3. The centroid locations are recomputed based on the assign- ment of data points in the previous step. 4. The process repeats, until one of the following conditions is met: (a) the centroid locations are stable (b) the data points do not change clusters or (c) the maximum number of itera- tions has been reached. An application of the k-means clustering algorithm to the in situ monitoring of an AM process using AET is found in Taheri et al. (2019). In this study, acoustic signatures were used for in situ monitoring of the DED AM process where the depo- sition was performed with the machine operating in five differ- ent states. These states included “control,” under which there was just powder spray, and “baseline,” under which there were no active deposition activities, as well as optimum (normal) process, low laser power, and low powder feed. Dominant features of acoustic signatures in both the time and frequency domains were identified and extracted from the acoustic signatures for all process conditions. The k-means clustering algorithm was applied to classify different process conditions, as shown in Figure 6. Correlations were demon- strated between metrics and various process conditions, which showcase the capability of AE for in situ monitoring of the AM process. Clear isolation of the baseline condition, at which no active deposition or laser-powder interaction occurs, shows that basic acoustic response of the AM system is distinct from when active manufacturing is happening. The next observa- tion is related to the optimum settings (C1) versus powder feed only (CO) conditions. However, a separation of C1 and CO clusters was observed for C1 and CO, but the smaller iso- lation of clusters and larger overlap of cluster data could be an indication of significant influence of laser-material interac- tion compared to system and material characteristics. Last but not least, comparison of the conditions where manufacturing processes are happening (C1, C2, and C3) is interpreted as the indication of AET for separation of manufacturing processes and significant influence of laser-material interaction in AM processes. Summary and Conclusions Acoustic techniques are proven methods for many traditional inspection and quality monitoring applications. Due to the promising capabilities of acoustic methods for nondestructive inspection and monitoring of many kinds of processes, they have been identified as an auspicious candidate for in situ measurement and monitoring for AM processes. Two major reasons impede the application of acoustic techniques for in situ monitoring in AM processes. First is the quite low SNR due to the high sensitivity of acoustic sensors to environmen- tal noise, which is the case in AM processes. The second is interpreting the signals to identify a correlation between the acoustic signals and the actual events. Various sensors and sensing approaches have been used to enhance the low SNR, such as using noncontact acoustic measurement via micro- phone or laser. Researchers have also utilized fiber-optic sensors to improve acoustic signal detection, which provides a new way of improving signal recording for in situ monitoring. Advanced signal processing techniques were used to perform data preparation, such as noise reduction and band filtering, to address the data processing and interpretation challenge. Consequently, ML algorithms have been adapted in different formats to extract and analyze the features of acoustic signals effectively. These algorithms showed an effective way and significant improvement in analyzing acoustic signals under different conditions for in situ process monitoring of AM and provide a promising pathway for the manufacturers to imple- ment acoustic techniques for monitoring and maintaining the quality of products. Sensor integration into the AM system, detection scheme, and SNR are the existing major gaps and barriers in acoustic-based in situ monitoring of AM processes ME |AI/ML 20 15 10 5 0 Centroid amplitude of Fourier transform (CA) Centroid frequencyufreq of Fourier transform (Cf) (kHz) 2.5 2 1.55 1 0.5 0 1150 12000 13000 13500 1400 BL: Baseline C1: Optimum settings C2: Low powerp C3: Low powder feed C0: Powder feed only f F f m 5 1 0 5 1 112500 B C O in s p Figure 6. A three- dimensional graphical representation of the additive manufacturing process condition using three spectral features (Taheri et al. 2019). 58 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 58 6/19/23 3:41 PM Peak amplitude of Fourier transform (PA)

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