Experimental investigation and optimization of process parameters for through induction hardening using factorial design of experiments

  • MUZAMIL MUHAMMAD NED University of Engineering and Technology, Karachi, Pakistan
  • Mubashir Ali Siddiqui NED University of Engineering and Technology, Karachi, Pakistan
  • Samiuddin Muhammad NED University of Engineering and Technology, Karachi, Pakistan
Keywords: Induction Hardening, Analysis of Variance (ANOVA), Optical Microscopic Analysis, Mathematical Model, Deformation, Process Parameters, Factorial Experimental Design.


Induction hardening is a heat treating process that is used to selectively case hardens the surface of material providing improved material properties. In this paper, a novel methodology is introduced to optimize hardness in longitudinal & cross sectional directions over the entire part, instead of selective region, without deforming the surface. The experimental trials are conducted on portable induction hardening machine using completely randomizedfactorial design model to analyze significant factors. Analysis of variance (ANOVA) technique has been used to study the effect of factors (i.e., Power & Heating Time) & their interaction on Hardness. Optical microscopy has also been performed to find out the change in phases by varying the factors. A mathematical model relating hardness with power and heating time has been developed which can be used for response prediction.

Author Biographies

MUZAMIL MUHAMMAD, NED University of Engineering and Technology, Karachi, Pakistan
Assistant Professor, Mechanical Engineering Department.
Mubashir Ali Siddiqui, NED University of Engineering and Technology, Karachi, Pakistan
Professor and Chairman Mechanical Engineering Department
Samiuddin Muhammad, NED University of Engineering and Technology, Karachi, Pakistan
Lecturer, Metallurgical Engineering Department


Larson, B. E., Kusy, R. P., and Whitley, J. Q. (1987). Torsional elastic property measurements of selected orthodontic archwires. Clinical Materials, 2(3): 165-179.

Mühlbauer A. (2008). History of Induction Heating and Melting (Heat Processing ed.), Vulkan-Verlag GmbH, Essen.

Kochure, P. G., and Nandurkar, K. N. (2012), Mathematical modeling for selection of process parameters in induction hardening of EN8 D steel. IOSR Journal of Mechanical and Civil Engineering, 1(2): 28-32.

Kohli, A., and Singh, H. (2010), Optimizing mean effective case depth of induction hardened parts (rolled condition) using response surface methodology. International Journal on Emerging Technologies, 1(1): 87-91.

Kohli, A., and Singh, H. (2012), Modeling and microstructural analysis of induction hardened parts. Materials and Manufacturing Processes, 27(3): 278-283.

Palanivasan, R., and Warkhedkar, R.M. (2010), Optimizing influence of process parameters on induction hardening for IC engine valve, Indian Journal of Science and Technology, 3(7): 795-797.

Schwenk, M., Hoffmeister, J. and Schulze, V. (2013), Experimental Determination of Process Parameters and Material Data for Numerical Modeling of Induction Hardening. Journal of Materials Engineering and Performance, 22(7): 1861-1870.

Onan, M., Baynal, K., Ünal, H. İ., and Katre, F. (2012), Optimization of Induction Hardened AISI 1040 Steel by Experimental Design Method and Material Characterization Analysis, Proceedings of the ASME International Mechanical Engineering Congress & Exposition IMECE, Houston, Texas, USA.

Kayacan, M. C., and Çolak, O. (2004), A fuzzy approach for induction hardening parameters selection. Materials & Design, 25(2): 155-16.

Todic, A., Cikara, D., Todic, T., Cikara-Anic, D., and Minic, D. (2012), Influence of Chemical Composition on the Structure, Hardness, and Toughness of High Alloyed Cr-Mo-V Steel. Materials and Manufacturing Processes, 27(11): 1193-1197.

Montgomery, D. C., and Runger, G. C. (2010). Applied Statistics and Probability for Engineers (3rd ed.), John Wiley & Sons, New York.

Maji, K., and Pratihar, D. K. (2011), Modeling of electrical discharge machining process using conventional regression analysis and genetic algorithms. Journal of Materials Engineering and Performance, 20(7), 1121-1127.

Kehoe, S., Ardhaoui, M., and Stokes J. (2011), Design of experiments study of hydroxyapatite synthesis for orthopaedic application using fractional factorial design. Journal of Materials Engineering and Performance, 20(8): 1423-1437.

Amar k. De, Jhon G. Speer, and David K. M. (2003), Color Tint- Etching for multi-phase steel. Advanced Material and Processes, Colorado School of Mines Golden, Colorado.

Al-Momani, E. S., Mayyas, A. T., Rawabdeh, I., and Alqudah, R. (2012), Modeling Blanking Process Using Multiple Regression Analysis and Artificial Neural Networks. Journal of Materials Engineering and Performance, 21(8): 1611-1619.

Nguyen, T., Zhang, L. C., Sun, D. L., and Wu, Q. (2014), Characterizing the Mechanical Properties of the Hardened Layer Induced by Grinding-Hardening. Machining Science and Technology, 18(2): 277-298.

Manimaran, G., and Kumar, M. P. (2013), Multiresponse optimization of grinding AISI 316 stainless steel using grey relational analysis. Materials and Manufacturing Processes, 28(4): 418-423.

Desai, S., and Lovell, M. (2008). Statistical Optimization of Process Variables In A Continuous Inkjet Process–A Case Study. International Journal of Industrial Engineering: Theory, Applications and Practice, 15(1): 104-112.

Radhakrishnan, R., Ramasamy, R., and Muthukrishnan, N. (2011). Optimization of machining parameters for turning AL-SIC (10P) MMC using Taguchi Grey relational analysis. International Journal of Industrial Engineering: Theory, Applications and Practice, 18(11).

Mechanical Engineering (1)