Machines

Wire Arc Additive Manufacturing (WAAM), also known as Wire Arc Directed Energy Deposition, is used for fabricating large metallic components with high deposition rates. However, the process often leads to residual stress, distortion, defects, undesirable microstructure, and inconsistent bead geometry. These challenges necessitate reliable in-situ monitoring for process understanding, quality assurance, and control. While several reviews exist on in-situ monitoring in other additive manufacturing processes, systematic coverage of sensing methods specifically tailored for WAAM remains limited. This review fills that gap by providing a comprehensive analysis of existing in-situ monitoring approaches in WAAM, including thermal, optical, acoustic, electrical, force, and geometric sensing. It compares the relative maturity and applicability of each technique, highlights the challenges posed by arc light, spatter, and large melt pool dynamics, and discusses recent advances in real-time defect detection and control, process monitoring, microstructure and property prediction, and minimization of residual stress and distortion. Apart from providing a synthesis of the existing literature, the review also provides research needs, including the standardization of monitoring methodologies, the development of scalable sensing systems, integration of advanced AI-driven data analytics, coupling of real-time monitoring with multi-physics modeling, exploration of quantum sensing, and the transition of current research from laboratory demonstrations to industrial-scale WAAM implementation.

Fatigue-related ‘rotor crack’ can cause catastrophic failure if neglected. Thus, IoT-enabled AI-based predictive maintenance for fault detection and diagnosis is explored. Training and testing AI models under similar conditions improves their prediction performance. On variable speed machines, loss of performance occurs when the testing speed differs from the training speed. This research addresses significant performance loss issues using convolutional neural network (CNN)-based transfer learning models. The main causes of performance loss are domain shift, overfitting, data class imbalance, low fault data availability, and biassed prediction. All the above difficult issues make CNN-based fault prediction systems function badly under varying operating conditions. The proposed methodology addresses all domain adaptation challenges. The proposed methodology was tested by collecting vibration data from an experimental rotor system under varied operating conditions. The proposed methodology outperforms classical machine learning (ML) and deep learning (DL) models, overcoming the overfitting issue with optimised hyperparameters, achieving a prediction accuracy of 99.5%. Under varying operating conditions, it outperforms with a prediction accuracy of 93.2%, and in the ‘data class imbalanced’ scenario, the maximal transfer learning capability achieved was 84.4% with the highest F1-Score. Thus, CNN-based transfer learning enables industrial variable speed machines diagnose rotor crack flaws better than ML and DL models.

This article presents an analytical and simulation analysis of the stability of an innovative handling trolley. The analysis demonstrated that the loaded trolley (100 kg load) requires a critical tipping force F_crit of 502.24 N and a work W of 279.05 J. A comparative analysis confirmed a 128% higher force stability for the proposed solution compared to a standard model F_crit = 220 N. Following the structural design, a prototype was created and tested directly at the workplace for which it was designed; in addition to load tests, which it passed without issue, it was necessary to verify its stability. This step was approached from both a theoretical and practical standpoint. Given the need for special clamping of the transported material, a test was first performed on the empty handling trolley, and subsequently, the trolley was verified with the material clamped. This procedure was applied to the theoretical mathematical analytical solution, the simulation, and the practical test. This process required full consideration, given the manner of clamping, the robust and heavy nature of the transported material, and its operation by a single operator. In the practical test, pressure was applied to the trolley, both without load and with load, which verified and confirmed its stability in both longitudinal and transverse directions. The conclusions define that the trolley’s structure was even more stable after adding the load (handling material). A prototype was created and tested directly at the workplace. Practical stability tests were conducted by applying lateral pressure to both empty and loaded configurations, confirming stability in longitudinal and transverse directions. Formal tilt-table testing according to EN 1757 and ISO 22915 standards is planned for final certification.