Differentiation of materials and laser powder bed fusion processing regimes from airborne acoustic emission combined with machine learning

Drissi-Daoudi, Rita; Pandiyan, Vigneashwara; Logé, Roland E.; Shevchik, Sergey; Masinelli, Giulio; Ghasemi-Tabasi, Hossein; Parrilli, Annapaola; Wasmer, Kilian

doi:10.1080/17452759.2022.2028380

Cited by 51 publications

(30 citation statements)

References 65 publications

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“…Based on this analysis, 304 features were selected from which 23 from the time domain, 18 from the frequency domain and 263 from the time-frequency domain. These features are the input of the ML algorithms; in this work Random Forest (RF) and all details can be found in Drissi-Daoudi et al [14].…”

Section: Classification Results For Various Materials and Materials C...mentioning

confidence: 99%

“…The number of materials depended on the specific study. The material compositions and other details can be found in Drissi-Daoudi et al [14]. Finally, this study focused on the main four process regimes that are considered in the AM community [9].…”

Section: Experimental Setup Material Data Acquisitionmentioning

confidence: 99%

“…As mentioned in the introduction, AE system is one of the most effective monitoring methods for dynamical processes [15]. Due to its advantages, AE sensors have been used in AM, particularly L-PBF for online quality monitoring [16,[23][24][25][26][27][28][29] and combining AI [9,14,[17][18][19][20][21][22]30]. It is important to mention that these publications have in common the fact that experiments are conducted for single material and single process parameter per regimes and/or category.…”

Section: Analysis Of the Origin Of The Ae Featuresmentioning

confidence: 99%

“…The main reason is the ability of the AE sensor to detect any acoustic wave reaching it, independently on its origin [34]. In the case of LPBF, the main AE sources are machine and environment noise, the various momentary events during the laser-material interaction as well as the process parameters themselves [14]. Consequently, to have a robust online monitoring system, it is of utmost importance to demonstrate that the AE feature selected for the classification task are only related to the laser-material interaction; which is responsible for the final workpiece quality.…”

Section: Analysis Of the Origin Of The Ae Featuresmentioning

confidence: 99%

“…in our case, laser-material interaction) and propagates within a medium (e.g. workpiece or air) [14]. Hence, it is one of the most effective monitoring methods for dynamical processes [15] and the reason are fourfold.…”

Section: Introductionmentioning

confidence: 99%

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When am (additive manufacturing) meets ae (acoustic emission) and AI (artificial intelligence)

Wasmer¹,

Drissi-daoudi²,

Masinelli³

et al. 2023

eJNDT

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Acoustic Emission (AE) is an effective method to monitor and control the quality in different technical processes and phenomena, including tribology and fracture mechanics. However, in highly dynamic processes such as Laser Additive Manufacturing (LAM) of metal, the processing of AE signals is very burdensome. At the same time, artificial intelligence (AI) has been considered as a new and powerful tool to overcome the complexity of the large data processing with a reasonable computational time. In this contribution, we have summarized our prior works. To start with, we demonstrated that the combination of AE with state-of-the-art signal processing including AI makes it possible to differentiate several process conditions, workpiece quality and materials. Then, we present some limitations when changing process parameters and materials. Finally, we also introduce alternative AI methods to reduce the amount of data needed to train the AI algorithms as well as transfer the knowledge from one material to another one. This will give the reader an overview of the advances of monitoring the LAM process combining AE with AI techniques to make a significant step forward in in situ and real-time process monitoring and quality control. In particular,

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Section: Classification Results For Various Materials and Materials C...mentioning

confidence: 99%

Section: Experimental Setup Material Data Acquisitionmentioning

confidence: 99%

Section: Analysis Of the Origin Of The Ae Featuresmentioning

confidence: 99%

Section: Analysis Of the Origin Of The Ae Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

When am (additive manufacturing) meets ae (acoustic emission) and AI (artificial intelligence)

Wasmer¹,

Drissi-daoudi²,

Masinelli³

et al. 2023

eJNDT

View full text Add to dashboard Cite

show abstract

Progress and Opportunities for Machine Learning in Materials and Processes of Additive Manufacturing

Ng,

Goh,

Goh

et al. 2024

Advanced Materials

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In recent years, there has been a widespread adoption of machine learning (ML) technologies to unravel intricate relationships among diverse parameters in various additive manufacturing (AM) techniques. These ML models excel at recognizing complex patterns from extensive, well‐curated datasets, thereby unveiling latent knowledge crucial for informed decision‐making during the AM process. The collaborative synergy between ML and AM holds the potential to revolutionize the design and production of AM‐printed parts. By leveraging the copious data generated in AM processes, ML algorithms can significantly enhance design optimization. This is achieved by employing forward problem analysis in tandem with iterative optimization techniques or generative artificial intelligence tools. The approach involves reverse‐engineering from desired outcomes to yield valuable insights, ultimately streamlining the AM design process. This review paper delves into the challenges and opportunities emerging at the intersection of these two dynamic fields. It provides a comprehensive analysis of the publication landscape for ML‐related research in the field of AM, explores common ML applications in AM research (such as quality control, process optimization, design optimization, microstructure analysis, and material formulation) and concludes by presenting an outlook that underscores the utilization of advanced ML models, the development of emerging sensors, and ML applications in emerging AM‐related fields. Notably, ML has garnered increased attention in AM due to its superior performance across various AM‐related applications. We envision that the integration of ML into AM processes will significantly enhance 3D printing capabilities across diverse AM‐related research areas. This article is protected by copyright. All rights reserved

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A Review on Application of Acoustic Emission Testing During Additive Manufacturing

Prem,

Sanker,

Sebastian

et al. 2023

J Nondestruct Eval

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Differentiation of materials and laser powder bed fusion processing regimes from airborne acoustic emission combined with machine learning

Cited by 51 publications

References 65 publications

When am (additive manufacturing) meets ae (acoustic emission) and AI (artificial intelligence)

When am (additive manufacturing) meets ae (acoustic emission) and AI (artificial intelligence)

Progress and Opportunities for Machine Learning in Materials and Processes of Additive Manufacturing

A Review on Application of Acoustic Emission Testing During Additive Manufacturing

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