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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2021.9
- 수록면
- 3,173 - 3,185 (13page)
- DOI
- 10.1007/s12540-020-00758-x
이용수
초록· 키워드
오류제보하기
Shot peening is well-known process for mechanical properties integrity in metallic materials. In present study influencesof different shot peening treatments on the surface hardness of different carbon steels were investigated experimentally andthen alternative approach by using artificial neural network is presented for hardness prediction of the shot peened surface.
After modeling a comprehensive parametric investigations and sensitivity analysis were applied according to the influenceof the related effective parameters on surface hardness improvements.
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등록된 정보가 없습니다.
참고문헌 (6)
참고문헌 신청
Erfan Maleki / 2019 / Efficiency Analysis of Shot Peening Parameters on Variations of Hardness, Grain Size and Residual Stress via Taguchi Approach / Metals and Materials International 25(6):1436~1447
Mehrshad Mehrpouya / 2019 / An artificial neural network model for laser transmission welding of biodegradable polyethylene terephthalate/polyethylene vinyl acetate (PET/PEVA) blends / The International Journal of Advanced Manufacturing Technology 102(5-8):1497~1507
T. Y. Wu / 2019 / Prediction of surface roughness in milling process using vibration signal analysis and artificial neural network / The International Journal of Advanced Manufacturing Technology 102(1-4):305~314
Erfan Maleki / 2021 / Fatigue limit prediction and analysis of nano-structured AISI 304 steel by severe shot peening via ANN / Engineering with Computers 37(4):2663~2678
E. Maleki / 2021 / Shot Peening Process Effects on Metallurgical and Mechanical Properties of 316 L Steel via: Experimental and Neural Network Modeling / Metals and Materials International 27(2):262~276
Mehrshad Mehrpouya / 2019 / An artificial neural network model for laser transmission welding of biodegradable polyethylene terephthalate/polyethylene vinyl acetate (PET/PEVA) blends / The International Journal of Advanced Manufacturing Technology 102(5-8):1497~1507
T. Y. Wu / 2019 / Prediction of surface roughness in milling process using vibration signal analysis and artificial neural network / The International Journal of Advanced Manufacturing Technology 102(1-4):305~314
Erfan Maleki / 2021 / Fatigue limit prediction and analysis of nano-structured AISI 304 steel by severe shot peening via ANN / Engineering with Computers 37(4):2663~2678
E. Maleki / 2021 / Shot Peening Process Effects on Metallurgical and Mechanical Properties of 316 L Steel via: Experimental and Neural Network Modeling / Metals and Materials International 27(2):262~276
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