BITS Faculty Publications

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Author: search.filters.author.Garg, Girish Kant

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    Non-negative matrix factorization combined with Fuzzy C-means enhanced k-nearest neighbor for fault detection and diagnosis in process industries
    (Elsevier, 2026-01) Mohanta, Hare Krishna; Garg, Girish Kant; Pani, Ajaya Kumar
    In the context of Industry 4.0, modern industrial processes generate high-dimensional, non-negative and potentially non-linear data streams, posing significant challenges for effective fault detection and diagnosis. Traditional statistical and multivariate techniques mostly assume restrictions such as a Gaussian distribution and linear relationships, which limit their use in real-world problems. This paper proposes a novel hybrid technique, Non-negative Matrix Factorization (NMF)–Enhanced Local Weighting Fuzzy C-Means (FCM) with Distance-Based k-Nearest Neighbors (NEFkNN), for fault detection. Initially, NMF is applied for dimensionality reduction. This is followed by FCM clustering, where cluster centers were refined with an enhanced local weighting (ELW) strategy. Detection threshold is determined by calculating the Euclidean distance between each sample and the enhanced cluster centers. A cluster-sensitive feature attribution method called Cluster-Aware Residual Contribution Analysis (CARCA) is proposed for fault diagnosis, which adjusts each feature's contribution to a fault by accounting for the local variance within its assigned cluster, enhancing interpretability. The NEFkNN technique was evaluated on two benchmark systems of a wastewater treatment plant(WWTP) and a continuous stirred tank reactor(CSTR) and achieved high fault detection rates and low false alarm rates. The diagnosis indicates that the fault is highly localized and attributable to a single process variable.
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    Eco-friendly tool-based electrochemical polishing of additively manufactured metallic components
    (Elsevier, 2023-12) Garg, Girish Kant
    The elevated surface roughness of metal additive manufacturing (MAM) parts detrimentally affects wear resistance, diminishes fatigue strength, and hampers cooling efficiency, which mandates post-processing. Electrochemical polishing is a non-contact and non-thermal post-processing technique but uses non-ecofriendly acidic baths and is ineffective in removing unmolten or partially molten metal particles. To address these challenges, we propose a novel approach using a flat nano-polished cylindrical tool as the cathode and an eco-friendly electrolyte for finishing a MAM component fabricated via atomic diffusion additive manufacturing (ADAM). Our study evaluates the effectiveness of this approach through numerical simulation and optimises the process through experimental analysis. The numerical simulation developed in COMSOL incorporates the surface roughness data of the ADAM part as the anode surface profile in the 2D simulation domain. The current density distribution, viscous layer formation, reduction in surface roughness and mass material removal rate (MRRg) are analysed from the simulation results. The experimental optimisation of parameters, including polishing time, inter-electrode gap (IEG), electrolyte flow, and electrolyte composition, resulted in a substantial decrease in the average surface roughness (Ra) value of the ADAM component by 95.91 %. The polished surface is more levelled, exhibiting a glossy finish with no waviness and fewer surface cracks. The EDS and XRD analysis portrayed the presence of passive films, indicating improved corrosion resistance. Repeating the experiment for rolled and milled surfaces with the same process parameters resulted in a similar reduction in the Ra value by 97.59 % and 94.73 %, respectively. The comparative analysis, of ECP on MAM, rolled, and milled surfaces, indicates the potential to achieve a similar notable improvement in surface finish, irrespective of the process history of the manufactured part for the same ECP parameters. Comparing the anode surface profiles, Ra values, and mass of material dissolved showed a close fit between experimental and simulation results. Our study highlights the feasibility of ECP with the flat tool electrode and eco-friendly electrolytes to reduce the surface roughness of an MAM component significantly.
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    Eco-friendly vibration-assisted electrochemical polishing of surfaces generated by wire electrical discharge machining
    (IOP, 2024) Garg, Girish Kant
    The study demonstrates an in-house developed eco-friendly vibration-assisted electrochemical polishing (ECP) process, where the electrolyte flushing with the squeezing action of the vibrating tool eliminates the electrolytic by-products in the inter-electrode gap (IEG). A two-dimensional numerical model is developed to study the squeezing effect on changing bubble faction, anodic dissolution, and current density distribution. The effect of process parameters such as current density, electrolyte flow velocity, IEG, vibration amplitude of the tool, and vibration speed is analysed based on the experimental design matrix of response surface methodology (RSM) for minimising average surface roughness (Ra) of SS 304 component fabricated by electrical discharge machining. The numerical results indicated an increased flow velocity at IEG due to the vibration, resulting in an effective flushing of generated gasses. Current, IEG, vibration speed of the tool, vibration amplitude, and interaction between current-IEG, current-vibration speed, and IEG-vibration speed are identified as the most influential parameters by implementing the analysis of variance. The parameters are optimised using RSM, leading to a 96.71% reduction in Ra value and a 62.54% lower Ra value than the ECP without vibration, indicating the effectiveness of vibration-assisted ECP to achieve a high surface finish using eco-friendly electrolytes
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    Localised electrochemical additive manufacturing via microjet electrochemical deposition
    (IOP, 2025) Garg, Girish Kant
    The current study presents a novel process developed in-house for microjet electrochemical additive manufacturing (Microjet-ECAM) of pure copper strips on a nano-polished brass substrate. A two-dimensional numerical model was developed to identify the stable material deposition zone, deposition height, potential, and current density distributions as the printing head traverses over the substrate. An experimental parametric study was conducted to assess the printability and the effect of parameters such as voltage, feed rate, and electrolyte flow rate. The parameters were varied at three levels, and linear features were printed layer-by-layer for 100 passes at each parameter combination according to the L27 orthogonal array. Numerical results estimated a deposition height of 72.2 μm and a steady material deposition of 1.33 to 4.33 mm along the substrate due to stray depositions at the initial and final microjet positions. The experimental results recorded voltage as the most influential parameter and flow and feed rates as the most interdependent during Microjet-ECAM. The microstructure of the material deposition showed a polycrystalline structure of the copper and a decrease in grain size on increasing feed and flow rates. A confined deposition having a refined grain structure with a deposition height of 70.4 μm was achieved.
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    An integrated modelling and optimization approach for the selection of process parameters for variable power consumption machining processes
    (Springer, 2023-08) Routroy, Srikanta; Garg, Girish Kant
    Manufacturing industries are under intense pressure to reduce the energy usage of the machining processes without sacrificing productivity, owing to the fast-rising worldwide market and environmental issues. Variable-power consumption machining processes are highly complex than constant-power consumption machining processes, owing to change in one of the process parameters, i.e. cutting speed during end facing. Besides, integrated modelling and optimization of the variable power consumption machining processes for energy-saving have not received attention, consequently, industry deployment of energy-saving solutions is impeded. To bridge these gaps, in this work, the empirical model developed by the author is integrated for the formulation of a multi-objective optimization model of cutting energy consumption (Ecdry) and average-material removal rate (MRR¯¯¯¯¯¯¯¯¯¯¯¯) expressed by process parameters. First, the optimal parameters are determined for Ecdry and MRR¯¯¯¯¯¯¯¯¯¯¯¯ by mono-objective optimization using the Taguchi technique. Second, Grey relational analysis coupled with the Taguchi method is used to obtain the cumulative performance index of the Ecdry and MRR¯¯¯¯¯¯¯¯¯¯¯¯, and to determine their common optimal parameters, resulting in better-compromised decisions. The MRR¯¯¯¯¯¯¯¯¯¯¯¯ improves to 99.97% with only a 10.08% increase in Ecdry on common optimal parameters compared to optimal parameters with mono optimization of Ecdry. Further, analysis of variance revealed that all considered process parameters have statistical significance, and depth of cut is the most significant parameter followed by spindle speed, feed rate and tool nose radius. It was found that energy consumption values predicted by the integrated modelling and optimization approach are close to the experimental values.
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    Application of Optimization and Statistical Techniques in Post-Harvest Supply Chain: A Systematic Literature Survey
    (CRC Press, 2021) Garg, Girish Kant; Routroy, Srikanta
    The objective of this literature survey is to address the utility and potential of optimization, statistical surveys and mathematical research into post-harvest supply chains (PHSCs) to reduce losses. Empirical approaches have always helped to understand a generic issue with a specific perspective to highlight the shortcomings with the existing practice. The optimization approaches are used in the PHSC literature to design an optimum system to enhance revenues and to minimize losses. The existing PHSCs in developing countries such as India suffer enormous losses – e.g., revenue losses, physical damage of produce, microbial losses, qualitative losses, quantitative losses, etc. – in the absence of market prospects due to inefficient packaging for transportation and handling. This study covers a systematic literature survey covering the mathematical, statistical and optimization research performed in the field of PHSC and critical analysis to highlight the key aspects covered in the literature and their future scope.
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    Fuzzy-TOPSIS based multi-objective optimization of machining parameters for improving energy consumption and productivity
    (Elsevier, 2021) Garg, Girish Kant; Routroy, Srikanta
    Due to the increasingly global market and environmental challenges, there is a lot of pressure on manufacturing industries to reduce the energy consumption of the machining process without compromising productivity. The objective of this work is to develop a multi-objective optimization model for the selection of optimal cutting parameters during the turning of an Aluminum workpiece using carbide inserts. Two performance characteristics: energy consumption and productivity were simultaneously optimized. The Taguchi full factorial orthogonal array L27 was used to obtain the experimental plan. The Fuzzy based Technique for Order Preference by Similarity to Ideal Solution (Fuzzy-TOPSIS) was applied to determine the optimal cutting parameters for multi-objective optimization. The optimal results obtained by Fuzzy-TOPSIS were further validated by using the Taguchi method. ANOVA results show that all the considered cutting parameters were statistically significant. Further, the depth of cut was found the most influencing cutting parameter on the energy consumption and productivity.
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    Selection of optimum cutting parameters for minimization of specific energy consumption during machining of Al 6061
    (IOP, 2019) Garg, Girish Kant
    Manufacturing sector consumes a significant amount of energy globally. Machine tools are one of the major equipment in manufacturing sector and hence major consumer of energy. The electrical energy consumed by the machine tools results in emission of harmful gases and substantial stress on environmental. This work focuses on selection of optimum cutting parameters to minimize specific energy consumption (SEC) during turning of Al 6061 with tungsten carbide inserts in dry condition. Experiment are planned using L27 orthogonal array and Taguchi method is applied to determine optimum and most influencing cutting parameters for minimizing SEC. Results shows that feed is the dominating factor followed by cutting speed and depth of cut. The optimum value of feed (mm/rev), cutting speed (m/min) and depth of cut (mm) are found 0.12, 46.2 and 1.0 respectively. Further the energy consumption maps are developed to analyse the influence of cutting parameters on specific energy consumption. The developed energy consumption maps can be used for correlating the region of minimum SEC with selected cutting parameters.
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    A Comparative Analysis of Surface Roughness Prediction Models Using Soft Computing Techniques
    (Springer, 2020-07) Garg, Girish Kant; Sangwan, Kuldip Singh
    Surface roughness is one of the significant index to measure the product quality of the machined parts. The objective of this work is to contribute towards the development of prediction models for surface roughness. In this work, the predictive models were developed for turning operations using soft computing techniques; support vector regression (SVR) and artificial neural network (ANN). The turning experiments are conducted to obtain the experimental data. The developed predictive models were compared using relative error and validated using hypothesis testing. The results indicate that both techniques provide a close relation between the predicted values and the experimental values for surface roughness and are appropriate to predict the surface roughness with significant acceptable accuracy. It is found that ANN performs better as compared to SVR.
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    Modeling of Stresses and Temperature in Turning Using Finite Element Method
    (Trans Tech Publications Ltd, 2013-02) Sangwan, Kuldip Singh; Garg, Girish Kant
    This paper focuses on finite element modeling of orthogonal cutting process of AISI 1045 steel using Modified Johnson Cook (MJC) as constitutive material flow model under various machining parameters. Finite element solutions of cutting forces, effective stresses and temperature are obtained for a wide range of cutting speeds and feeds. The effect of feed and cutting speed on cutting forces, effective stresses and temperature has been studied over a wide range of values. Percentage variation of each is also studied to predict co-relation with the different machining parameters.