Faculty Publications
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Item Comparison of Response Surface Methodology (RSM) and Machine Learning Algorithms in Predicting Tensile Strength and Surface Roughness of AA8090/B4C Surface Composites Fabricated by Friction Stir Processing(Springer Science and Business Media Deutschland GmbH, 2024) Adiga, K.; Herbert, M.A.; Rao, S.S.; Shettigar, A.K.; Shrivathsa, T.V.; Tapariya, R.Friction stir processing is an innovative solid-state process, widely utilized for surface composite fabrication, material property enhancement, and microstructural modification. Rotational speed, traverse speed, groove width, and axial force are key FSP parameters that improve the characteristics of surface composites (SCs). This work makes use of FSP to fabricate AA8090/B4C SCs by altering parameters within ranges. Response variables include ultimate tensile strength (UTS) and surface roughness (SR). Central composite design (CCD) of response surface methodology (RSM) leads trials, establishing a mathematical relationship between input parameters and UTS/SR. The models’ adequacy is validated using ANOVA, which investigates the impact of input parameters on UTS and SR. This study also looks into machine learning regression methodologies for UTS and SR forecasting in AA8090/B4C SCs. The ML algorithms are evaluated by utilizing performance metrics like coefficient of determination (R2) and root mean squared error (RMSE). Predicted UTS and SR values from RSM are compared with machine learning outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.Item Application of particle swarm optimization and response surface methodology for machining parameters optimization of aluminium matrix composites in milling operation(Springer Verlag service@springer.de, 2017) Malghan, R.L.; Karthik, K.M.C.; Shettigar, A.K.; Rao, S.S.; D’Souza, R.J.Face milling is extensively used machining operation to generate the various components. Usually the selection of the process parameters are incorporated by trial and error method, literature survey and the machining hand book. This kind of selection of process parameters turns out to be very tedious and time-consuming. In order to overcome this there is a need to develop a technique that could be able to find the optimal process parameters for the desired responses in machining. The present paper illustrates an application of response surface methodology (RSM) and particle swarm optimization (PSO) technique for optimizing the process parameters of milling and provides a comparison study among desirability and PSO techniques. The experimental investigations are carried out on metal matrix composite material AA6061-4.5%Cu-5%SiCp to study the effect of process parameters such as feed rate, spindle speed and depth of cut on the cutting force, surface roughness and power consumption. The process parameters are analyzed using RSM central composite face-centered design to study the relationship between the input and output responses. The interaction between the process parameters was identified using the multiple regression technique, which showed that spindle speed has major contribution on all the responses followed by feed rate and depth of cut. It has shown good prediction for all the responses. The optimized process parameters are acquired through multi-response optimization using the desirability approach and the PSO technique. The results obtained from PSO are closer to the values of the desirability function approach and achieved significant improvement. © 2016, The Brazilian Society of Mechanical Sciences and Engineering.Item Artificial bee colony, genetic, back propagation and recurrent neural networks for developing intelligent system of turning process(Springer Nature, 2020) Shettigar, A.K.; Gowdru Chandrashekarappa, G.C.M.; Ganesh, G.R.; Vundavilli, P.R.; Parappagoudar, M.B.Intelligent manufacturing requires significant technological interventions to interface manufacturing processes with computational tools in order to dynamically mold the systems. In this era of the 4th industrial revolution, Artificial neural network (ANNs) is a modern tool equipped with a better learning capability (based on the past experience or history data) and assists in intelligent manufacturing. This research paper reports on ANNs based intelligent modelling of a turning process. The central composite design is used as a data-driven modelling tool and huge input–output is generated to train the neural networks. ANNs are trained with the data collected from the physics-based models by using back-propagation algorithm (BP), genetic algorithm (GA), artificial bee colony (ABC), and BP algorithm trained with self-feedback loop. The ANNs are trained and developed as both forward and reverse mapping models. Forward modelling aims at predicting a set of machining quality characteristics (i.e. surface roughness, cylindricity error, circularity error, and material removal rate) for the known combinations of cutting parameters (i.e. cutting speed, feed rate, depth of cut, and nose radius). Reverse modelling aims at predicting the cutting parameters for the desired machining quality characteristics. The parametric study has been conducted for all the developed neural networks (BPNN, GA-NN, RNN, ABC-NN) to optimize neural network parameters. The performance of neural network models has been tested with the help of ten test cases. The network predicted results are found in-line with the experimental values for both forward and reverse models. The neural network models namely, RNN and ABC-NN have shown better performance in forward and reverse modelling. The forward modelling results could help any novice user for off-line monitoring, that could predict the output without conducting the actual experiments. Reverse modelling prediction would help to dynamically adjust the cutting parameters in CNC machine to obtain the desired machining quality characteristics. © 2020, Springer Nature Switzerland AG.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 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.Item Control factor optimization for friction stir processing of AA8090/SiC surface composites(Elsevier B.V., 2024) Adiga, K.; Herbert, M.A.; Rao, S.S.; Shettigar, A.K.Friction Stir Processing is a state-of-the-art technology for microstructure refinement, material property enhancement, and surface composites fabrication. This investigation concentrates on AA8090/SiC surface composites produced via friction stir processing. Experiments were conducted by varying the following friction stir processing parameters: Tool rotational speed (800–1400 rpm), Tool traverse speed (25–75 mm/min), and Groove width (1.0–1.8 mm). Response measures encompassed Ultimate Tensile Strength and surface roughness. Central Composite Design of Response Surface Methodology designed the experiments and mathematical relationships established between input parameters and ultimate tensile strength and surface roughness. Analysis of variance was used to test the model's adequacy. The models examined individual and interaction effects of input factors on ultimate tensile strength and surface roughness of surface composites. A combinations of input parameters was identified that yields the maximum ultimate tensile strength and minimum surface roughness. The current work employs the friction stir processing approach to synthesis near-net-shaped surface composites without additional machining by systematically optimizing process parameters. Results indicate that increasing tool rotational speed produces well-finished AA8090/SiC surface composites with decreased strength. In contrast, increased tool traverse speed and groove width generate surface composites with rougher surfaces and higher strength. Surface and contour plots further explored the influence of parameter interactions on responses. © 2024 The AuthorsItem Experimental investigations on the milling characteristics of Cu alloys and additively manufactured CuCrZr(Institute for Problems in Mechanical Engineering, Russian Academy of Sciences, 2025) Mundla, S.R.; Vendan, S.A.; Paul, C.P.; Shettigar, A.K.; Jambagi, S.C.Copper is one of the widely used materials in various fields such as automotive, electronics, aviation, etc. The inherent property of copper makes it useful in wide variety of applications. The features on different components using the copper material can be made using different manufacturing techniques. However, post processing is one of the inevitable steps in any manufacturing process. Machining is one of the widely used post processing. There are multiple varieties of milling process. Among them, end milling process is widely used for making the slots. Three important process parameters in end milling process are depth of cut, cutting rate and feed rate. In this experimental approach, the copper is subjected to end milling operation by varying the aforementioned input parameters. In this fast-moving world, any manufacturing industry aims to produce the features with good dimensional accuracy with minimal amount of tool wear. Hence, the output responses selected are surface roughness and the tool wear. This research investigates the machining behavior of pure copper (Cu) and additively produced CuCrZr alloys to assess how fabrication methods affect processability. Pure copper, recognized for exceptional thermal / electrical conductivity, is compared against additively manufactured CuCrZr, which retains copper’s advantages while offering improved strength and wear resistance through alloy composition. During the milling process the following parameters such surface quality, cutting forces, tool degradation, and removal rates are reviewed through proper analysis. Compared to commercial copper, CuCrZr is more difficult to machine because it requires precise control over machining parameters to attain superior surface quality during milling. It is found that the CS and FR parameters balance material removal rate while controlling surface quality in both materials. As–built CuCrZr finds demand in high–performance applications such as heat exchangers, rocket engine components, and electrical contacts wherein strength, excellent thermal conductivity and additive manufacturability are critical. © S.R. Mundla, S. Arungalai Vendan, P. Paul, A.K. Shettigar, S.C. Jambagi, 2025.