| 1. Pawanr, S., Garg, G. K., & Routroy, S. (2022). A novel approach to model the energy consumption of machine tools for machining cylindrical parts. Journal of Manufacturing Processes, 84, 28-42. 2. Pawanr, S., Garg, G. K., & Routroy, S. (2022). Prediction of energy efficiency, power factor and associated carbon emissions of machine tools using soft computing techniques. International Journal on Interactive Design and Manufacturing (IJIDeM), 1-19. 3. Pawanr, S., Garg, G. K., & Routroy, S. (2022). Development of an empirical model to quantify carbon emissions for machining of cylindrical parts. Environmental Science and Pollution Research, 1-23. 4. Pawanr, S., Garg, G. K., & Routroy, S. (2022). Development of an empirical model for variable power consumption machining processes: a case of end facing. Arabian Journal for Science and Engineering, 1-12. 5. Priyadarshi, R., Routroy, S., & Kant, G. (2022). Analyzing the post-harvest supply chain enablers of vertical integration for rural employability and marketability. Journal of Business & Industrial Marketing, 37(3), 529-548. 6. Priyadarshi, R., Routroy, S., & Garg, G. K. (2022). Analysis of post-harvest supply chain impediments for rural employability and waste reduction. International Journal of Services and Operations Management, 41(1-2), 163-183. 7. Pawanr, S., Garg, G. K., & Routroy, S. (2022). Prediction of energy consumption of machine tools using multi-gene genetic programming. Materials Today: Proceedings, 58, 135-139. 8. Priyadarshi, R., Routroy, S., & Garg, G. K. (2021). Postharvest supply chain losses: A state-of-the-art literature review and bibliometric analysis. Journal of Advances in Management Research, 18(3), 443-467. 9. Pawanr, S., Tanishk, T., Gulati, A., Garg, G. K., & Routroy, S. (2021). Fuzzy-TOPSIS based multi-objective optimization of machining parameters for improving energy consumption and productivity. Procedia CIRP, 102, 192-197. 10. Pawanr, S., Garg, G. K., & Routroy, S. (2021). Development of a transient energy prediction model for machine tools. Procedia CIRP, 98, 678-683. 11. Pawanr, S., Garg, G. K., & Routroy, S. (2021). Modelling of variable energy consumption for CNC machine tools. Procedia CIRP, 98, 247-251. 12. Garg, G. K., Pawanr, S., & Sangwan, K. S. (2020). A Comparative Analysis of Surface Roughness Prediction Models Using Soft Computing Techniques. In Enhancing Future Skills and Entrepreneurship: 3rd Indo-German Conference on Sustainability in Engineering (pp. 149-155). Cham: Springer International Publishing. 13. Priyadarshi, R., Panigrahi, A., Routroy, S., & Garg, G. K. (2019). Demand forecasting at retail stage for selected vegetables: a performance analysis. Journal of Modelling in Management, 14(4), 1042-1063. 14. Pawanr, S., & Garg, G. K. (2019). Selection of optimum Cutting Parameters for Minimization of Specific Energy Consumption during Machining of Al 6061. In Journal of Physics: Conference Series (Vol. 1240, No. 1, p. 012064). IOP Publishing. 15. Priyadarshi, R., Routroy, S., & Kant, G. (2019). Analysis of enablers for vertical integration to enhance rural employability. Journal of Business & Industrial Marketing. 16. Pawanr, S., Garg, G. K., & Routroy, S. (2019). Multi-objective optimization of machining parameters to minimize surface roughness and power consumption using TOPSIS. Procedia CIRP, 86, 116-120. 17. Garg, G. K., Garg, S., & Sangwan, K. S. (2018). Development of an empirical model for optimization of machining parameters to minimize power consumption. In IOP Conference Series: Materials Science and Engineering (Vol. 346, No. 1, p. 012078). IOP Publishing. 18. Sangwan, K. S., & Kant, G. (2017). Optimization of machining parameters for improving energy efficiency using integrated response surface methodology and genetic algorithm approach. Procedia CIRP, 61, 517-522. 19. Kant, G., Sangwan, K.S., 2015. Predictive modelling for energy consumption in machining using artificial neural network. Procedia CIRP 37, 205–210. 20. Kant, G., Sangwan, K.S., 2015. Predictive modelling and optimization of machining parameters to minimize surface roughness using artificial neural network coupled with genetic algorithm. Procedia CIRP 31, 448-453 21. Sangwan, K.S., Saxena, S., Kant, G., 2015. Optimization of machining parameters to minimize surface roughness using integrated ANN-GA approach. Procedia CIRP 29, 305-310. 22. Kant, G., Sangwan, K.S., 2015. Predictive modelling for power consumption in machining using artificial intelligence techniques. Procedia CIRP 26, 403–407. 23. Kant, G., Sangwan, K.S., 2014. Prediction and optimization of machining parameters for minimizing power consumption and surface roughness in machining. Journal of Cleaner Production 83, 151–164. 24. Kant, G., Rao V, V., Sangwan, K.S., 2013. Predictive modelling of turning operations using response surface methodology. Applied Mechanics and Materials 307, 170–173. 25. Sangwan, K.S., Kant, G., Deshpande, A., Sharma, P., 2013. Modelling of stresses and temperature in turning using finite element method. Appllied Mechanics and Materials 307, 174–177. 26. Kant, G., Sangwan, K.S., 2011. Modelling and Simulation of Metal Cutting Processes. International Journal of Mechanical Engineering Research 1, 129–138. |