Machine Learning (ML) has revolutionized various fields, enabling the development of intelligent systems capable of solving complex problems. However, the process of manually designing and optimizing ML models is often time-consuming, labor-intensive, and requires specialized expertise. To address these challenges, Automatic Machine Learning (AutoML) has emerged as a promising approach that automates the process of selecting and optimizing ML models. Within the realm of AutoML, Neural Architecture Search (NAS) has emerged as a powerful technique that automates the design of neural network architectures, the core components of ML models. It has recently gained significant attraction due to its capability to discover novel and efficient architectures that surpass human-designed counterparts. This manuscript aims to present a systematic review of the literature on this topic published between 2017 and 2023 to identify, analyze, and classify the different types of algorithms developed for NAS. The methodology follows the guidelines of Systematic Literature Review (SLR) methods. Consequently, this study identified 160 articles that provide a comprehensive overview of the field of NAS, encompassing discussion on current works, their purposes, conclusions, and predictions of the direction of this science branch in its main core pillars: Search Space (SSp), Search Strategy (SSt), and Validation Strategy (VSt). Subsequently, the key milestones and advancements that have shaped the field are highlighted. Moreover, we discuss the challenges and open issues that remain in the field. We envision that NAS will continue to play a pivotal role in the advancement of ML, enabling the development of more intelligent and efficient ML models for a wide range of applications.

PrognosticsAircraft Prognostics and Health Management (PHM) is a multidisciplinary framework that provides vital information to operators to ensure maximum system uptime and system safety. It does this by estimating the current and future condition (health) of engineering systems and providing decision support. In recent years, PHM has evolved from being a post hoc maintenance support tool to an essential system that should be integrated throughout all stages of the equipment lifecycle. This chapter describes the essential steps of how PHM can be used in the design and manufacturing of future aircraft. There are many benefits in adopting and evaluating PHM in the design stage. This includes a system that is ultimately easier to monitor and maintain, has better logistics, has reduced overall costs, and has less unplanned downtime. As such, it is argued here that PHM should be designed together with the aircraft. Therefore, this chapter proposes a methodology that includes PHM considerations at all stages of aircraft design. By promoting the integration of these disciplines − PHM, engineering design and manufacturing −, we hope to contribute to more reliable and safe aircraft that can achieve more cost-effective operations and a more sustainable future.

The ability for humans to work in close contact with robots in a manufacturing environment has been limited due to safetySafety in manufacturing concerns and the robot’s inability to sense, react, and coordinate with a human without explicit, rigid programming. However, advances in Deep Reinforcement Learning (DRL) have shown considerable promise in developing processes that allow robots to work in a dynamic environment, solving problems and adapting to the actions and communication from human counterparts. This chapter explores the current state of the art for Human Robot Interaction (HRI), discussing the tools, algorithms, and methods being explored. Representative use cases are discussed to better understand what can be accomplished in today’s manufacturing environment and what challenges could be faced. The concerns around safetySafety in manufacturing, ethics, and unintended consequences are identified. Finally, the chapter looks ahead at the obstacles that still need to be overcome before HRI can be fully scalable and widely used.

Continuous monitoring of spray velocity during the cold spray process would be desirable to support quality control, as spray velocity is the key process parameter determining the deposit quality. This study explores the feasibility of utilising Airborne Acoustic Emission (AAE) for real-time monitoring of spray velocity. Six spray tests were conducted, varying pressure and temperature to achieve different velocities. Optical means were used to measure velocity; while, the signal from the AAE was captured during deposition via a microphone. Features demonstrating a strong correlation with velocity were extracted from the acoustic signals. Both rule-based and machine learning models were employed to identify the moments where the nozzle was engaged with the substrate and diagnose the velocity. The results indicate that monitoring the spray velocity of the cold spray process using AAE is feasible.