Heat pipes are one of the most efficient devices of transporting heat from one region to another. It was utilized in wide range of cooling applications. Heat pipe’s thermal behaviors deeply interlinked with the outlet temperature. To predict the behaviors of the heat pipe, a model was essential to predict the outlet temperature. In this work, models viz., Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Deep Neural Network (DNN) and Deep Neural Network with Dropouts (DNN-DO) have been used to predict the outlet temperature of the heat pipe. The result shows that, out of four, the most precise model was CNN followed by DNN-DO, DNN and ANN. Compared to ANN model, CNN’s mean absolute error (MAE) reduced by 66.3545% and R² score increased by 15.8346%. In case of percentage error (PE), 95.0649% data predicted between +1.5% by CNN model.

In this study, the tensile and shear strengths of aluminum 6061-differently grooved stainless steel 304 explosive clads are predicted using deep learning algorithms, namely the conventional neural network (CNN), deep neural network (DNN), and recurrent neural network (RNN). The explosive cladding process parameters, such as the loading ratio (mass of the explosive/mass of the flyer plate, R: 0.6–1.0), standoff distance, D (5–9 mm), preset angle, A (0–10°), and groove in the base plate, G (V/Dovetail), were varied in 60 explosive cladding trials. The deep learning algorithms were trained in a Python environment using the tensile and shear strengths acquired from 80% of the experiments, using trial and previous results. The remaining experimental findings are used to evaluate the developed models. The DNN model successfully predicts the tensile and shear strengths with an accuracy of 95% and less than 5% deviation from the experimental result.

The current study attempts to predict the outlet temperature of a hybrid nanofluid heat pipe using three machine learning models, namely Extra Tree Regression (ETR), CatBoost Re-gression (CBR), and Light Gradient Boosting Machine Regression (LGBMR), in the Python environment. Based on 7000 experimental data (various heat input, inclination angle, flow rate, and fluid ratio), different training (95%–5%) and testing (5%–95%) split sizes, a closer prediction was attained at 85:15. The three attempted machine learning models are capable of predicting the outlet temperature, as evidenced by the less than 5% deviation from the experi-mental results. Of the three attempted machine learning models, the ETR model outperforms the other two with a higher accuracy (98%). Further, the sensitivity analysis indicates the ab-sence of data overfitting in the attempted models.

The effectiveness of the pulsed mode laser weld joint is characterized by the supply of optical energy to the interface. The laser welding process parameters, such as laser power, pulse duration, travel speed and focus, determine the efficacy of the weld, which is defined by full penetration and free of pores and defects. However, expressing the relationship between various process parameters and mechanical strength is intricate due to the prevalence of non linear relationship. The development of computational approaches aids in optimizing the laser welding parameters and reducing trial and error. Hence, the artificial neural network (ANN) model is developed, in a python environment, to predict the optimum process parameter values for a desirable depth to width ratio and maximum tensile strength of UNS S32750 laser butt weld joints. The developed model was assessed by experiments, not utilized for training. The ANN model predicts the depth to width ratio and tensile strength of the weld joints with an accuracy of 90% and less than 10% divergence from the experimental result. Furthermore, for the process parametric condition (Power: 550 W, Focus: -1 mm, Pulse duration: 13 Hz and Travel speed: 136 mm/min), the maximum tensile strength is obtained.

In this study, an artificial neural network (ANN) model is created to predict aluminium-stainless steel explosive clads' tensile and shear strengths. The parameters for the explosive cladding process, such as the loading ratio (mass ratio of the explosive and the flyer, 0.6-1.0), standoff distance (5-9 mm), preset angle (0°-10°), and groove in the base plate (V/Dovetail), were altered. The ANN algorithm was trained in Python using the tensile and shear strengths gathered from 80% of the experiments (60), trials, and prior results. The constructed model was evaluated utilizing the remaining experimental results. The ANN model successfully predicts the tensile and shear strengths with an accuracy of less than 10% deviation from the experimental result.