Surface hardness improvement in high efficiency deep grinding process by optimization of operating parameters

Document Type: Research Paper


1 School of mechanical engineering, College of Engineering, University of Tehran, Tehran, Iran

2 Department of mechanical engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran


The grinding is one of the most important methods that directly affects tolerances in dimensions, quality and finished surface of products. One of the major problems in the material removal processes specially grinding is the heat generation during the process and the residual tensile stress in the surfaces of product. Therefore, optimization of High Efficiency Deep Grinding (HEDG) process is the main goal of this study to reduce the generated heat and residual tensile stress and increase strength and surface hardness of AISI1045 annealed steel. To this end, the effects of main parameters e.g. depth of cut, wheel speed, workpiece speed and cross feed on surface hardness has been investigated. The experimental results demonstrated the reduction in surface temperature and increase in hardness as optimum conditions are applied to the grinding process. Moreover, the experimental results were validated by comparing with other experimental results and analyzing of surface microhardness, surface temperature and normal and tangential forces.


Main Subjects

[1]           J. J. Martell, C. R. Liu, J. Shi, Experimental investigation on variation of machined residual stresses by turning and grinding of hardened AISI 1053 steel, The International Journal of Advanced Manufacturing Technology, Vol. 74, No. 9-12, pp. 1381-1392, 2014.

[2]           O. Fergani, Y. Shao, I. Lazoglu, S. Y. Liang, Temperature effects on grinding residual stress, Procedia CIRP, Vol. 14, pp. 2-6, 2014.

[3]           Y. Deng, S. Xiu, Research on microstructure evolution of austenitization in grinding hardening by cellular automata simulation and experiment, The International Journal of Advanced Manufacturing Technology, pp. 1-14, 2017.

[4]           C. Yao, T. Wang, J. Ren, W. Xiao, A comparative study of residual stress and affected layer in Aermet100 steel grinding with alumina and cBN wheels, The International Journal of Advanced Manufacturing Technology, Vol. 74, No. 1-4, pp. 125-137, 2014.

[5]           Y. Xiao, A. Konak, A simulating annealing algorithm to solve the green vehicle routing & scheduling problem with hierarchical objectives and weighted tardiness, Applied Soft Computing, Vol. 34, pp. 372-388, 2015.

[6]           C. A. Floudas, P. M. Pardalos, 2014, Recent advances in global optimization, princeton University press,

[7]           S.-m. Ahn, S.-Y. Park, Y.-C. Kim, K.-S. Lee, J.-Y. Kim, Surface residual stress in soda-lime glass evaluated using instrumented spherical indentation testing, Journal of materials science, Vol. 50, No. 23, pp. 7752-7759, 2015.

[8]           K. Deb, Multi-objective optimization,  in: Search methodologies, Eds., pp. 403-449: Springer, 2014.

[9]           M. Azadi Moghaddam, F. Kolahan, An empirical study on statistical analysis and optimization of EDM process parameters for inconel 718 super alloy using D-optimal approach and genetic algorithm, Journal of Computational Applied Mechanics, Vol. 46, No. 2, pp. 267-277, 2015.

[10]         K. Salonitis, A. Kolios, Experimental and numerical study of grind-hardening-induced residual stresses on AISI 1045 Steel, International Journal of Advanced Manufacturing Technology, Vol. 79, 2015.

[11]         A. D. Batako, M. Morgan, B. W. Rowe, High efficiency deep grinding with very high removal rates, The International Journal of Advanced Manufacturing Technology, Vol. 66, No. 9-12, pp. 1367-1377, 2013.

[12]         W. B. Rowe, 2013, Principles of modern grinding technology, William Andrew,

[13]         G. Khalaj, H. Yoozbashizadeh, A. Khodabandeh, A. Nazari, Artificial neural network to predict the effect of heat treatments on Vickers microhardness of low-carbon Nb microalloyed steels, Neural Computing and Applications, Vol. 22, No. 5, pp. 879-888, 2013.

[14]         A. Bayat, R. Moharami, Numerical Analysis of explosion effects on the redistribution of residual stresses in the underwater welded pipe, Journal of Computational Applied Mechanics, Vol. 47, No. 1, pp. 121-128, 2016.

[15]         Y. Zhang, X. J. Meng, Z. J. Yuan, Study of Grinding Hardening Force Based on the Influence of Grinding Arc Temperature and Workpiece Deformation, in Proceeding of, Trans Tech Publ, pp. 98-103.

[16]         Y. Shao, O. Fergani, Z. Ding, B. Li, S. Y. Liang, Experimental investigation of residual stress in minimum quantity lubrication grinding of AISI 1018 steel, Journal of Manufacturing Science and Engineering, Vol. 138, No. 1, pp. 011009, 2016.

[17]         M. Hamedi, H. Eisazadeh, Numerical Simulation of Nugget Geometry and Temperature Distribution in Resistance Spot Welding, Journal of Computational Applied Mechanics, Vol. 46, No. 1, pp. 13-19, 2015.

[18]         K. Salonitis, On surface grind hardening induced residual stresses, Procedia CIRP, Vol. 13, pp. 264-269, 2014.

[19]         U. Alonso, N. Ortega, J. A. Sanchez, I. Pombo, S. Plaza, B. Izquierdo, In-process prediction of the hardened layer in cylindrical traverse grind-hardening, The International Journal of Advanced Manufacturing Technology, Vol. 71, No. 1-4, pp. 101-108, 2014.

[20]         U. Alonso, N. Ortega, J. Sanchez, I. Pombo, B. Izquierdo, S. Plaza, Hardness control of grind-hardening and finishing grinding by means of area-based specific energy, International Journal of Machine Tools and Manufacture, Vol. 88, pp. 24-33, 2015. 

Volume 49, Issue 1
June 2018
Pages 171-178
  • Receive Date: 01 August 2017
  • Revise Date: 28 August 2017
  • Accept Date: 17 October 2017