Optimization of shot peening parameters by response surface methodology

被引:81
|
作者
Unal, Okan [1 ]
机构
[1] Karabuk Univ, Dept Mech Engn, TR-78050 Karabuk, Turkey
来源
关键词
Response surface methodology; Almen intensity; Shot peening; ANOVA; Regression; FINITE-ELEMENT SIMULATION; LOW-CARBON STEEL; RESIDUAL-STRESSES; FATIGUE BEHAVIORS; LAYER PROPERTIES; ALMEN INTENSITY; MICROSTRUCTURE; ROUGHNESS; WEAR;
D O I
10.1016/j.surfcoat.2016.08.004
中图分类号
TB3 [工程材料学];
学科分类号
0805 ; 080502 ;
摘要
In this study, the shot peening parameters which directly influence the arc height of the Almen strip and its characteristics are optimized within the context of Almen intensity, surface roughness and surface hardness via response surface methodology. Determination of the Almen intensity by trial and error method depending on the experience of the technician (measuring the arc height of Almen strips by changing the parameters repeatedly for each shot peening process) makes the optimization approaches valuable. The optimization is considered to perform by selecting surface roughness and surface hardness as the responses in order to classify the shot peening processes by taking into consideration of wide range of plastic deformation level. The effect of input parameters air pressure, shot diameter and peening duration on the Almen intensity, surface roughness and surface hardness is to be determined by using ANOVA regression analysis. Based on the estimated models, optimum peening conditions are introduced via response optimizer. The model adequacy is verified by the confirmation tests. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码:99 / 109
页数:11
相关论文
共 50 条
  • [1] Combining the finite element method and response surface methodology for optimization of shot peening parameters
    Wang, Xiaoli
    Wang, Zhou
    Wu, Gang
    Gan, Jin
    Yang, Ying
    Huang, Haiming
    He, Jiaxi
    Zhong, Hanlie
    [J]. INTERNATIONAL JOURNAL OF FATIGUE, 2019, 129
  • [2] The Numerical Optimization of Laser Shot Peening Parameters Based on Response Surface Analysis
    Jiang, Suqin
    Wu, Jianhua
    Xu, Hongguang
    Zhou, Jianzhong
    [J]. FUNCTIONAL MANUFACTURING TECHNOLOGIES AND CEEUSRO II, 2011, 464 : 443 - +
  • [3] Use of response surface methodology for shot peening process optimization of an aircraft structural part
    Yong-Seog Nam
    Ung Jeon
    Hee-Kweon Yoon
    Byung-Cheol Shin
    Jai-Hyun Byun
    [J]. The International Journal of Advanced Manufacturing Technology, 2016, 87 : 2967 - 2981
  • [4] Use of response surface methodology for shot peening process optimization of an aircraft structural part
    Nam, Yong-Seog
    Jeon, Ung
    Yoon, Hee-Kweon
    Shin, Byung-Cheol
    Byun, Jai-Hyun
    [J]. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2016, 87 (9-12): : 2967 - 2981
  • [5] Optimization of shot peening parameters for AA7B50-T7751 using response surface methodology
    Li, Bin
    Qin, Zhi
    Xue, Hongqian
    Sun, Zhidan
    Gao, Tao
    [J]. SIMULATION MODELLING PRACTICE AND THEORY, 2022, 115
  • [6] Optimization of Shot Peening Effective Parameters on Surface Hardness Improvement
    Maleki, Erfan
    Unal, Okan
    [J]. METALS AND MATERIALS INTERNATIONAL, 2021, 27 (09) : 3173 - 3185
  • [7] Optimization of Shot Peening Effective Parameters on Surface Hardness Improvement
    Erfan Maleki
    Okan Unal
    [J]. Metals and Materials International, 2021, 27 : 3173 - 3185
  • [8] Optimization of parameters for the best shot peening effect based on surface response and neural network model
    Wang, Chengan
    Kim, Taehyung
    [J]. MATERIALS RESEARCH EXPRESS, 2024, 11 (01)
  • [9] Effect of Optimization of Shot Peening Parameters on Surface Integrity of NAK80
    Miao, H.
    Zuo, D. W.
    Wang, H. F.
    Sha, X. W.
    [J]. FUNCTIONAL MANUFACTURING TECHNOLOGIES AND CEEUSRO I, 2010, 426-427 : 537 - 539
  • [10] Numerical Simulation and Optimization of Shot Peening Process Parameters
    Zhu, Ren-Sheng
    Zhao, Hong-Ling
    Zhao, Han
    Zhang, Yue
    Peng, Ji-You
    [J]. JOURNAL OF THE CHINESE SOCIETY OF MECHANICAL ENGINEERS, 2018, 39 (02): : 163 - 170