The Prediction of Shot Peening’s Surface Roughness with Premixed Water Jet Based on Neural Network

Rui Hong Wang; Chong Wang; Xiao Mei Zhang

doi:10.4028/www.scientific.net/AMR.136.172

Paper Titles

Development of a Ring Permeability Test System
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The Variable Non-Linear Flow Channel Method and Device
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Research on Bearing Life Prediction Based on Three Parameters Weibull Distribution
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The Study of Target Points Center Extracting and Matching in Calibration
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The Prediction of Shot Peening’s Surface Roughness with Premixed Water Jet Based on Neural Network
p.172

Research on Safety Protection Technology of Conventional Lathe CA6140
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Preparation and Properties of Waterborne Polyurethane with PDMS Side Chain
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Investigation of the Drawing Process of a Square Rod from a Round Bar by Using 3-D Finite Element Analysis
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Finite Element Numerical Simulation of EDM Surface Strengthening in Gas Medium of Stainless Steel 1Cr13
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The Prediction of Shot Peening’s Surface Roughness with Premixed Water Jet Based on Neural Network

Abstract:

Shot peening’s surface roughness is an important factor affecting the effect of shot-peening. The paper selects blasting pressure, scanning speed and target distance as affecting factors in the process parameters, the shot penning test which aims at 2A11 aluminum alloy materials through applying the premixed water jet, according to test data, the paper establishes mathematical model of shot peening’s surface roughness applying neural network, and applies this model to predict shot peening’s surface roughness. The results show that the training average error of this model is small, the predicted effect is good, it can meet the requirements of shot peening’s surface roughness prediction accuracy in the industrial production, it has greater practical value.