Faculty Publications
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Item Advantages of cryogenic machining technique over without-coolant and with-coolant machining on SS316(IOP Publishing Ltd, 2021) Karthik, M.; Malghan, R.L.; Shettigar, A.K.; Herbert, M.A.; Rao, S.S.The analysis concentrated towards the influence of speed of the spindle along with a cryogenic (LN2) cooling technique in treating SS316 usingCNC(Computerized numerical control) milling machine. An comparative study path was set and anlyised among three states i.e. Dry (Without coolant), wet (With coolant) and cryogenic (With liquid LN2) machining using coated carbide inserts. The coolant used in case of wet machining was water-soluble, referred to as cutting fluid. The experimental range falls in 3 different levels of spindle speed (SS), such as low level (1000 rpm), medium level (2000 rpm), and high level (3000 rpm), respectively. Meanwhile, feed rate (FR) and depth of cut (DOC) were reserved steadily with 450 mm min-1, 1 mm separately. This vital focus is towards cryogenic (LN2) machining effects and its perception of machinability on SS316, such as tool wear -TW(?m), cutting force-CF (N), cutting temperature-CT (oC) and surface roughness-Ra (?m). The experiments were conducted and documented with cryogenic (LN2) techniques to establish the fairness and practicability of the method to compare with without-coolant (dry) and with-coolant (wet) machining. The attained statistical results in comparison of LN2 method over without-coolant and with-coolant machining concerned to test cases for CF- Fx (N), CT(oC), Ra (?m) andFW(?m) are 53.21%-34.20%, 65.88%-44.51%, 75.43%-44.27%,&59.76%-23.10%, respectively. © 2021 IOP Publishing Ltd.Item Application of back propagation algorithms in neural network based identification responses of AISI 316 face milling cryogenic machining technique(Taylor and Francis Ltd., 2022) Karthik, K.R.; Malghan, R.L.; Shettigar, A.; Rao, S.S.; Herbert, M.A.The paper explores the potential study of artificial neural network (ANN) for prediction of response surface roughness (Ra) in face milling operation with respect to cryogenic approach. The model of Ra was expressed as the main factor in face milling of spindle speed, feed rate, depth of cut and coolant type. The ANN is trained using four various back propagation algorithms (BPA). The emphasis of the paper is to investigate the performance and the accuracy of the attained results depicts the effectiveness of the trained ANN in identifying the predicted Ra. The incorporated various BPA in predicting the Ra. The performance comparative study is made among statistical (Response Surface Methodology (RSM)) and ANN (BPA–training algorithm) methods. The various incorporated BPA algorithms are Gradient Descent (GD), Scaled Conjugate Gradient Descent (SCGD), Levenberg Marquardt (LM) and Bayesian Neural Network (BNN). Afterwards the best suitable BPA is identified in predicting Ra for AISI 316 in face milling operation using liquid nitrogen (LN2) as cutting fluid. The outperformed BPA is identified based on the attained deviation percentage and time required for the training the network. © 2020 Engineers Australia.