BITS Faculty Publications

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Author: search.filters.author.Garg, Girish KantSubject: search.filters.subject.Mechanical engineering

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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.