The proliferation of machine learning (ML) advances will have long-lasting effects on the nondestructive testing/evaluation (NDT/E) community. As these advances impact the field and as new datasets are created to support these methods, it is important for researchers and practitioners to understand the associated challenges. This article provides basic definitions from the ML literature and tips for nondestructive researchers and practitioners to choose an ML architecture and to understand its relationships with the associated data. By the conclusion of this article, the reader will be able to identify the type of ML architecture needed for a given problem, be aware of how characteristics of the data affect the architecture’s training, and understand how to evaluate the ML performance based on properties of the dataset.
There have been numerous efforts in the metrology, manufacturing, and nondestructive evaluation communities to investigate various methods for effective in situ monitoring of additive manufacturing processes. Researchers have investigated the use of a variety of techniques and sensors and found that each has its own unique capabilities as well as limitations. Among all measurement techniques, acoustic-based in situ measurements of additive manufacturing processes provide remarkable data and advantages for process and part quality assessment. Acoustic signals contain crucial information about the manufacturing processes and fabricated components with a sufficient sampling rate. Like any other measurement technique, acoustic-based methods have specific challenges regarding applications and data interpretation. The enormous size and complexity of the data structure are significant challenges when dealing with acoustic data for in situ process monitoring. To address this issue, researchers have explored and investigated various data and signal processing techniques empowered by artificial intelligence and machine learning methods to extract practical information from acoustic signals. This paper aims to survey recent and innovative machine learning techniques and approaches for acoustic data processing in additive manufacturing in situ monitoring.
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