A neural network-based method for coverage measurement of shot-peened panels

Shahid, Lubna; Janabi–Sharifi, Farrokh

doi:10.1007/s00521-017-3339-3

Cited by 6 publications

(2 citation statements)

References 4 publications

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“…Based on this, Holdgate [21] developed another model that extends the existing model to describe the growth pattern of coverage involving multiple peening sources. Shahid et al [22] adopted an artificial neural network-based method to improve the accuracy of coverage recognition results. The experiments showed that this method was superior to previous standard image segmentation methods.…”

Section: Introductionmentioning

confidence: 99%

Analytical model for predicting coverage in suspension waterjet shot peening and its influence on fatigue

Sun

et al. 2023

Mater. Res. Express

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Suspension waterjet peening is an emerging technology for surface modification. Coverage is a key factor affecting the integrity of a modified material surface, however such an experimental method that can be utilized for precise control has not yet been established. To determine the numerical value of coverage after surface peening treatment, In this paper, a coverage analytical method was proposed and then verified by the results through experiments. Furthermore, to explore the impact of high coverage on surface integrity, a large-scale coverage peening modification was performed on 18CrNiMo7-6 carburized steel specimens using a specialized suspension waterjet equipment. The results indicate that coverage has a significant impact on roughness and compressive residual stress field, with the highest improvement on surface and their maximum values reaching 51.6% and 24.7%, respectively. It is shown that the fatigue performance of the specimens can be significantly enhanced.

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Section: Introductionmentioning

confidence: 99%

Analytical model for predicting coverage in suspension waterjet shot peening and its influence on fatigue

Sun

et al. 2023

Mater. Res. Express

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“…On the other hand, artificial intelligence (AI) based methods such as neural networks (NN) are remarkably applied in different aspects of science and engineering [26][27][28][29], as well as their applications in fatigue behavior prediction and analysis [30][31][32][33][34][35] and modelling of SP process [28,30,[36][37][38]. In general, a neural network has three major layers of input, hidden and output [39].…”

Section: Introductionmentioning

confidence: 99%

Analysing the Fatigue Behaviour and Residual Stress Relaxation of Gradient Nano-Structured 316L Steel Subjected to the Shot Peening via Deep Learning Approach

et al. 2021

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In this study, the effect of kinetic energy of the shot peening process on microstructure, mechanical properties, residual stress, fatigue behavior and residual stress relaxation under fatigue loading of AISI 316L stainless steel were investigated to figure out the mechanisms of fatigue crack initiation and failure. Varieties of experiments were applied to obtain the results including microstructural observations, measurements of hardness, roughness, induced residual stress and residual stress relaxation as well as axial fatigue test. Then deep learning approach through neural networks was used for modelling of mechanical properties and fatigue behavior of shot peened material. Comprehensive parametric analyses were performed to survey the effects of different key parameters. Afterward, according to the results of neural network analysis, further experiments were performed to optimize and experimentally validate the desirable parameters. Based on the obtained results the favorable range of shot peening coverage regarding improved mechanical properties and fatigue behavior was identified as no more than 1750% considering Almen intensity of 21 A (0.001 inch). Graphic abstract

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Prediction of residual stress, surface roughness, and grain refinement of 42CrMo steel subjected to shot peening by combining finite element method and artificial neural network

Huang,

Wang,

Wang

et al. 2023

Int J Adv Manuf Technol

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A neural network-based method for coverage measurement of shot-peened panels

Cited by 6 publications

References 4 publications

Analytical model for predicting coverage in suspension waterjet shot peening and its influence on fatigue

Analytical model for predicting coverage in suspension waterjet shot peening and its influence on fatigue

Analysing the Fatigue Behaviour and Residual Stress Relaxation of Gradient Nano-Structured 316L Steel Subjected to the Shot Peening via Deep Learning Approach

Prediction of residual stress, surface roughness, and grain refinement of 42CrMo steel subjected to shot peening by combining finite element method and artificial neural network

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