During milling operations, wear of cutting tool is inevitable; therefore, tool condition monitoring is essential. One of the difficulties in detecting the state of milling tools is that they are visually inspected, and due to this, the milling process needs to be interrupted. Intelligent monitoring systems based on accelerometers and algorithms have been developed as a part of Industry 4.0 to monitor the tool wear during milling process. In this paper, acoustic emission (AE) and vibration signals captured through sensors are analyzed and the scalograms were constructed from Morlet wavelets. The relative wavelet energy (RWE) criterion was applied to select suitable wavelet functions. Due to the availability of less experimental data to train the LSTM model for the prediction of tool wear, SinGAN was applied to generate additional scalograms and later several image quality parameters were extracted to construct feature vectors. The feature vector is used to train three long short-term memory network (LSTM) models: vanilla, stacked, and bidirectional. To analyze the performance of LSTM models for tool wear prediction, five performance parameters were computed namelyR², adjustedR², mean absolute error (MAE), root mean square error (RMSE), and mean square error (MSE). The lowest MAE, RMSE, and MSE values were observed as 0.005, 0.016, and 0.0002 and highR²and Adj.R²values as 0.997 are observed from the vibration signal. Results suggest that the stacked LSTM model predicts the tool wear better as compared to other LSTM models. The proposed methodology has given very less errors in tool wear predictions and can be extremely useful for the development of an online deep learning tool condition monitoring system.

Accurate lithium-ion battery state of health evaluation is crucial for correctly operating and managing battery-based energy storage systems. Experimental determination is problematic in these applications since standard functioning is necessary. Machine learning techniques enable accurate and effective data-driven predictions in such situations. In the present paper, an optimized explainable artificial intelligence (Ex-AI) model is proposed to predict the discharge capacity of the battery. In the initial stage, three deep learning (DL) models, stacked long short-term memory networks (stacked LSTMs), gated recurrent unit (GRU) networks, and stacked recurrent neural networks (SRNNs) were developed based on the training of six input features. Ex-AI was applied to identify the relevant features and further optimize Ex-AI operating parameters, and the jellyfish metaheuristic optimization technique was considered. The results reveal that discharge capacity was better predicted when the jellyfish-Ex-AI model was applied. A very low RMSE of 0.04, MAE of 0.60, and MAPE of 0.03 were observed with the Stacked-LSTM model, demonstrating our proposed methodology’s utility.

The present study proposes a novel method for predicting the discharge capabilities of lithium-ion (Li-ion) batteries using a digital twin model in practice. By combining cutting-edge machine learning techniques, such as AdaBoost and long short-term memory (LSTM) network, with a semiempirical mathematical structure, the digital twin (DT)—a virtual representation that mimics the behavior of actual batteries in real time is constructed. Various metaheuristic optimization methods, such as antlion, grey wolf optimization (GWO), and improved grey wolf optimization (IGWO), are used to adjust hyperparameters in order to optimize the models. As indicators of performance, mean absolute error (MAE) and root-mean-square error (RMSE) are applied to the models after they have undergone extensive training and ten-fold cross-validation. The models are rigorously trained and cross-validated using the NASA battery aging dataset, a widely accepted benchmark dataset for battery research. The IGWO-AdaBoost digital twin model emerges as the standout performer, achieving exceptional accuracy in predicting the discharge capacity. This model demonstrates the lowest mean absolute error (MAE) of 0.01, showcasing its superior precision in estimating discharge capabilities. Additionally, the root mean square error (RMSE) for the IGWO-AdaBoost DT model is also the lowest at 0.01. The findings of this study offer insightful information about the potential utilization of the digital twin model to accurately predict the discharge capacity of batteries.

Intelligent fault diagnosis gives timely information about the condition of mechanical components. Since rolling element bearings are often used as rotating equipment parts, it is crucial to identify and detect bearing faults. When there are several defects in components or machines, early fault detection becomes necessary to avoid catastrophic failure. This work suggests a novel approach to reliably identifying compound faults in bearings when the availability of experimental data is limited. Vibration signals are recorded from single ball bearings consisting of compound faults, i.e., faults in the inner race, outer race, and rolling elements with a variation in rotational speed. The measured vibration signals are pre-processed using the Hilbert–Huang transform, and, afterward, a Kurtogram is generated. The multiscale-SinGAN model is adapted to generate additional Kurtogram images to effectively train machine-learning models. To identify the relevant features, metaheuristic optimization algorithms such as teaching–learning-based optimization, and Heat Transfer Search are applied to feature vectors. Finally, selected features are fed into three machine-learning models for compound fault identifications. The results demonstrate that extreme learning machines can detect compound faults with 100% Ten-fold cross-validation accuracy. In contrast, the minimum ten-fold cross-validation accuracy of 98.96% is observed with support vector machines.

The automation in image analysis while dealing with enormous images generated is imperative to deliver defect-free surfaces in the optoelectronic area. Five distinct morphological images of hybrid perovskites are investigated in this study to analyse and predict the surface properties using machine learning algorithms. Here, we propose a new framework called Multi-Scale-SinGAN to generate multiple morphological images from a single-image. Ten different quality parameters are identified and extracted from each image to select the best features. The heat transfer search is adopted to select the optimized features and compare them with the results obtained using the cuckoo search algorithm. A comparison study with four machine learning algorithms has been evaluated and the results confirms that the features selected through heat transfer search algorithm are effective in identifying thin film morphological images with machine learning models. In particular, ANN-HTS outperforms other combinations : Tree-HTS, KNN-HTS and SVM-HTS, in terms of accuracy,precision, recall and F1-score.