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.

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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.

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This paper provides a detailed review of a digital thread for composite aerospace components. The current state of the digital thread continues to progress and at an ever-accelerating rate due to advancements in supporting technologies such as AI, data capture/processing/storage, sensors, simulation, and blockchain. While the individual steps that make up the digital thread have made manufacturing innovation and benefits possible, the connection points of the thread are not consistently solid, with many experiments and proof-of-concepts being conducted, but with few full digital threads in deployment. Key gaps include the ability to handle such large and continuous amounts of data, the infrastructure needed to capture and process them for insight, and the AI-based analytics to build and scale enough to obtain the expected exponential benefits for life cycle insight and manufacturing optimization. Though some of these gaps may take specific technology innovations to advance, there is a specific roadmap that can be deployed immediately in order to obtain “rolling ROI” benefits that will scale in value as this cycle is repeated across the product line.@en