The unequal distribution of residual stress caused by incorrect machining processes will probably distort the machined connecting rods of the diesel engines. Thus, the influence of the machining processes on the residual stress and distortion of the rod need to be studied. Firstly, the finite element model is established based on the geometry of the connecting rod. Based on the life and death unit method, the influence of material removal on the residual stress of the connecting rod is discussed. Secondly, an orthogonal test is designed. The variables of the test include the quenching temperature, tempering temperature, cooling speed and the sequence of the machining processes. The deformation of the connecting rod is analyzed and then predicted based on the generalized regression neural network. The results show that the minimum distortion is achieved when the quenching temperature is 860 °C, the tempering temperature is 620 °C, the cooling speed is 50 °C h−1, and the machining sequence is “drilling the small end hole-milling, the upper planes-sawing, the connecting rod-turning, and the big end hole”. The priority of the influence of the processes on the deformation of the connecting rod is ranked as follows: cooling speed, quenching temperature, tempering temperature, and process sequence. The tensile residual stress is produced inside the connecting rod by machining processes. The generalized regression neural network predicts the connecting rod deformation accurately and the average error is 8.1%.
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Ashtiani HRR, Shahsavari P (2016) A comparative study on the phenomenological and artificial neural network models to predict hot deformation behavior of AlCuMgPb alloy. J Alloy Compd 687:263–273
Cerutti X, Mocellin K (2016) Influence of the machining sequence on the residual stress redistribution and machining quality: analysis and improvement using numerical simulations. Int J Adv Manuf Technol 83(1–4):489–503
Dean D (2009) FEM prediction of welding residual stress and distortion in carbon steel considering phase transformation effects. Mater Des 30(2):359–366
Denkena B (2008) Milling induced residual stresses in structural parts out of forged aluminum alloys. Int J Mach Mach Mater 4(4):335–344
Erik RD, Jarred CH, Panagiotis M (2015) Residual stress and distortion modeling of electron beam direct manufacturing Ti–6Al–4V. Proc Inst Mech Eng Part B J Eng Manuf 229(10):1803–1813
Jiang X, Wang Y, Ding Z (2017) An approach to predict the distortion of thin-walled parts affected by residual stress during the milling process. Int J Adv Manuf Technol 93:1–14
Li HY, Wei DD, Li YH (2012) Application of artificial neural network and constitutive equations to describe the hot compressive behavior of 28CrMnMoV steel. Mater Des 35(2):557–562
Liam G, Yi L, Neil P, Malcolm S (2010) Effect of welding sequence on residual stress and distortion in flat-bar stiffened plates. Mar Struct 23(3):385–404
Masoudi S, Amini S, Saeidi E (2015) Effect of machining-induced residual stress on the distortion of thin-walled parts. Int J Adv Manuf Technol 76:597–608
Mirzaee-Sisan A, Bastola A (2017) Redistribution of welding residual stress following high plastic deformation in seamless pipes. Int J Press Vessels Pip 158:37–50
Moraitis GA, Labeas GN (2008) Residual stress and distortion calculation of laser beam welding for aluminum lap joints. J Mater Process Technol 198(1–3):260–269
Nik M, Ahmed AD, Sarhan Mohsen ANH, Mohd H (2016) Optimization of cutting conditions for minimum residual stress, cutting force and surface roughness in end milling of S50C medium carbon steel. Measurement 86:253–265
Rai JK, Xirouchakis P (2008) Finite element method based machining simulation environment for analyzing part errors induced during milling of thin-walled components. Int J Mach Tools Manuf 48(6):629–643
Sim WM (2008) Challenges of residual stress and part distortion in the civil airframe industry. In Proceedings of the 2nd International Conference on Distortion Engineering, Bremen, Germany
Singh A, Agrawal A (2015) Investigation of surface residual stress distribution in deformation machining process for aluminum alloy. J Mater Process Technol 225:195–202
Wan XH, Dong ZW, Wang B (2016) The integrated frame structure type influencing on machining deformation simulation and experiment research. Mach Des Manuf 07:107–109
Wang SP, Padmanaban S (2004) A new approach for FEM simulation of NC machining processes. In: AIP conference proceedings, pp 1371–1376
Wang ZH, Yuan JT, Liu TT, Xia LL (2012) Machining deformation in birth–death element for thin-walled workpiece. J Harbin Univ Sci Technol 06:81–88
Wei Y, Wang XW (2007) Computer simulation and experimental study of machining deflection due to original residual stress of aerospace thin-walled parts. Int J Adv Manuf Technol 33(3–4):260–265
Yang Y, Li M, Li KR (2014) Comparison and analysis of main effect elements of machining distortion for aluminum alloy and titanium alloy aircraft monolithic component. Int J Adv Manuf Technol 70(90):1803–1811
This work was supported by the National Natural Science Foundation of China (No. 51605207) and the Natural Science Foundation of Jiangsu Province of China (No. BK20160563).
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Zhou, H., Liu, S., Li, G. et al. Machining Stress Analysis and Deformation Prediction of Connecting Rod Based on FEM and GRNN. Iran J Sci Technol Trans Mech Eng 44, 183–192 (2020). https://doi.org/10.1007/s40997-018-0256-8
- Orthogonal test
- Connecting rod
- Residual stress