Prediction Models and Generalization Performance Study in Electrical Discharge Machining

Qing Gao; Qin He Zhang; Shu Peng Su; Jian Hua Zhang; Rong Yu Ge

doi:10.4028/www.scientific.net/AMM.10-12.677

Paper Titles

Inequi-Diameter Milling of Gear Indexing Cam and Computer Aided NC Programming
p.657

3-D Modeling for Teeth Correction on the Basis of Optical Measurement
p.662

Assessment of Ground Surface Roughness Based on Computer Vision Technology
p.667

Optimization Algorithm for Automated Assembly of Circular Parts
p.672

Prediction Models and Generalization Performance Study in Electrical Discharge Machining
p.677

Research on Feature-Based Rapid Programming for Aircraft NC Parts
p.682

Prediction of Residual Stress in Hard Turning of AISI 52100 Using 2D FEM
p.688

Optimization of Laser Peening Parameters Using Taguchi Method
p.692

An Integrated Modeling Method for an Engine Cylinder Body Based on Cross-Sectional Imaging System
p.697

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 10-12Prediction Models and Generalization Performance...

Prediction Models and Generalization Performance Study in Electrical Discharge Machining

Abstract:

In the past decade, artificial neural network(ANN) has been applied in Electrical discharge machining(EDM). However, most of them only discuss parameter prediction or optimization result, few tell how to improve generalization performance. In this study, machining process models have been established based on different training algorithms of ANN, namely Levenberg-Marquardt algorithm (LM), Resilient algorithm (RP), Scaled Conjugate Gradient algorithm (SCG) and Quasi-Newton algorithm(BFGS). All models have been trained by same experimental data, checked by another group data, their generalization performance are compared. Take LM as the example, some main factors that may influence generalization performance are discussed.