A Digital twin (DT) enables the acquisition and subsequent analysis of large amounts of process data. Various machine learning (ML) algorithms exist for analysis and prediction that can be used in this scenario. However, there is very little understanding of the relative merit of these methods. This paper proposes a DT framework in the context of predictive maintenance in smart manufacturing to compare the prediction efficacy of prevalent ML models. Data-driven models were developed using machine learning algorithms to predict surface roughness and power consumption during a CNC turning operation. Three process parameters, namely cutting velocity, feed rate, and depth of cut, and two dependent parameters, surface roughness and power consumption, were selected for model development. Seven ML algorithms were tested for each response parameter: Linear Regression, XGB Regressor, Random Forest Regressor, Average Ensemble, AdaBoost Regressor, SVR, and MLP. The results of the comparative analysis of the ML algorithms showed that the Random Forest Regressor is the best prediction model for surface roughness, with the highest R² (94.2% ± 2.4%), lowest MAE (0.011 ± 0.002), lowest MAPE (15.6% ± 4.0%), and lowest RMSE (0.017 ± 0.003), while the XGB Regressor demonstrated the best performance for power consumption prediction, with the highest R² (98.9% ± 0.5%), lowest MAE (22.513 ± 4.424), lowest MAPE (3.0% ± 0.5%), and lowest RMSE (42.650 ± 8.933). The best-performing machine learning algorithm was subsequently utilized in the data-driven models, helping to achieve an improved surface finish. This enables predictive maintenance, reducing energy usage for more sustainable production.

Recent research has shown that carbon nanotube (CNT) acts as a model reinforcement material for fabricating nanocomposites. The addition of CNT as a reinforcing material into the matrix improves the mechanical, thermal, tribological, and electrical properties. In this research paper multiwalled carbon nanotube (MWCNT), with different weight percentage (5%, 10%, and 15%), was reinforced into manganese dioxide (MnO₂) matrix using solution method. The different weight % of MWCNT/MnO₂nanocomposite powders was compacted and then sintered. The phase analysis, morphology, and chemical composition of the nanocomposites were examined by X-ray diffractometer, Field Emission Scanning Electron Microscope (FESEM), and Energy Dispersive X-Ray (EDX), respectively. The XRD analysis indicates the formation of MWCNT/MnO₂nanocomposites. The FESEM surface morphology analysis shows that MnO₂nanotube is densely grown on the surface of MWCNT. Further, microhardness of MWCNT/MnO₂nanocomposite was measured and it was found that 10 wt% has higher microhardness in comparison to 5 and 15 wt%. The microhardness of the composites is influenced by mass density, nanotube weight fraction, arrangement of tubes, and dispersion of MWCNT in H₂SO₄(aq) solution.