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Sahay S.S.,John Deere Asia Technology Innovation Center | El-Zein M.,John Deere Moline Technology Innovation Center
Surface Engineering | Year: 2011

The potential of residual stress engineering for developing leaner, greener, and safer design is discussed. The technique can be successfully employed at the design stage in the transportation sector and creates significant value by reducing the product's weight, cost, and carbon footprint. It is possible to estimate the compressive residual stresses in the carburized automotive gears or transmission components through residual stress engineering. The superposition of the compressive residual stresses on the externally applied load will result in overall reduction in stresses. Incorporation of residual stresses at the design stage for carburized gears will result in considerable weight reduction for a specified performance. These weight reduction and cost savings opportunities can also be used to create a more effective business case for the new processes, such as low pressure carburizing and gas quenching.

Sahay S.S.,John Deere Asia Technology Innovation Center | Mohapatra C.,John Deere Asia Technology Innovation Center | Caster R.,John Deere Moline Technology Innovation Center | Cuthy H.,John Deere Coffeyville Works
Advanced Materials and Processes | Year: 2014

Modern industrial heat treating operations have sophisticated IT architectures, where significant amounts of data in GB/TB per year are generated including characteristics of input material, process parameters and product quality are generated. For example, many surprises about process and product can emerge from process analysis and modeling, where the insight generated from this approach could directly impact product quality and design recommendations. In contrast, physics-based models can also lead to an optimum operating condition, which is far removed from current conditions. Physics-based models for heat treating operations incorporate mass and energy conservation, laws of heat transfer, metallurgical thermodynamics, and chemical reactions and kinetics. Because of the non- isothermal effect, accelerated annealing kinetics with a reduction in heating rate was observed through physics-based modeling and laboratory kinetics experiments.

Deshmukh V.,John Deere Asia Technology Innovation Center | Sahay S.,John Deere Asia Technology Innovation Center | Agrawal B.,John Deere Technology Center India | Padhan U.,John Deere Technology Center India | El-Zein M.,John Deere Moline Technology Innovation Center
SAE Technical Papers | Year: 2011

Induction hardening is an important heat treatment operation for a number of components, including shafts, crank-shafts, gears, and axles to improve wear and fatigue properties. These parts are widely used in automotive as well as off-highway vehicles. Induction hardening process comprises of two distinct steps, induction heating and quenching operation. It involves phenomenologically many overlapping complex processes such as temperature evolution, phase transformation, microstructure evolution and structural changes. Therefore, it is important to understand and quantify the aforementioned processes to avoid the residual stress and distortion in the component resulting from induction hardening. In the present work, a two step simulation methodology has been developed by coupling two commercial FEA softwares, based on electromagnetic and heat treatment simulations, respectively. This methodology enables accurate prediction of the temperature profile in the component during induction heating as well as the changes in temperature, phase transformation, microstructure, residual stress and distortion during the subsequent quenching step. In the present work, the coupled simulation exercise was carried out on a simple geometry (solid cylinder) as well as a complex geometry (ring gear). Thus, the obtained simulation results were validated with the experimental data and found to be in good agreement. The present study shows that an integrated methodology of solving induction hardening gives an opportunity to include large number of process information and providing precise prediction and thereby enables opportunity for process and design optimization. This presentation would detail on the overall approach, validation results and optimization possibility. Copyright © 2011 SAE International.

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