Sri Ramakrishna Engineering College

This study investigates the tribological behaviour of friction stir processed (FSPed) AA8011 surface composites reinforced with 1–3 wt

Localized muscle fatigue is an exercise-induced decline in the force-generating capacity of muscles. The patients with severe motor disabilities, such as tetraplegics, can use their facial muscles to control assistive devices. However, repeated use of these muscles often leads to fatigue which in turn alters the characteristics of facial EMG. The facial EMG signals are stochastic, nonstationary, and multicomponent, and their characteristic changes under fatiguing contractions are not yet well established. In this work, facial EMG signals are recorded from the left and right frontalis muscles of fifty healthy subjects under a standard experimental protocol. The first and last six-second segments of the signals correspond to nonfatigue and fatigue conditions, respectively. These signals are preprocessed and decomposed using a three-level maximum overlap discrete wavelet packet transform (MODWPT). The first two different wavelets are employed for the decomposition, namely Daubechies-4 (db4) and discrete Meyer (dmey). In order to quantify the time-scale representations, wavelet energy is extracted from all scales. Finally, these features are used to develop a multilayer perceptron (MLP) network for detecting the fatigue state. MODWPT is capable of representing the nonstationary and multicomponent variations of facial EMG under both fatigue and nonfatigue conditions. Wavelet energy is higher in the nonfatigue state across all scales and both the wavelets (p < 0.05). The MLP based on db4 achieves a maximum accuracy of 94.35

The rapid adoption of the Industrial Internet of Things (IIoT) in smart manufacturing and critical infrastructure has significantly increased the exposure of industrial networks to sophisticated cyber threats. Ensuring secure communication and reliable threat detection in IIoT environments has therefore become a critical challenge. This study proposes an intelligent Cyber Threat Detection and Response System that integrates a Hybrid Deep Neural Network with the Grey Wolf Optimizer to enhance security in IIoT networks. The proposed framework utilizes CyberTec IIoT Malware Dataset (CIMD‑2024) on Kaggle containing network traffic characteristics, device communication patterns, and anomaly indicators. A comprehensive data preprocessing phase is employed, including noise removal, normalization, and missing value handling, to improve data quality and model reliability. The hybrid deep learning architecture combines Convolutional Neural Networks for spatial feature extraction with Long Short-Term Memory networks to capture temporal dependencies in network behavior. Additionally, a dual-attention mechanism is incorporated to emphasize significant spatial and temporal features, thereby improving the accuracy of cyber threat classification. The Grey Wolf Optimizer is applied to optimize key hyperparameters such as learning rate, dropout rate, and batch size, leading to improved model performance. Experimental results demonstrate that the proposed model achieves an accuracy of 96.5

Engineering materials have evolved tremendously in today’s environment. Because conventional materials are failing to meet the needs of todays applications, a variety of composites are being developed to solve these difficulties. In the majority of cases, the most important manufacturing procedure is turning, and the cutting tool’s state and its input parameters have the biggest impact on surface finish. For a long time, academics and professional engineers have been interested in the process of finding optimal settings for turning innovative composite materials. Al7075/ZrO2 composites are created in this study by stirring casting with 4 wt

This research project developing an real time tool wear monitoring system for CNC turning operations, leveraging a computer vision and predictive modelling to enabling a real-time tool condition assessment. This system addresses critical industry challenges of unplanned downtime, tooling costs, and quality control due to uneven tool wear, through an innovative non-contact approach that extracts machining parameters directly from CNC displays. The core methodology integrates optical character recognition (OCR) using OpenCV and Tesseract with a physics-based wear model adapted from Choudary’s equations. A calibrated camera captures spindle load, speed, and feed rate data at regular intervals, achieving 98.5