Shrikant Shankarrao Pawar

@bits-pilani.ac.in

Doctoral fellow, Department of Mechanical Engineering
BITS Pilani, Pilani Campus

Shrikant Shankarrao Pawar


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https://researchid.co/monya123

RESEARCH INTERESTS

CAD/CAM, CNC machine tools, Energy efficient machining
8

Scopus Publications

70

Scholar Citations

4

Scholar h-index

3

Scholar i10-index

Scopus Publications

  • Identification of a generic surface error classification scheme in peripheral milling
    Shrikant Shankarrao Pawar, Tufan Chandra Bera, Kuldip Singh Sangwan
    Advances in Materials and Processing Technologies, 2026
  • Machine Learning Based Cutting Force Prediction in Thin-Wall Turning Operation
    Pronamika Borthakur, Ashrut Sharma, Shrikant Shankarrao Pawar, Tufan Chandra Bera, Kuldip Singh Sangwan
    Procedia CIRP, 2026
    Many complex components may contain thin-walled sections which are mainly produced by machining operation. These thin-walled sections are likely to deform during machining due to cutting forces resulting into form error in the final component. During machining, cutting force is the major controlling parameter determining the part quality, productivity and energy consumption. The current study mainly aims to estimate longitudinal, tangential and radial force components in thin-wall turning through machine learning algorithms. The required data for the development of the predictive models are obtained through turning investigations conducted for various cutting conditions. Results acquired by the predictive models are compared with the experimental ones over a wide range of cutting conditions. The present work will be helpful to predict the form error of thin-walled turned components. The expense of conducting multiple machining operation will be reduced with the help of such force model by predicting various force components, which will help in lowering the energy consumption and decreasing carbon foot prints.
  • Towards energy efficient milling of variable curved geometries
    Shrikant Shankarrao Pawar, Tufan Chandra Bera, Kuldip Singh Sangwan
    Journal of Manufacturing Processes, 2023
  • Developing Energy Efficient Milling Strategy for Variable Curved Geometry Using Constant Engagement Method
    Shrikant Shankarrao Pawar, Pronamika Borthakur, Tufan Chandra Bera, Kuldip Singh Sangwan
    Procedia CIRP, 2023
    The continuous fluctuation in force profile in milling of variable curved geometry creates a barrier in stable machining and in cutting power consumption. The fluctuation in force profile happens due to alteration in chip load in the presence of workpiece curvature. The present study aims to develop an energy efficient machining strategy for milling of variable curved geometries where more uniform cutting force and cutting power consumption profiles are accomplished due to constant chip load along the peripheral length of curved geometry. The proposed strategy involves mechanics of milling, instantaneous cutting force and cutting power consumption. It is formulated based on constant chip load by regulating entry angle of milling cutter according to workpiece curvature along the peripheral length. Thus, the cutting power fluctuation that is occurred due to variation of workpiece curvature is reduced by regulating tool-workpiece engagement. The energy consumption is reduced almost 18 % using the proposed approach. It results into developing an energy efficient machining strategy for milling of variable curved geometry. It also provides stable machining process and increased tool life by reducing tool wear due to reduction of force fluctuation during metal removal process.
  • Energy consumption modelling in milling of variable curved geometry
    Shrikant Shankarrao Pawar, Tufan Chandra Bera, Kuldip Singh Sangwan
    International Journal of Advanced Manufacturing Technology, 2022
  • Modelling of spindle energy consumption in CNC milling
    Shrikant Shankarrao Pawar, Tufan Chandra Bera, Kuldip Singh Sangwan
    Procedia CIRP, 2022
    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.
  • An Investigation on Reduction of Cutting Energy Consumption Using High Efficiency Machining Strategy
    Soikot Banerjee, Shrikant Shankarrao Pawar, Tufan Chandra Bera, Kuldip Singh Sangwan
    Procedia CIRP, 2022
    A 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.
  • Modelling of Energy Consumption for Milling of Circular Geometry
    S.S. Pawar, T.C. Bera, K.S. Sangwan
    Procedia CIRP, 2021
    Machine tools are dominant end users of electrical energy in manufacturing, and responsible for high carbon emissions. There is hardly any research work on the energy modelling for curved surface milling. The present study aims to develop energy consumption model for milling of circular geometries as a part of process planning for machining operations to reduce cost, improve energy efficiency and general productivity. The circular geometry may have concave or convex shape which leads to change in magnitude of curvature. Therefore, the magnitude and distribution of cutting forces and concerned cutting powers are quite different in both these machining situations. This necessitates the need to investigate this aspect comprehensively. A process geometry model is developed based on process geometry variables of feed per tooth along cutter contact path, entry and exit angles of tooth, engagement angle, undeformed chip thickness, etc. Next, the process geometry variables in conjunction with mechanistic cutting constants are used to develop a force model for estimating the feed force and normal force components. Lastly, a power consumption model is developed based on the instantaneous force component and velocity of milling cutter to estimate both the instantaneous and average power consumed during the milling process. Machining experiments are performed to conform the validity of the proposed model by comparing the measured power to their predicted counterpart. The developed model can be used for estimating the power consumption for milling of circular geometries reliably and efficiently without conducting the costly experiments. In addition to this, the proposed model extends the existing model by considering the effect of workpiece curvature and aims at providing a useful aid for prediction of power consumption in peripheral milling of circular surfaces. Therefore, an attempt has been made to provide a basic platform for in-depth comprehension and characterization of energy consumption. The proposed model has many applications particularly in die-mold manufacturing and aircraft industry and it can be extended to curved geometries having variable curvatures.

RECENT SCHOLAR PUBLICATIONS

  • Identification of a generic surface error classification scheme in peripheral milling
    SS Pawar, TC Bera, KS Sangwan
    Advances in Materials and Processing Technologies, 1-28 , 2026
    2026
  • Machine learning based cutting force prediction in thin-wall turning operation
    P Borthakur, A Sharma, SS Pawar, TC Bera, KS Sangwan
    Procedia CIRP 138, 421-426 , 2026
    2026
    Citations: 1
  • Towards energy efficient milling of variable curved geometries
    SS Pawar, TC Bera, KS Sangwan
    Journal of Manufacturing Processes 94, 497-511 , 2023
    2023
    Citations: 5
  • Developing Energy Efficient Milling Strategy for Variable Curved Geometry Using Constant Engagement Method
    SS Pawar, P Borthakur, TC Bera, KS Sangwan
    Procedia CIRP 116, 402-407 , 2023
    2023
    Citations: 1
  • Energy consumption modelling in milling of variable curved geometry
    SS Pawar, TC Bera, KS Sangwan
    The International Journal of Advanced Manufacturing Technology 120 (3), 1967 … , 2022
    2022
    Citations: 18
  • An Investigation on Reduction of Cutting Energy Consumption Using High Efficiency Machining Strategy
    S Banerjee, SS Pawar, TC Bera, KS Sangwan
    Procedia CIRP 105, 198-203 , 2022
    2022
    Citations: 4
  • Modelling of spindle energy consumption in CNC milling
    SS Pawar, TC Bera, KS Sangwan
    Procedia Cirp 105, 192-197 , 2022
    2022
    Citations: 19
  • Modelling of energy consumption for milling of circular geometry
    SS Pawar, TC Bera, KS Sangwan
    Procedia CIRP 98, 470-475 , 2021
    2021
    Citations: 22

MOST CITED SCHOLAR PUBLICATIONS

  • Modelling of energy consumption for milling of circular geometry
    SS Pawar, TC Bera, KS Sangwan
    Procedia CIRP 98, 470-475 , 2021
    2021
    Citations: 22
  • Modelling of spindle energy consumption in CNC milling
    SS Pawar, TC Bera, KS Sangwan
    Procedia Cirp 105, 192-197 , 2022
    2022
    Citations: 19
  • Energy consumption modelling in milling of variable curved geometry
    SS Pawar, TC Bera, KS Sangwan
    The International Journal of Advanced Manufacturing Technology 120 (3), 1967 … , 2022
    2022
    Citations: 18
  • Towards energy efficient milling of variable curved geometries
    SS Pawar, TC Bera, KS Sangwan
    Journal of Manufacturing Processes 94, 497-511 , 2023
    2023
    Citations: 5
  • An Investigation on Reduction of Cutting Energy Consumption Using High Efficiency Machining Strategy
    S Banerjee, SS Pawar, TC Bera, KS Sangwan
    Procedia CIRP 105, 198-203 , 2022
    2022
    Citations: 4
  • Machine learning based cutting force prediction in thin-wall turning operation
    P Borthakur, A Sharma, SS Pawar, TC Bera, KS Sangwan
    Procedia CIRP 138, 421-426 , 2026
    2026
    Citations: 1
  • Developing Energy Efficient Milling Strategy for Variable Curved Geometry Using Constant Engagement Method
    SS Pawar, P Borthakur, TC Bera, KS Sangwan
    Procedia CIRP 116, 402-407 , 2023
    2023
    Citations: 1
  • Identification of a generic surface error classification scheme in peripheral milling
    SS Pawar, TC Bera, KS Sangwan
    Advances in Materials and Processing Technologies, 1-28 , 2026
    2026