An Assembly Prediction Model Based on GA-BP Neural Network

Song Ling Wang; Ming Jiang; Cheng Gong

doi:10.4028/www.scientific.net/AMR.915-916.214

Paper Titles

The Analysis on the Tribological Properties of TPU/PVC Blends with Chrome-Plated Steel under Dry Sliding
p.193

The Fault Analysis and Research of Turbine Generator Sliding Bearing Oil Film Instability
p.198

A New Method to Quickly Optimize the Thickness of Multi-Holes Plate
p.205

A Study about Wind Stresses of 4-Dish Parabolic Solar Collector System by Using FEM and Response Surface Method
p.209

An Assembly Prediction Model Based on GA-BP Neural Network
p.214

Design and Optimization of the Flywheel Compressor Based on SolidWorks and Fluent
p.218

Design of Electrically Controlled Movable Vane Regulation System of Axial Flow Fan
p.223

Development of a 90°/180° Upender for Large and Heavy Workpiece by Using Airbags-Drived Flipping Worktable
p.228

Hypoid Gear Three-Dimensional Modeling with Pro/E
p.236

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 915-916An Assembly Prediction Model Based on GA-BP Neural...

An Assembly Prediction Model Based on GA-BP Neural Network

Abstract:

The four key factors include gravity, roughness, bolt pressure and contact area as the input parameters of neural network, the GA-BP neural network was established for predicting the assembly deformation. And the experimental data is trained for this GA-BP neural network. Finally, the test data shows that BP neural network optimized by GA algorithm can achieve higher accuracy than BP neural network.