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Author: search.filters.author.Prabhu, P.R.Subject: search.filters.subject.Cold rolling

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    Multi-response optimization of the turn-assisted deep cold rolling process parameters for enhanced surface characteristics and residual stress of AISI 4140 steel shafts
    (Elsevier Editora Ltda, 2020) Prabhu, P.R.; Kulkarni, S.M.; Sharma, S.
    Surface and near-surface areas play an important role as far as safety and dependability ofengineering components particularly when it is subjected to fatigue loading. By applyingdiverse mechanical surface enhancement (MSE) strategies, close to surface layers can becustom-made bringing about enhanced fatigue strength. MSE methods are used to gener-ate surface hardened components without the time and energy-consuming heat treatment.Deep cold rolling (DCR) is one such method that can be employed where the mechanicalenergy induced enables surface-hardening of steels and thereby the combination of hard-ening and finishing in one single step. The objective of this work is to enhance residualstress and near-surface properties of AISI 4140 steel which is the most commonly usedmaterial in the automobile and aerospace industry. The samples were first turned and thendeep cold rolled with various process parameters. Microstructure, surface hardness, sur-face finish, fatigue life, and residual compressive stress after the treatment were examined.Response surface methodology (RSM) and desirability function approach (DFA) was used torelate the empirical relationship between the various process variables and responses andalso to determine the optimum parameter settings for better responses. Further, numericalsimulation of turn-assisted deep cold rolling (TADCR) process was done by utilizing ANSYS-LS-DYNA software to understand the state of residual stress under various treating settings.Confirmation experiments conducted with the optimum parameter setting to validate theimprovements in response and it is found that the deviation between optimum predictedand confirmatory experimental values is about 5%. © 2020 The Authors.
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    Surface Properties and Corrosion Behavior of Turn-Assisted Deep-Cold-Rolled AISI 4140 Steel
    (Springer, 2020) Prabhu, P.R.; Prabhu, D.; Sharma, S.; Kulkarni, S.M.
    In this research, the effect of various turn-assisted deep-cold-rolling process parameters on the residual stress, microstructure, surface hardness, surface finish, and corrosion behavior of AISI 4140 steel has been investigated. The examination of the surface morphology of the turned and processed samples was performed by using a scanning electron microscope, energy-dispersive spectroscopy, and atomic force microscopy. Response surface methodology and desirability function approach were used for reducing the number of experiments and finding local optimized conditions for parameters under the study. The results from the residual stress measurements indicate that the rolling force has the highest effect by generating a deeper layer of residual compressive stress. The outcomes of surface hardness and surface finish emphasize that rolling force and number of tool passes are the most significant parameters affecting the responses. Surface studies confirmed the corrosion and its intensity onto the metal surface, and according to atomic force microscopy studies, the surface had become remarkably rough after exposure to the corrosive medium. Improvements in surface microhardness from 225 to 305.8 HV, the surface finish from 4.84 down to 0.261 ?m, and corrosion rate from 6.672 down to 3.516 mpy are observed for a specific set of parameters by turn-assisted deep-cold-rolling process. The multiresponse optimization for surface finish and corrosion rate together shows that a ball diameter of 10 mm, a rolling force of 325.75 N, initial roughness of 4.84 µm, and number of tool passes of 3 give better values for the two responses under consideration with composite desirability of 0.9939. Based on the experimental work at the optimum parameter setting, the absolute average error between the experimental and predicted values for the corrosion rate is calculated as 3.2%. © 2020, The Author(s).