Anomalies often occur in metal additive manufacturing from processing inconsistencies and uncertainty. A robust fault detection system that uses sensor measurements such as melt pool imaging has the potential to improve part quality and save production time by anticipating print failure. Toward this goal, we develop and validate a fault detection technique using melt pool geometry-related measurements from an in situ near-infrared optical camera. This method is unsupervised and is trained on a small dataset, mitigating human error in classifying fault types, and reducing lead times for preparing training datasets. Furthermore, this method uses learned geometry-informed nominal behavior of the melt pool signal to make informed decisions on the process health. There are spatial-temporal characteristics embedded in the melt pool images, caused by the periodicity in the geometry-dependent raster pattern. These characteristics can be captured in the frequency domain using the signal spectrogram, a representation of the frequency content over time. Defects will appear in the spectrogram, disrupting the healthy spectral response. To quantify healthy spectrograms, we use principal component (PC) decomposition to extract the features of these spectrograms as a set of nominal basis vectors. Anomaly detection is then performed by calculating the error between the original and reconstructed spectrogram vector by projection of the spectrogram PCs onto the nominal basis. The reconstruction error for anomalous signals is larger than that from healthy signals, which is then used for fault detection. A one-tailed statistical test is used to determine the fault detection threshold for the reconstruction error signal. This method is tested on three raster patterns and performs better than a comparative time-series thresholding method. We demonstrate that this time-frequency algorithm can detect both temporal faults (which occur at a single time instant) and spatial faults (such as those introduced by an improper sintering), differentiating them from nominal operation.
McCannRObeidiMHughesC, et al. In-situ sensing, process monitoring and machine control in laser powder bed fusion: a review. Addit Manuf2021; 45.
2.
ChenYPengXKongL, et al. Defect inspection technologies for additive manufacturing. Int J Extreme Manuf2021; 3(2): 022002.
3.
FuYDowneyAYuanL, et al. Machine learning algorithms for defect detection in metal laser-based additive manufacturing: a review. J Manuf Processes2022; 75: 693–710.
4.
HuangDLiH. A machine learning guided investigation of quality repeatability in metal laser powder bed fusion additive manufacturing. Mat Design2021; 203: 109606.
5.
NadammalNCabezaSMishurovaT, et al. Effect of hatch length on the development of microstructure, texture and residual stresses in selective laser melted superalloy Inconel 718. Mat Design2017; 134: 139–150.
6.
NadammalNMishurovaTFritschT, et al. Critical role of scan strategies on the development of microstructure, texture, and residual stresses during laser powder bed fusion additive manufacturing. Add Manuf2021; 38: 101792.
7.
KudzalAMcWilliamsBHofmeisterC, et al. Effect of scan pattern on the microstructure and mechanical properties of Powder Bed Fusion additive manufactured 17-4 stainless steel. Mat Design2017; 133: 205–215.
8.
OkaroIJayasingheSSutcliffeC, et al. Automatic fault detection for laser powder-bed fusion using semi-supervised machine learning. Add Manuf2019; 27: 42–53.
9.
RomboutsMKruthJFroyenL, et al. Fundamentals of selective laser melting of alloyed steel powders. CIRP Ann Manuf Technol2006; 55.
10.
LiYGuDThermal behavior during selective laser melting of commercially pure titanium powder: Numerical simulation and experimental study. Addit Manuf2014; 1: 99–109.
11.
MoranTWarnerDPhanN. Scan-by-scan part-scale thermal modelling for defect prediction in metal additive manufacturing. Addit Manuf2021; 37: 101667.
12.
GrassoMRemaniADickinsA, et al. In-situ measurement and monitoring methods for metal powder bed fusion: an updated review. Meas Sci Technol2021; 32(11): 112001.
13.
HarbigJWenzlerDBaehrS, et al. Methodology to determine melt pool anomalies in powder bed fusion of metals using a laser beam by means of process monitoring and sensor data fusion. Materials2022; 15: 1265.
14.
AshbyAGussGGaneriwalaR, et al. Thermal history and high-speed optical imaging of overhang structures during laser powder bed fusion: a computational and experimental analysis. Addit Manuf2022; 53: 102669.
15.
OgokeFFarimaniA. Thermal control of laser powder bed fusion using deep reinforcement learning. Addit Manuf2021; 46: 102033.
16.
StrantzaMBaereDRomboutsM, et al. Feasibility study on integrated structural health monitoring system produced by metal three-dimensional printing. Struct Health Monit2015; 14: 622–632.
17.
ShkorutaAMishraSRockS. An experimental study on process modeling for selective laser melting. In: 2020 American Control Conference (ACC), 2020, pp. 467–473. IEEE.
18.
ShkorutaAMishraSRockS. Real-time image-based feedback control of laser powder bed fusion. In: ASME Letters in Dynamic Systems and Control. 2022; 021001.
19.
CraeghsTClijstersSYasaE, et al. Determination of geometrical factors in layerwise laser melting using optical process monitoring. Optics Lasers Eng2011; 49: 1440–1446.
20.
JeonIYangLRyuK, et al. Online melt pool depth estimation during directed energy deposition using coaxial infrared camera, laser line scanner, and artificial neural network. Add Manuf2021; 47: 102295.
21.
SeifiSTianWDoudeH, et al. Layer-wise modeling and anomaly detection for laser-based additive manufacturing. J Manuf Sci Eng Trans ASME2019; 141: 1–12.
22.
ScimeLSiddelDBairdS, et al. Layer-wise anomaly detection and classification for powder bed additive manufacturing processes: a machine-agnostic algorithm for real-time pixel-wise semantic segmentation. Addit Manuf2020; 36: 101453.
23.
FerrucciMCraeghsTCornelissenS, et al. Lessons learned in the design of reference fiducials for layer-wise analysis of test coupons made by laser powder bed fusion. Addit Manuf2021; 42: 101997.
24.
BartlettJJaramaAJonesJ, et al. Prediction of microstructural defects in additive manufacturing from powder bed quality using digital image correlation. Mat Sci Eng A2020; 794: 140002.
25.
AbdelrahmanMReutzelENassarA, et al. Flaw detection in powder bed fusion using optical imaging. Addit Manuf2017; 15: 1–11.
26.
BarrettCMacDonaldEConnerB, et al. Micron-level layer-wise surface profilometry to detect porosity defects in powder bed fusion of Inconel 718. Jom2018; 70: 1844–1852.
27.
GrassoMLaguzzaVSemeraroQ, et al. In-process monitoring of selective laser melting: spatial detection of defects via image data analysis. J Manuf Sci Eng Trans ASME2017; 139: 051001.
28.
ScimeLBeuthJ. Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm. Addit Manuf2018; 19: 114–126.
29.
TaheriHKoesterLBigelowTFaiersonEBondL. In situ additive manufacturing process monitoring with an acoustic technique: Clustering performance evaluation using K-means algorithm. J Manuf Sci Eng Trans ASME2019; 141: 1–10.
30.
LuoSMaXXuJ, et al. Deep learning based monitoring of spatter behavior by the acoustic signal in selective laser melting. Sensors2021; 21: 7179.
31.
HossainMTaheriH. In-situ process monitoring for metal additive manufacturing through acoustic techniques using wavelet and convolutional neural network (CNN). Int J Adv Manuf Technol2021; 116: 3473–3488.
32.
LiuSJiaKWanH, et al. Inspection of the internal defects with different size in Ni and Ti additive manufactured components using laser ultrasonic technology. Optics Laser Technol2022; 146: 107543.
33.
LiFYuZYangZ, et al. Real-time distortion monitoring during fused deposition modeling via acoustic emission. Struct Health Monit2020; 19: 412–423.
34.
EschnerNWeiserLHäfnerB, et al. Classification of specimen density in Laser Powder Bed Fusion (L-PBF) using in-process structure-borne acoustic process emissions. Addit Manuf2020; 34: 101324.
35.
AbouelNourYGuptaN. In-situ monitoring of sub-surface and internal defects in additive manufacturing: a review. Mat Design2022; 222: 111063.
36.
CaggianoAZhangJAlfieriV, et al. Machine learning-based image processing for on-line defect recognition in additive manufacturing. CIRP Ann2019; 68: 451–454.
37.
SnowZReutzelEPetrichJ. Correlating in-situ sensor data to defect locations and part quality for additively manufactured parts using machine learning. J Mat Process Technol2022; 302: 117476.
38.
MazzoleniLDemirACaprioL, et al. Real-time observation of melt pool in selective laser melting: spatial, temporal, and wavelength resolution criteria. IEEE Trans Instrum Measur2020; 69: 1179–1190.
39.
BugattiMColosimoB. Towards real-time in-situ monitoring of hot-spot defects in L-PBF: a new classification-based method for fast video-imaging data analysis. J Intell Manuf2022; 33: 293–309.
40.
DemirKZhangZBen-ArtzyA, et al. Laser scan strategy descriptor for defect prognosis in metal additive manufacturing using neural networks. J Manuf Process2021; 67: 628–634.
41.
ZhuQLiuZYanJ. Machine learning for metal additive manufacturing: predicting temperature and melt pool fluid dynamics using physics-informed neural networks. Comput Mech2021; 67: 619–635.
42.
MahmoudiMEzzatAElwanyA. Layerwise anomaly detection in laser powder-bed Fusion metal additive manufacturing. J Manuf Sci Eng Trans ASME2019; 141: 1–13.
43.
YeZLiuCTianWKanC. In-situ point cloud fusion for layer-wise monitoring of additive manufacturing. J Manuf Syst2021; 61: 210–222.
44.
GaikwadAWilliamsRWintonH, et al. Multi phenomena melt pool sensor data fusion for enhanced process monitoring of laser powder bed fusion additive manufacturing. Mat Design2022; 221: 110919.
45.
LeeHHeoghWYangJ, et al. Deep learning for in-situ powder stream fault detection in directed energy deposition process. J Manuf Syst2022; 62: 575–587.
46.
MahatoVObeidiMBrabazonD, et al. Detecting voids in 3D printing using melt pool time series data. J Intell Manuf2020; 33: 845–852.
47.
ReischRHauserTLutzB, et al. A Context awareness in process monitoring of additive manufacturing using a digital twin. Int J Adv Manuf Technol2022; 119: 3483–3500.
48.
TanYJinBNettekovenA, et al. An encoder-decoder based approach for anomaly detection with application in additive manufacturing. In: Proceedings – 18th IEEE international conference on machine learning and applications, ICMLA 2019, 2019: 1008–1015. New York: IEEE.
49.
AkhavanJManoochehriS. Sensory data fusion using machine learning methods for in-situ defect registration in additive manufacturing: a review. In: 2022 IEEE international IOT, electronics and mechatronics conference, IEMTRONICS 2022, 2022. New York: IEEE.
50.
ZhangYHongGYeD, et al. Extraction and evaluation of melt pool, plume and spatter information for powder-bed fusion AM process monitoring. Mat Design2018; 156: 458–469.
51.
GaikwadAYavariRMontazeriM, et al. Toward the digital twin of additive manufacturing: Integrating thermal simulations, sensing, and analytics to detect process faults. IISE Trans2020; 52: 1204–1217.
52.
PandiyanVDrissi-DaoudiRShevchikS, et al. Deep transfer learning of additive manufacturing mechanisms across materials in metal-based laser powder bed fusion process. J Mat Process Technol2022; 303: 117531.
53.
LiuPYangLYiK, et al. Application of nonlinear ultrasonic analysis for in situ monitoring of metal additive manufacturing. Struct Health Monit2022; 22: 1760–1775.
54.
ChenAKopsaftopoulosFMishraS. Unsupervised online anomaly detection of metal additive manufacturing processes via a statistical time-frequency domain approach. ASME 2022 international mechanical engineering congress and exposition. Vol. 1, Acoustics, Vibration, and Phononics, 2022. ASME.
55.
ShkorutaACaynoskiWMishraS, et al. Iterative learning control for power profile shaping in selective laser melting. In: IEEE international conference on automation science and engineering2019, pp. 655–660. New York: IEEE.
