Browsing by Author "Garg, Girish Kant"

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Analysis of enablers for vertical integration to enhance rural employability
(Emerald, 2019-06) Routroy, Srikanta; Garg, Girish Kant
The purpose of the paper is to identify, analyze and select the enablers for vertical integration of Aloe vera supply chain (AVSC) so that rural employability will be enhanced in the context of Rajasthan, India.
Analysis of post-harvest supply chain impediments for rural employability and waste reduction
(Inder Science, 2022-03) Routroy, Srikanta; Garg, Girish Kant
The tremendous amount of economic losses, welfare losses and distress among the farmers due to produce spoilage and quality losses are a few major concerns in the Indian post-harvest supply chain (PHSC). A study for identifying and analysing the impediments in the post-harvest supply chain was performed to minimise them. The interpretive structural modelling (ISM) was applied on the identified impediments to analyse the interactions among them and matrix cross-reference multiplication applied to a classification (MICMAC) analysis was used to categorise the impediments into four clusters on the basis of driving and dependence power. The key impediments obtained from the analyses are lack of government support, lack of transportation infrastructure and lack of fixation of minimum support price. And their revisions are to be improved with time to improve farmer profitability, rural employability and waste reduction. These impediments appear at the bottom in structural analysis thus hold a prime value among the identified impediments. The utility of this study is stated in the practical implications section, i.e., adaption of value addition practices for onion to reduce the spoilage ratio.
Analyzing the post-harvest supply chain enablers of vertical integration for rural employability and marketability
(Emerald, 2022) Garg, Girish Kant; Routroy, Srikanta
The purpose of this study is to analyze the post-harvest supply chain enablers (PHSCEs) for vertical integration to enhance rural employability, farmer profitability and rural produce marketability (i.e. market prospects) in the post-harvest supply chain (PHSC). The impact of vertical integration is also explored for various commercial produces.
Application of Optimization and Statistical Techniques in Post-Harvest Supply Chain
(CRC Press, 2021) Routroy, Srikanta; Garg, Girish Kant
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.
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.
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.
Development of a Transient Energy Prediction Model for Machine Tools
(Elsevier, 2021) Routroy, Srikanta; Garg, Girish Kant
Machine tools are one of the vital equipment in the manufacturing sector and consume a significant amount of energy during the steady state and transient state. An extensive amount of research work had conducted to model energy consumption in a steady state of machine tools. The researchers generally neglect the transient state because the profile of transient state energy consumption is stochastic, and time duration is concise, which results in a significant error during the prediction of total energy consumption of machine tools. This work presents an experimental study to acquire the transient state energy consumption of a machine tool. The Matlab software is used to process the captured data, and an empirical model is developed for the prediction of energy consumed by a machine tool in a transient state. The coefficient of determination is determined to evaluate the fitness of the empirical model. The results indicate that the model can predict the transient state energy consumption accurately. This approach can be conveniently applied to predict the total energy consumption of machine tools accurately. The accurate energy consumption model of machine tools is the foundation for selection of optimal process parameters of a machining process that leads to sustainable manufacturing
Development of an Empirical Model for Optimization of Machining Parameters to Minimize Power Consumption
(IOP, 2018) Garg, Girish Kant; Sangwan, Kuldip Singh
The manufacturing sector consumes huge energy demand and the machine tools used in this sector have very less energy efficiency. Selection of the optimum machining parameters for machine tools is significant for energy saving and for reduction of environmental emission. In this work an empirical model is developed to minimize the power consumption using response surface methodology. The experiments are performed on a lathe machine tool during the turning of AISI 6061 Aluminum with coated tungsten inserts. The relationship between the power consumption and machining parameters is adequately modeled. This model is used for formulation of minimum power consumption criterion as a function of optimal machining parameters using desirability function approach. The influence of machining parameters on the energy consumption has been found using the analysis of variance. The validation of the developed empirical model is proved using the confirmation experiments. The results indicate that the developed model is effective and has potential to be adopted by the industry for minimum power consumption of machine tools.
Development of an Empirical Model for Variable Power Consumption Machining Processes: A Case of End Facing
(Springer, 2021-10) Garg, Girish Kant; Routroy, Srikanta
Machining processes contribute significantly to the energy consumption of manufacturing industries, and reducing their energy consumption is a major challenge to achieve sustainable and cleaner manufacturing. The accurate and practical energy consumption prediction models for a machine tool are the foundation for sustainable and cleaner manufacturing. The machining of a workpiece mainly involves constant-power consumption machining processes e.g. turning and variable-power consumption machining processes e.g. end facing. The cutting power characteristics of the variable-power consumption machining processes are more complex and dynamic, due to change in one of the process parameters (e.g. cutting speed during end facing) than the constant-power consumption machining processes, and have received limited attention in the literature. In the present work, an empirical model is developed to predict the cutting energy consumption of the variable-power consumption machining process i.e. end facing. The end facing experiments were performed on a Computer Numerical Control Lathe in the dry and wet environment to obtain the fitting constants of the developed model. Four validation experiments were performed to confirm the prediction capability of the developed model. The validation experiments confirm that the accuracy of the developed model is more than 96%. Further, the predicted power profiles were in good agreement with the measured power profiles, which shows that the developed model satisfactorily encompasses the influences of the process parameters on the cutting power consumption.
Development of an empirical model to quantify carbon emissions for machining of cylindrical parts
(Springer, 2022-10) Routroy, Srikanta; Garg, Girish Kant
As a result of growing environmental issues and stringent carbon emission (CEM) regulations imposed throughout the globe, low CEM has become one of the essential requirements of manufacturing industries. Low-carbon manufacturing, which aims to reduce carbon intensity and improve process efficiency, has evolved as emerging issue that has encouraged a lot of research into quantifying the CEM of different manufacturing processes. To comply with increasingly stringent CEM regulations and achieve low carbon manufacturing, manufacturing industries require accurate CEM data for their products. In this work, an empirical model is developed to quantify carbon emissions for machining of cylindrical parts. The CEM associated with a cylindrical part machining is decomposed into CEM from electrical energy consumption, material consumption, cutting tool wear, and coolant consumption and from the disposal of machining waste materials. Electrical energy consumption of a machine tool is further decomposed into different energy modules: startup, standby, spindle acceleration, idle, rapid positioning, air-cutting, and cutting for accurate quantification of CEM. Energy consumption models are developed for each module, and are integrated to quantify the total energy consumption of the machine tool. Finally, the developed model is applied on a cylindrical part with three different process plans to validate the developed model for practical implementation in industry. The proposed model can be utilized in the manufacturing industry to quantify carbon emissions based on different process parameters before machining a cylindrical part to achieve low carbon manufacturing process planning and scheduling.
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.
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
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.
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.
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.
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.
Modelling of Variable Energy Consumption for CNC Machine Tools
(Elsevier, 2021) Routroy, Srikanta; Garg, Girish Kant
Machining is a prevalent process in manufacturing industries and consumes a considerable amount of energy which causes adverse environmental effects. Establishing an accurate energy evaluation model is essential for a sustainable machining process. An extensive amount of research work is conducted to model energy consumption for the constant material-removal rate machining processes such as turning and milling. However, no significant attempt is made to model energy consumed during variable material-removal rate machining processes like end face turning, grooving etc., which results in a substantial amount of error for the prediction of the total energy required for machining a product. In this work, experiments are performed on a computerized numerical control machine tool to acquire the material-removal energy consumption of end face turning process. The fluke 435 power analyzer is used to measure energy consumption. An empirical model is established between cutting parameters and energy consumed during the end face turning process. The coefficient of determination is used to evaluate the fitness of the model. The results indicate that the model can predict the end face turning energy consumption data accurately. The developed model can be further used to estimate the total energy consumption for machining of a product beforehand in early design stages and to identify the most suitable sustainable machining options.
Multi-objective optimization of machining parameters to minimize surface roughness and power consumption using TOPSIS
(Elsevier, 2019) Routroy, Srikanta; Garg, Girish Kant
Energy saving in the industrial sector is mandatory for emerging countries to reduce negative environmental impact. Manufacturing consumes a significant amount of energy and releases a large amount of waste (solid, liquid and gas), resulting in the substantial stress on the environment. Negative environmental impact is due to a large amount of energy consumption by the machine tools in discrete manufacturing processes like turning, milling and drilling etc. This paper presents a multi-objective optimization model to optimize the machining parameters in turning process. Two objectives, surface roughness and power consumption are simultaneously optimized. The machining parameters are cutting speed, feed rate and depth of cut. Technique for order preference by similarity to ideal solution (TOPSIS) is used to identify the optimal turning parameters and the obtained results indicate that depth of cut is the most significant factor followed by the feed rate and cutting speed. The results obtained by the TOPSIS approach are compared with the existing grey relational analysis approach results. It is found that both optimization techniques show different optimal values. The confirmations experiments are necessary to select the best optimization approach.
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.
A novel approach to model the energy consumption of machine tools for machining cylindrical parts
(Elsevier, 2022-12) Routroy, Srikanta; Garg, Girish Kant
Machine tools plays a significant role in manufacturing industries and are unfortunately responsible for huge energy consumption and associated greenhouse gas emissions. In industry, the length and diameter are reduced to obtain the final dimensions of a cylindrical part. Typically, external turning operation is used to reduce the diameter of the part i.e. Constant-Material Removal Rate (CMRR) machining process and facing is conducted to reduce the length of the part i.e. Variable-Material Removal Rate (VMRR) machining process. In the past, energy evaluation models for machine tools are developed based on CMRR machining processes only, while the machining of a cylindrical part requires both CMRR and VMRR machining processes to manufacture the final product. In the present study, the energy consumption of a machine tool is divided into different energy modules: start-up, standby, spindle acceleration, idle, rapid positioning, air-cutting, CMRR machining process and VMRR machining process. Energy consumption models for each module are developed and integrated to establish the total energy consumption model for a machine tool. Experiments are conducted on a LMW-Smarturn CNC lathe machine tool in the dry and wet environment to obtain the fitting coefficients of the developed models for different energy modules. The validation test results show that the developed model's accuracy is more than 97 %. The developed model can be applied in the industry by the process planners to identify the most energy-efficient process plan before the actual machining of a cylindrical part.