Many industries rely heavily on the availability and reliability of complex structural and functional components to execute operations efficiently. However, failure of components during service occurs due to exposure to unfavourable operating conditions, causing wear and tear of these components. The damage will result in costly downtime and potential safety hazards. Repairing, remanufacturing and refurbishing these complex parts is critical. Restoring broken structures into operation ensures the smooth operation of the industry, thus preventing losses. Conventionally, repairing parts poses a challenge. However, Wire-arc additive manufacturing (WAAM), which employs the welding principle, has revolutionised component repairing or remanufacturing. This paper reviews the literature on manufacturing complex parts, repair, remanufacture and refurbishment of broken structural and functional parts using WAAM technology. This paper also highlights the various strategies and techniques currently used to improve the quality of WAAM 3D printed parts. The study further covers the immense potential of WAAM in revolutionising the remanufacturing and repair of components. The review study has provided a roadmap for future research and development to take full advantage of this new cutting-edge manufacturing technology.

The increased role of wind turbine systems makes it important for its operational states to be con-tinuously monitored and optimized. This goal can be achieved using existing methods, which re-lies on closed-form expressions. The use of these methods, however, becomes challenging when interacting parameters cannot be fully presented with closed form expressions. In this paper, an artificial neural network (ANN) based algorithm is proposed as a solution to this problem. This algorithm is used to estimate wind turbine systems operational state and reliability. The proposed method is able to provide a more holistic approach to manage a wind turbine system with respect to the problem mentioned above. Simulation results show that the developed ANN can predict the average number of failures per year, distribution of failure and average downtime per failure with good accuracy. This was achieved using an ANN model with 5-15-3 architecture. The model generates mean square errors of 4.6 × 10^-3, 4.2 ×10-3, and 4.0 × 10^-3 at the training, validation, and testing stages, respectively. The study is beneficial to wind turbine practitioners and manufacturers as its findings can provide in-depth understandings of reliability issues of the system.