Order 0144 |ebook 0144-e Nondestructive Testing Handbook, Radiographic Testing fourth edition, volume 3 NDT Handbook Vol. 3: Radiographic Testing 17 chapters available Aerospace NDT Industry Handbook 19 chapters available Order 1001K |ebook 1001K-e Ultrasonic Testing (UT) Questions &Answers Set: Method and Techniques Recommended Practice No. SNT-TC-1A: Personnel Qualification and Certification in Nondestructive Testing (2020) Order 2073 |ebook 2073-e Thermal/Infrared Testing second edition Order 2265 ebook 2265-e Liquid Penetrant Testing third edition Order 6101 ebook 6101-e Introduction to NDT Order 1530 ebook 1530-e Visual Testing (VT) Instructor Package and Student Package second edition Order 1655 Instructor Package Order 1665 Student Package ASNT Level II Study Guides Available in PT, RT, VT, MT, and UT ASNT Level III Study Guides Available in Basic, PT, RT, VT, MT, UT, IR, LT, and ET Standards Standards, recommended practices, and supplementary materials Q&As Level I, II, and III sample questions for instruction and exam preparation Nondestructive Testing Handbooks The NDT Handbook covers all major methods of nondestructive testing, each major method in its own volume From compelling research to the knowledge you need to earn or maintain your certifications, discover what’s new in ASNT’s catalog of publications. Select publications are available in both print and digital versions. ASNT ...CREATING A SAFER WORLD! ® SHOP NOW at asnt.org/store. Personnel Training Publications (PTP) Classroom Training Series The PTP Series is available in six methods: PT, MT, UT, ET, RT, and VT Each volume covers Level I and II PTP Programmed Instruction Series The PTP PI Series is a self-study resource for Level I and II candidates NDT Method References Resources for learning and reviewing the fundamentals of NDT methods Order 2204 |ebook 2204-e Principles and Applications of Liquid Penetrant Testing second edition NEW! 15 chapters available NDT Handbook Vol. 1: Liquid Penetrant Testing Individual Handbook Chapters 15 chapters available NDT Handbook Vol. 2: Leak Testing Browse ebooks.asnt.org. 2307 ME July dup.indd 49 6/19/23 3:41 PM

MACHINE LEARNING TECHNIQUES FOR ACOUSTIC DATA PROCESSING IN ADDITIVE MANUFACTURING IN SITU PROCESS MONITORING A REVIEW HOSSEIN TAHERI* AND SUHAIB ZAFAR† ABSTR ACT 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. KEYWORDS: additive manufacturing, in situ monitoring, acoustic, machine learning, data processing Introduction Various additive manufacturing (AM) methods are utilized for manufacturing parts with complex geometries and compli- cated features that are either unfeasible or highly challenging to produce via traditional manufacturing techniques. This outstanding capability of AM provides substantial design flex- ibility and facilitates the production of complex parts with marginal added cost compared to subtractive and traditional manufacturing methods (Calta et al. 2018). Laser powder bed fusion (LPBF), directed energy deposition (DED), and wire arc additive manufacturing (WAAM) are among the most popular methods of metal AM (Koester et al. 2018). Fused deposi- tion modeling (FDM), stereolithography (SLA), direct ink writing (DIW), and selective laser sintering (SLS) are the most common AM techniques for polymers (Baechle-Clayton et al. 2022 Lee et al. 2020). The AM processes not only can cause different mechani- cal properties for the parts manufactured, but also lead to the potential generation of specific types of discontinuities and defects in AM parts (Koester et al. 2018, 2019b Taheri et al. 2017). The types of defects in AM parts significantly depend on manu- facturing process conditions and type of materials. A summary of defect types, causes of defect generation, and their potential effect on AM parts is presented in Table 1. Although inspection and quality assessment for the manu- factured parts can be done after the production is finished (ex situ), there are several significant challenges in traditional ex situ inspection methods. One of the major challenges of tra- ditional inspection of AM parts is due to the capability of AM techniques to produce complex-geometry components. This is an outstanding capability for AM but makes traditional inspec- tion of AM parts extremely challenging since many available nondestructive testing (NDT) techniques have been developed for simpler geometries (Bond et al. 2019). Another primary concern in post-production or ex situ inspection of AM parts is that AM techniques are used to manufacture many critical, high-valued, or exotic parts. Possible rejection of such unique parts due to unacceptable quality causes a significant loss of time and cost and is not a desirable outcome for industries (Koester et al. 2018c Taheri 2018). Despite the complexity of the processes in AM, the layer-by-layer deposition of materials allows the measurement and recording of large amounts of data on each layer for statistical process monitoring and quality assessment (Grasso and Colosimo 2017 Koester et al. 2018b). *The Laboratory for Advanced Non-Destructive Testing, In-situ Monitoring and Evaluation (LANDTIE), Department of Manufacturing Engineering, Georgia Southern University, Statesboro, GA, USA 30458 htaheri@georgiasouthern.edu † Stellantis, Chrysler Technology Center, 800 Chrysler Dr., Auburn Hills, MI 48326, USA Materials Evaluation 81 (7): 50–60 https://doi.org/10.32548/2023.me-04356 ©2023 American Society for Nondestructive Testing ME |REVIEWPAPER 50 M A T E R I A L S E V A L U A T I O N • J U L Y 2 0 2 3 2307 ME July dup.indd 50 6/19/23 3:41 PM