Residual Stresses during Hard Turning of AISI 52100 Steel: Numerical Modelling with Experimental Validation

被引:2
|
作者
Pawar, Sujit [1 ]
Salve, Aniket [1 ]
Chinchanikar, Satish [1 ]
Kulkarni, Atul [1 ]
Lamdhade, Ganesh [1 ]
机构
[1] VIIT Pune, Mech Engn Dept, Pune 411048, Maharashtra, India
关键词
ABAQUS Explicit; Numerical modelling; Residual stresses; X-ray Diffraction;
D O I
暂无
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Residual stresses induced by machining processes are a consequence of thermo-mechanical and microstructural phenomena generated during the machining operation. In this study, a numerical approach has been developed to predict the near surface residual stresses resulting from turning of AISI 52100 alloys steel and validated by experimental results. Effect of cutting parameters, namely cutting speed and depth of cut on induced residual stresses in machined surface was investigated by modelling using ABAQUS/CAE 14.0 software. Explicit Dynamics time integration with adaptive meshing finite element method is employed to simulate the 2D model. The Johnson-Cook material model is used to describe the work material behaviour to simulate high speed machining with an orthogonal cutting. And also for experimental analysis X-Ray diffraction method is considered. While study residual stresses at different cutting speed, feed rate, and depth of cut is studied. As a conclusion we can say that cutting speed and feed rate plays major role as compared to depth of cut. And as cutting speed or feed rate increases tensile residual stresses increases. (C) 2017 Elsevier Ltd. All rights reserved.
引用
收藏
页码:2350 / 2359
页数:10
相关论文
共 50 条
  • [21] Experimental investigation on CBN turning of hardened AISI 52100 steel
    Chou, YK
    Evans, CJ
    Barash, MM
    JOURNAL OF MATERIALS PROCESSING TECHNOLOGY, 2002, 124 (03) : 274 - 283
  • [22] SURFACE AND SUBSURFACE ALTERATIONS INDUCED BY HARD TURNING OF AISI 52100 BEARING STEEL
    Trindade, Saulo P.
    Silva, Klaus H. S.
    Oliveira, Diogo A.
    Abrao, Alexandre M.
    JOURNAL OF ENGINEERING SCIENCE AND TECHNOLOGY, 2018, 13 (09) : 2765 - 2778
  • [23] Formation mechanisms of white layers induced by hard turning of AISI 52100 steel
    Hosseini, S. B.
    Klement, U.
    Yao, Y.
    Ryttberg, K.
    ACTA MATERIALIA, 2015, 89 : 258 - 267
  • [24] Experimental and numerical modelling of the residual stresses induced in orthogonal cutting of AISI 316L steel
    Outeiro, J. C.
    Umbrello, D.
    M'Saoubi, R.
    INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE, 2006, 46 (14): : 1786 - 1794
  • [25] A hybrid finite element method-artificial neural network approach for predicting residual stresses and the optimal cutting conditions during hard turning of AISI 52100 bearing steel
    Umbrello, D.
    Ambrogio, G.
    Filice, L.
    Shivpuri, R.
    MATERIALS & DESIGN, 2008, 29 (04): : 873 - 883
  • [26] Surface Integrity of AISI 52100 Steel during Hard Turning in Different Near-Dry Environments
    Chavan, Ajay
    Sargade, Vikas
    ADVANCES IN MATERIALS SCIENCE AND ENGINEERING, 2020, 2020 (2020)
  • [27] Application Of Regression And Artificial Neural Network Analysis In Modelling Of Surface Roughness In Hard Turning Of AISI 52100 Steel
    Paturi, Uma Maheshwera Reddy
    Devarasetti, Harish
    Narala, Suresh Kumar Reddy
    MATERIALS TODAY-PROCEEDINGS, 2018, 5 (02) : 4766 - 4777
  • [28] Modeling of Residual Stresses by Correlating Surface Topography in Machining of AISI 52100 Steel
    Liu, Chao
    He, Yan
    Li, Yufeng
    Wang, Yulin
    Wang, Shilong
    Wang, Yan
    JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME, 2022, 144 (05):
  • [29] Numerical modelling of turning to find residual stresses
    Stenberg, N.
    Proudian, J.
    14TH CIRP CONFERENCE ON MODELING OF MACHINING OPERATIONS (CIRP CMMO), 2013, 8 : 258 - 264
  • [30] Evaluation of Cutting Tool Vibration and Surface Roughness in Hard Turning of AISI 52100 Steel: An Experimental and ANN Approach
    Ambhore, Nitin
    Kamble, Dinesh
    Chinchanikar, Satish
    JOURNAL OF VIBRATION ENGINEERING & TECHNOLOGIES, 2020, 8 (03) : 455 - 462