Faculty Publications
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Item Soft computing techniques during drilling of bi-directional carbon fiber reinforced composite(Elsevier Ltd, 2016) Shetty, N.; Herbert, M.A.; Shetty, R.; Shetty, D.S.; Vijay, G.S.Due to the intricacy of machining processes and inconsistency in material properties, analytical models are often unable to describe the mechanics of machining of carbon fiber reinforced polymer (CFRP) composites. Recently, soft computing techniques are used as alternate modeling and analyzing methods, which are usually robust and capable of yielding comprehensive, precise, and unswerving solutions. In this paper, drilling experiments as per the Taguchi L27 experimental layout are carried out on bi-directional carbon fiber reinforced polymer (BD CFRP) composite laminates using three types of drilling tools: high speed steel (HSS), uncoated solid carbide (USC) and titanium nitride coated SC (TiN-SC). The focus of this work is to determine the best drilling tool that produces good quality drilled holes in BD CFRP composite laminates. This paper proposes a novel prediction model 'genetic algorithm optimised multi-layer perceptron neural network' (GA-MLPNN) in which genetic algorithm (GA) is integrated with Multi-Layer Perceptron Neural Network. The performance capability of response surface methodology (RSM) and GA-MLPNN in prediction of thrust force is investigated. RSM is also used to evaluate the influence of process parameters (spindle speed, feed rate, point angle and drill diameter) on thrust force. GA is used to optimize the thrust force and its optimization performance is compared with that of RSM. It is observed that the GA-MLPNN is better predicting tool than the RSM model. The investigation in this paper demonstrates that TiN-SC is the best tool for drilling BD CFRP composite laminates as minimum thrust force is developed during its use. © 2016 Elsevier B.V. All rights reserved.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.Item Parameter investigation and optimization of friction stir welded AA6061/TiO2 composites through TLBO(Springer Science and Business Media Deutschland GmbH, 2022) Prabhu B, S.R.; Shettigar, A.; Herbert, M.A.; Rao, S.S.This paper explicates the joining of AA 6061/TiO2 composites by the friction stir welding (FSW) process. FSW experiments were conducted as per the three factors, three-level, central composite ivy– face-centered design method. Mathematical relationships between the FSW process parameters, namely tool geometry, welding speed, and tool rotational speed, and the output responses such as hardness, yield strength, and ultimate tensile strength were established using response surface methodology. Adequacies of established models were assessed through the analysis of variance method. Further, the paper elucidates the application of the teaching–learning-based optimization (TLBO) algorithm to identify the optimal values of input variables and to obtain an FSW joint with superior mechanical properties. The optimized experimental condition obtained from the TLBO yields an FSW joint with a UTS of 174 MPa, yield strength of 120 MPa, and hardness of 126HV. The study revealed that the result of the TLBO algorithm matched the findings of the FSW experiments. © 2021, The Author(s).Item Optimization of process parameters for friction stir processing (FSP) of AA8090/boron carbide surface composites(Springer Science and Business Media Deutschland GmbH, 2024) Adiga, K.; Herbert, M.A.; Rao, S.S.; Shettigar, A.K.Friction Stir Processing (FSP) is an innovative and promising technique for microstructure refinement, material property enhancement, and surface composite production. The current study describes the fabrication of AA8090/boron carbide surface composites (SCs) by FSP. Experimental studies were conducted by varying the FSP parameters, specifically the rotational speed (800–1400 rpm), traverse speed (25–75 mm/min), and groove width (1–1.8 mm). Ultimate Tensile Strength (UTS), Surface Roughness (SR), and Percentage Elongation (El) were used as response measures. Experiments were planned based on the central composite design (CCD) of Response Surface Methodology (RSM) and a mathematical relationship between the input parameters and UTS, SR and El, and were obtained by RSM. The model adequacy was tested using analysis of variance (ANOVA). The models enabled the examination of individual and interaction effects of input parameters on the UTS, SR, and El of the produced SCs. AA8090/boron carbide SC strength was optimal of 366 MPa at 800 rpm, 75 mm/min, and 1.8 mm and optimal 21.13% elongation at 1400 rpm, 25 mm/min, and 1 mm. A smoother surface with 0.82-μm roughness was optimal at 1400 rpm, 25 mm/min, and 1.2 mm. The present study uses the FSP method to synthesize near-net-shaped SCs without further machining by systematically selecting process parameters. The study shows that the increase in rotational speed during AA8090/boron carbide SC fabrication produces composites with a good surface finish, lower UTS, and good ductility. However, the increase in the other two parameters, namely, traverse speed and groove width, produces low ductile composites with rougher surfaces and higher strengths. Graphical abstract: (Figure presented.) © International Institute of Welding 2024.