Cutting tool condition is crucial in metal cutting. In-process tool failures significantly influences the surface roughness, power consumption, and process endurance. Industries are interested in supervisory systems that anticipate the health of the tool. A methodology that utilizes the information to predict problems and to avoid failures must be embraced. In recent years, several machine learning-based predictive modelling strategies for estimating tool wear have been emerged. However, due to intricate tool wear mechanisms, doing so with limited datasets confronts difficulties under varying operating conditions. This article proposes the use of transfer learning technology to detect tool wear, especially flank wear under distinct cutting environments (dry, flood, MQL and cryogenic). In this study, the state of the cutting tool was determined using the pre-trained networks like AlexNet, VGG-16, ResNet, MobileNet, and Inception-V3. The best-performing network was recommended for tool condition monitoring, considering the effects of hyperparameters such as batch size, learning rate, solver, and train-test split ratio. In light of this, the recommended methodology may prove to be highly helpful for classifying and suggesting the suitable cutting conditions, especially under limited data situation. The transfer learning model with Inception-V3 is extremely useful for intelligent machining applications.

A crucial method of maintenance in the metal removal manufacturing industry is machine vision-based fault diagnostics and condition monitoring of machine tools. The surface properties of the material are more affected by friction during tool-workpiece contact. Effective problem diagnosis is necessary for machine systems to continue operating safely. Data-driven approaches have recently exhibited great promise for intelligent fault diagnosis. Unfortunately, the data collected under real-world conditions may be imbalanced, making diagnosis difficult. In dry, MQL, and cryogenic circumstances, the method of failure detection of proposed design is novel. The purpose of this interrogation is to evaluate the roughness profiles obtained from the machined surfaces and class separation. Markov Transition Field (MTF) is adopted to encode the surface profiles. In addition to this, Conditional Generative Adversarial Network (CGAN) for augmentation and Long-Short Term Memory, Multi-layer Perceptron (MLP), and 2D- convolutional neural network (CNN) models are used for surface profile forecasting and correlation with process parameters. According to the study’s findings, the 2D-CNN was significantly more accurate than the MLP model in predicting surface profiles, with an average accuracy of above 99.6% in both training and testing and a lower average accuracy of 98.6%. In the lime light, the suggested approach can demonstrate to be quite useful for categorizing and proposing appropriate machining circumstances, specifically in situations with minimal data.

The kidneys play an energetic role in eliminating excess products and fluids from the body, by a complex mechanism which is crucial for upholding a stable balance of body chemicals. Chronic kidney disease (CKD) is considered by an unhurried weakening in renal function that may eventually result in kidney injury or failure. The difficulty of diagnosing the illness rises as it worsens. However, using data from normal medical visits to evaluate the various phases of CKD could help with early detection and prompt care. Researchers suggest a classification strategy for CKD along with optimization strategies used in the learning process. The incorporation of artificial intelligence offers promise because it may often astonish with its skills and enable seemingly difficult undertakings. Modern machine learning techniques have been developed to detect renal illness in light of this. In the current study, a new deep learning model for CKD initial recognition and prediction is introduced. The main objective of the project is to build a strong deep neural network (DNN) and estimate its result outcomes in comparison to other leading-edge machine learning techniques. The outcomes demonstrate that the proposed strategy outperforms current approaches and has promise as a useful tool for CKD detection.

The brain tumor is a life-threatening disease which is caused by the uncontrolled spread of cancerous cells. The diagnosis of this disease is a challenging task due to the variability and size of the lesion. Traditionally, tumors are detected using invasive techniques which are time-consuming and may cause manual errors. This may have a severe impact on patient’s survival rate. The diagnosis of tumors can be improved by adopting automated computer-aided approaches. In this article, a deep learning model is adopted to classify brain MRI images. Three different types of brain tumours, including gliomas, meningiomas, and pituitary tumours, are taken into account in the experimental study together with normal images devoid of malignancies. An effective Mobilenet with Adam Optimizer is proposed for classifying the brain MRI images. The performance of Mobilenet is compared with EfficientNet, AlexNet and VGG16. The classification accuracy of Mobilenet is 91% which is better than the other compared algorithms.

In milling operation, the analysis of power signals are quite important because power signals provide the immediate information of the process that allows the operator to monitor the performance and make necessary modifications in real time. In addition, the power signals are correlated with the quality of machined surface because high power may result in tool breakage that leads to poor surface finish. Therefore, this work firstly investigates the analysis of power signals and its impact on surface characteristics during the milling of additively manufactured aluminium alloys under different conditions. The milling experiments were performed under different cooling conditions and the power signals were calculated from the main cutting forces. Then, the Swin transformer based deep learning model was used on the calculated power signals, and the performance was compared with different models to validate the accuracy of Swin transformer model. Further, the relation between cutter angle rotation and power signals were analyzed under different conditions. According to the surface analysis, the higher power ranges observed during dry machining have a major impact on surface quality and lead to rough milling surfaces. The outcomes also demonstrated that the Swin transformer shows better performance with higher accuracy and Kappa metrics with 0.0001 learning rate.