Faculty Publications
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Item State of the art on sustainable manufacturing using mono/hybrid nano-cutting fluids with minimum quantity lubrication(Taylor and Francis Ltd., 2022) Singh, V.; Sharma, A.K.; Sahu, R.K.; Katiyar, J.K.In machining operations, the application of cutting fluids has been of prime importance for the extraction of heat from rake surfaces, ease of removal of the chips and reduction of friction at the chip–tool interface. These three objectives are achieved by the supply of suitable conventional cutting fluid at the machining zone using different techniques. However, the misuse of these fluids and their wrong disposal methods were found to have an adverse effect on the environment and health of human. To reduce the usage of conventional cutting fluid, minimum quantity lubrication (MQL) technique has been emerged as an alternative means in the last few years, leading to better eco-friendly. Further, to increase the sustainability of MQL technique, it becomes necessary to use an appropriate exceptional nanostructured material with MQL that could be an effective cutting fluid (i.e. nanocutting fluids) with better tribological and thermophysical properties, and might be helpful in addressing the eco-friendly problem to a great extent. Therefore, the present paper focuses on the review of important published works related to the use of mono/hybrid nanocutting fluids with MQL technique at various processing parameters in different metal cutting operations. Most of the studies have shown a significant reduction in cutting forces, temperature at cutting zone, tool wear, and friction coefficient, and considerable improvement in surface quality by the addition of mono/hybrid nanoparticles enriched cutting fluid in MQL technique as compared to dry as well as wet machining processes. Further, the paper discusses the future scope in the area of hybrid nano-cutting fluids in different machining processes. © 2022 Taylor & Francis.Item Estimation of Strength Properties of Some Rocks using Ball Mill Grinding Characteristics(World Researchers Associations, 2025) Sahas, S.V.; Bijay, K.M.; Chandar, K.R.The strength properties of rocks namely uniaxial compressive strength and tensile strength are important in design and stability evaluation of various mining, geotechnical engineering and other rock engineering projects. Accurate determination of these properties relies on high-quality samples, but challenges like sample availability, preparation of sample, cost and time constraints have led to an increasing reliance on computational methods for prediction. In this context, an indirect approach is proposed for predicting rock strength properties, specifically the uniaxial compressive strength (UCS) and tensile strength (TS), using grinding characteristics of ball mill, an unconventional yet indirect approach. A predictive modelling using multivariate regression is carried out to estimate the relationship between UCS, TS and the grinding characteristics of ball mill. The developed models demonstrated high accuracy with R² values of 0.93 for UCS and 0.96 for TS. Performance evaluation metrics showed an RMSE of 6.03 MPa and a VAF of 93.45% for UCS and an RMSE of 0.99 MPa and a VAF of 96.47% for TS. The validation was performed using experimental UCS and TS values of basalt rocks along with ball mill grinding test data. The error analysis revealed that UCS prediction error ranged from 5.1% to 11.61% while TS prediction error varied between 4.26% and 16.39%. © 2025, World Researchers Associations. All rights reserved.Item A Hybrid Random Forest optimized with the Dolphin Swarm Algorithm for predicting P-Wave Velocity of Sedimentary Rocks using Ball Mill Grinding Characteristics(World Researchers Associations, 2025) Sahas, S.V.; Bijay, K.M.; Chandar, K.R.Rock properties play a crucial role in mining, geotechnical engineering and various engineering projects. P-wave velocity helps in determining the quality and stability of rock masses, essential for tunnel excavation, slope stability and mining operations. P-wave velocity also provides critical input for designing foundations for dams, bridges and other rock structures. Accurate determination of P-wave velocity relies on high-quality samples. However, challenges such as preparation, cost and time constraints have prompted a growing reliance on computational methods for its prediction. Previous investigations predominantly leaned on laboratory-based tests and indirect methodologies for predicting rock properties including P-wave velocity. In contrast, this study introduces an innovative technique for predicting wave velocity (Vp) of sedimentary rocks, particularly limestone using ball mill grinding characteristics throughout the grinding procedure, an unconventional yet effective approach. A hybrid random forest model optimized with dolphin swarm algorithm was developed to predict Vp from grinding characteristics. The performance of the model in training and testing phases was assessed based on determination coefficients (R2), root mean-squared error (RMSE) and variance account for (VAF) which are 0.984, 96.204 m/s and 98.25% in training and 0.973, 102.32 m/s and 97.63% in testing phase respectively. © 2025, World Researchers Associations. All rights reserved.Item Prediction of uniaxial compressive strength of limestone from ball mill grinding characteristics using supervised machine learning techniques(Nature Research, 2025) Swamy, S.V.; Kunar, B.M.; Chandar, K.R.; Alwetaishi, M.; Shashikumar, S.; Reddy, S.Uniaxial Compressive Strength (UCS) is a fundamental parameter in rock engineering, governing the stability of foundations, slopes, and underground structures. Traditional UCS determination relies on laboratory tests, but these face challenges such as high-quality core sampling, sample preparation difficulties, high costs, and time constraints. These limitations have driven the adoption of indirect approaches for UCS prediction. This study introduces a novel indirect method for predicting uniaxial compressive strength, harnessing the grinding characteristics of a ball mill as predictive variables through supervised machine learning techniques. The correlation between grinding characteristics and UCS was examined to determine whether a linear relationship exists between them. A hybrid support vector machine-recursive feature elimination (SVM-RFE) algorithm is applied to identify the critical grinding parameters influencing UCS. Four supervised machine learning models viz., Multiple Linear Regression (MLR), k-Nearest Neighbor Regression (k-NNR), Support Vector Regression (SVR), and Random Forest Regression (RFR) were developed for UCS prediction, with hyperparameter optimization performed using RandomisedSearchCV technique. The Random Forest model outperformed others as the best prediction model, achieving a coefficient of determination (R²) of 0.95, followed by SVR (R² = 0.87), k-NNR (R² = 0.82), and MLR (R² = 0.758). Model robustness was further assessed using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Variance Accounted For (VAF). Internal validation by means of K-fold cross validation and external validation with independent datasets confirmed generalization capability, showing an average prediction error of ± 10%. The findings demonstrate that combining grinding characteristics with machine learning offers an accurate, cost-effective alternative to conventional UCS testing, with significant practical applications in rock engineering. © The Author(s) 2025.