BITS Faculty Publications
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Item An Investigation on Reduction of Cutting Energy Consumption Using High Efficiency Machining Strategy(Elsevier, 2022) Bera, T.C.; Sangwan, Kuldip SinghA large number of machine tools are used on regular basis consuming a large amount of energy. Moreover, the machine tools have poor energy efficiencies and thus, they are ideal candidates for energy saving strategies. Improvement in energy efficiency of machining system will not only benefit the industries economically but also help the world in taking care of energy crisis and air pollution. Therefore, an attempt has been made in the present work to reduce the cutting power consumption using a high efficiency machining (HEM) strategy. The HEM strategy has been used primarily for roughing operation utilizing a lower radial depth of cut (RDOC) and a higher axial depth of cut (ADOC) for milling. During machining, the radial chip thinning occurs with varying RDOC that results into variation in uncut chip thickness and respective chip load. Based on process geometry of milling, a specific energy consumption (SEC) model has been analyzed for the milling. Next, the cutting power has been reduced using high efficiency milling approach. The proposed HEM strategy can reduce cutting time that results into less power consumption and increased productivity of milling by removing more material in unit time. Therefore, the present study is able to contribute significantly towards energy-efficient manufacturing and cleaner production.Item Supply Chain of Sanitary Ware: Sustainability Assessment(The InfoLibrary, 2018) Sangwan, Kuldip SinghSanitary wares are the integral part of construction materials but there is hardly any study in the literature which shows the environmental impacts from the sanitary ware. This paper aims at assessing sustainability of a ceramic sanitary ware supply chain by quantifying the environmental impacts from materials and resources used throughout the different phases of a sanitary ware life cycle. The impacts are quantified using ReCiPe endpoint and midpoint assessment methods with Umberto NXT Software and eco-invent 3.0 database. This study uses climate change, fossil depletion, human toxicity, metal depletion, ozone depletion, terrestrial acidification, water depletion, damage to ecosystem quality, human health, and resources assessment categories to quantify the environmental impacts. The life cycle assessment finds that consumption of heavy fuel oil, electricity, grass, and cement mortar is primarily responsible for the negative impacts on the environment. It is also found that manufacturing and transportation phases of the supply chain have maximum contribution to the environmental degradation. The methodology, assessment methods and impact categories used in the study can be used by the other ceramic enterprises for the identification and benchmarking of environmental hotspots in their supply chains. It is expected that this study will be useful for the policy makers as well as the manufacturer to find the key areas for decreasing the environmental impacts and enhancing sustainability of a sanitary ware supply chain.Item A sustainability assessment framework for cement industry – a case study(Emerald, 2019-02) Digalwar, Abhijeet K.; Sangwan, Kuldip SinghThe purpose of this paper is to develop a framework and key performance indicators (KPIs) to assess the sustainability of the manufacturing organizations along the integrated supply chain.Item Sustainability Assessment of Sanitary Ware Supply Chain Using Life Cycle Assessment Framework—A Case Study(Springer, 2020-07) Sangwan, Kuldip SinghSanitary wares are the integral part of construction materials but there is hardly any study in the literature which shows the environmental impacts from the sanitary ware. This paper aims at assessing sustainability of a ceramic sanitary ware supply chain by quantifying the environmental impacts from materials and resources used throughout the different phases of a sanitary ware life cycle. The impacts are quantified using ReCiPe endpoint and midpoint assessment methods with Umberto NXT Software and eco-invent 3.0 database. This study uses climate change, fossil depletion, human toxicity, metal depletion, ozone depletion, terrestrial acidification, water depletion, damage to ecosystem quality, human health, and resources assessment categories to quantify the environmental impacts. The life cycle assessment finds that consumption of heavy fuel oil, electricity, grass, and cement mortar is primarily responsible for the negative impacts on the environment. It is also found that manufacturing and transportation phases of the supply chain have maximum contribution to the environmental degradation. The methodology, assessment methods and impact categories used in the study can be used by the other ceramic enterprises for the identification and benchmarking of environmental hotspots in their supply chains. It is expected that this study will be useful for the policy makers as well as the manufacturer to find the key areas for decreasing the environmental impacts and enhancing sustainability of a sanitary ware supply chain.Item A Comparative Analysis of Surface Roughness Prediction Models Using Soft Computing Techniques(Springer, 2020-07) Garg, Girish Kant; Sangwan, Kuldip SinghSurface 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.Item Development of an Electric-Load Intelligence System for Component Level Disaggregation to Improve Energy Efficiency of Machine Tools(Springer, 2020-07) Sangwan, Kuldip SinghEnergy and resource efficient manufacturing has become a key priority due to higher energy cost, market competition and environmental regulations. Better transparency and higher levels of disaggregation of energy data are necessary for energy efficiency improvement of machine tools. Since the beginning of the 21st century, some attempts have been made by the researchers to quantify the energy data but only up to the operational state of the machine tool. Better accuracy and transparency require disaggregation up to the component level. This study proposes an Electric-Load Intelligence (E-LI) system for identification of machine tool operating state and disaggregation of time and energy consumed up to the component level. The energy profile is obtained at the power input of a machine tool and analyzed using a set of signal processing techniques and load-disaggregation algorithms. The proposed methodology is validated through a case study of milling process. Various classifiers used in the disaggregation algorithms are compared for their accuracies using the case study data. The results reveal that only a small portion of the total cutting energy (782.24 kJ) was used for actual material removal (40.73 kJ). The proposed study provides accurate data in user friendly format to assist designers and manufacturers for strategic and economic decision making.Item A Bibliometric Analysis of Sustainable Supply Chain Management: Research Implications and Future Perspectives(Springer, 2021-11) Sangwan, Kuldip SinghSustainable supply chain (SSC) is an emerging research area that focuses on the triple bottom line pertaining to all stakeholders and related activities. Important Business decisions relevant processes, and activities are focused on features in SSC articles. Using the bibliometric techniques, the author attempted to analyze the research area's impact, its associated eminent authors, along with their affiliated institutions and countries. Through conducting network analysis in VOSviewer software and Gephi software researchers focus on co-authorship, author specified keywords clustering, and countries-based bibliographic analysis. The research identifies the most influential research work/authors in the defined duration. Using network analysis, authors can identify knowledge groups their affiliations, and future research opportunities. In contrast to the existing literature, the author here used keywords occurrence as a criterion for clustering. The identified clusters define the research themes and keyword occurrence helps in identifying future research implications.Item Modelling of spindle energy consumption in CNC milling(Elsevier, 2022) Sangwan, Kuldip Singh; Bera, T.C.In manufacturing industries, machine tools are frequently used and required a lot of energy to work. Spindle acceleration is a common process when machine tools are in use. It generates a high-energy intensive power peak. The total energy consumption of machine tools in the machining process is strongly affected by these high-power peaks of short duration. Many researchers have overlooked the energy consumption of spindle acceleration resulting into inaccuracies in the prediction of overall energy consumption of machine tools. Therefore, the present study aims to develop a model to predict the spindle acceleration energy consumption of computer numerical control (CNC) milling machines. The proposed model is based on the principle of spindle motor control and includes the computation of moment of inertia of the spindle drive system. To validate the effectiveness of the proposed model, machining experiments are carried out on a CNC milling machine. Without performing time-consuming experiments, the proposed models can be utilized to estimate the power, time, and energy consumption of spindle acceleration. The proposed model helps to determine total energy consumption during machining process correctly.Item Operations and Supply Chain Management in the Food Industry(Springer, 2022) Sangwan, Kuldip SinghThe Indian food supply chain is highly complex, involving government and private traders, big and small players, traditional as well as modern systems. All parts of the Indian food supply chain—procurement, storage, transportation, processing, packaging, and delivery—involve different systems and are not dependent upon a single system. It partially explains why the Indian food supply chain shows strong resiliency during the current pandemic lockdown. Food waste/spoilage and pilferage need to be brought down during post-harvesting, harvesting, and pre-harvesting to feed the growing world population at a reasonable cost. The agility and traceability in food supply chains are critical factors to be improved using modern techniques like blockchain and data analytics. However, more research is required to study these losses, their quantification, root causes, and mitigation techniquesItem Experiential learning: integrating learning and experience in shaping the future of the engineers(Universitat Politècnica de Catalunya, 2022) Sangwan, Kuldip Singh; Sangwan, DevikaThe industry demands skill-equipped engineering graduates who could be efficient enough to adapt to face the challenges of uncertainty posed by a lack of skills and resources. Accreditation boards have identified problem-solving, teamwork, communication, etc. as the workplace required skills. However, industry/employers feel that the engineers seem to lack problem-solving, teamwork, etc. To groom these skills, experiential learning (EL) platform provides hands-on practice.