Optimizing in-situ monitoring for laser powder bed fusion process: Deciphering acoustic emission and sensor sensitivity with explainable machine learning

被引:30
作者
Pandiyan, Vigneashwara [1 ]
Wrobel, Rafal [1 ,2 ]
Leinenbach, Christian [1 ,3 ]
Shevchik, Sergey [1 ]
机构
[1] Empa, Swiss Fed Labs Mat Sci & Technol, Ueberlandstr 129, CH-8600 Dubendorf, Switzerland
[2] Swiss Fed Inst Technol, Dept Mat, Lab Nanomet, Vladimir Prelog Weg 1-5-10, CH-8093 Zurich, Switzerland
[3] Ecole Polytech Fed Lausanne, Lab Photon Mat & Characterizat, CH-1015 Lausanne, Switzerland
基金
瑞士国家科学基金会;
关键词
Laser Powder Bed Fusion; Process Monitoring; Empirical Mode Decomposition; Acoustic Emission; Explainable AI (XAI); EMPIRICAL MODE DECOMPOSITION; SUPPORT VECTOR MACHINE; DEFECT DETECTION; FAULT-DIAGNOSIS;
D O I
10.1016/j.jmatprotec.2023.118144
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Metal-based Laser Powder Bed Fusion (LPBF) has made fabricating intricate components easier. Yet, assessing part quality is inefficient, relying on costly Computed Tomography (CT) scans or time-consuming destructive tests. Also, intermittent inspection of layers also hampers machine productivity. The Additive Manufacturing (AM) field explores real-time quality monitoring using sensor signatures and Machine Learning (ML) to tackle this. One such approach is sensing airborne Acoustic Emissions (AE) from process zone perturbations and comprehending flaw formation for monitoring the LPBF process. This study emphasizes the importance of selecting airborne AE sensors for accurately classifying LPBF dynamics in 316 L, utilizing a flat response sensor to capture AE's during three regimes: Lack of Fusion, conduction mode, and keyhole. To comprehensively under-stand AE from a broad process space, the data was collected for two different 316 L stainless steel powder distributions (> 45 mu m and < 45 mu m) using two different parameter sets. Frequency analysis unveiled distinct LPBF dynamics as dominant and correlated in specific frequency ranges. Empirical Mode Decomposition was used to examine the periodicity of AE signals by separating them into constituent signals for comparison. Transformed AE signals were trained to distinguish regimes using ML classifiers (Convolutional Neural Networks, eXtreme Gradient Boosting, and Support Vector Machines). Sensitivity analysis using saliency maps and feature importance scores identified frequency information below 40 kHz relevant for decision-making. This study highlights interpretable machine learning's potential to identify critical frequency ranges for distinguishing LPBF regimes and underscores the importance of sensor selection for enhanced process monitoring.
引用
收藏
页数:17
相关论文
共 53 条
[1]   MTEX-CNN: Multivariate Time series EXplanations for Predictions with Convolutional Neural Networks [J].
Assaf, Roy ;
Giurgiu, Ioana ;
Bagehorn, Frank ;
Schumann, Anika .
2019 19TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM 2019), 2019, :958-963
[2]   In situ defect detection in selective laser melting via full-field infrared thermography [J].
Bartlett, Jamison L. ;
Heim, Frederick M. ;
Murty, Yellapu V. ;
Li, Xiaodong .
ADDITIVE MANUFACTURING, 2018, 24 :595-605
[3]   Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals [J].
Ben Ali, Jaouher ;
Fnaiech, Nader ;
Saidi, Lotfi ;
Chebel-Morello, Brigitte ;
Fnaiech, Farhat .
APPLIED ACOUSTICS, 2015, 89 :16-27
[4]   Monitoring and flaw detection during wire-based directed energy deposition using in-situ acoustic sensing and wavelet graph signal analysis [J].
Bevans, Benjamin ;
Ramalho, Andre ;
Smoqi, Ziyad ;
Gaikwad, Aniruddha ;
Santos, Telmo G. ;
Rao, Prahalad ;
Oliveira, J. P. .
MATERIALS & DESIGN, 2023, 225
[5]   A comprehensive survey on support vector machine classification: Applications, challenges and trends [J].
Cervantes, Jair ;
Garcia-Lamont, Farid ;
Rodriguez-Mazahua, Lisbeth ;
Lopez, Asdrubal .
NEUROCOMPUTING, 2020, 408 :189-215
[6]  
Chen T., 2015, XGBOOST EXTREME GRAD
[7]  
Chen Z, 2023, J Mater Res Technol
[8]  
Cheng B., 2017, 2017 INT SOL FREEF F
[9]  
de Formanoir C., 2023, Healing of Keyhole Porosity by Means of Defocused Laser Beam Remelting Operando Observation by X -Ray Imaging and Acoustic Emission -Based Detection
[10]   Additive manufacturing of metallic components - Process, structure and properties [J].
DebRoy, T. ;
Wei, H. L. ;
Zuback, J. S. ;
Mukherjee, T. ;
Elmer, J. W. ;
Milewski, J. O. ;
Beese, A. M. ;
Wilson-Heid, A. ;
De, A. ;
Zhang, W. .
PROGRESS IN MATERIALS SCIENCE, 2018, 92 :112-224