic Algorithm
p.2535

Prediction of Surface Roughness on CNC Turning Based on Monitoring of Cutting Force and Cutting Temperature
p.2540

Study on the Ultrasonic Characteristics in the Contact Interface under Ultrasonic Cutting
p.2550

The Effect of Tool Geometrical Parameters on Cutting Temperature Based on Deform-3D
p.2554

The Nano-Cutting Mechanism Research of Polysilicon Based on Molecular Dynamics
p.2559

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 690-693Parameter Recognition and Optimization of Residual...

Parameter Recognition and Optimization of Residual Stress Prediction Model Based on Genetic Algorithm

Abstract:

Building a forecast model of cutting surface's residual stress due to different cutting parameters and finding the optimal combination of cutting parameters is the demand of developing ultra precision industries such as aviation and aerospace. Owing to the limitation of the least square method and other conventional methods, genetic algorithm was introduced into the parameter identification of cutting surface's residual stress forecast model and to optimize cutting parameters. In this way, a cutting surface's residual stress forecast model of aluminum alloy is given. By contrast, genetic algorithm is more suitable to identify parameters of cutting surface's residual stress forecast model, and to guarantee the optimal cutting parameter combination.

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Periodical:

Advanced Materials Research (Volumes 690-693)

Pages:

2535-2539

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.690-693.2535

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Online since:

May 2013

Authors:

Hai Tao Liu, Ya Zhou Sun, De Bin Shan

Keywords:

Genetic Algorithm (GA), Optimization, Prediction Model, Residual Stress

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