Toward the digital twin of additive manufacturing: Integrating thermal simulations, sensing, and analytics to detect process faults

Gaikwad, Aniruddha; Yavari, Reza; Montazeri, Mohammad; Cole, Kevin D.; Bian, Linkan; Rao, Prahalada

doi:10.1080/24725854.2019.1701753

Cited by 136 publications

(57 citation statements)

References 31 publications

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“…Supervised learning algorithms refer to machine learning methods in which models are trained using labels. Typical supervised learning methods used in digital twin include supper vector machine (SVM) [ 195 , 201 ], decision trees [ 86 , 93 , 94 ], k-nearest neighbors [ 102 ], convolutional neural networks (CNN) [ 103 , 135 , 202 , 206 , 270 ] and recurrent neural networks (RNN) [ 202 ]. In practice, data labeling can be an expensive task.…”

Section: Discussionmentioning

confidence: 99%

“…For advanced metal AM and laser material processing techniques, e.g., laser powder bed fusion (LPBF) and laser melting deposition (LMD), research efforts focus on the subtle impacts of thermal effects on materials, such as the microstructure and parts’ distortion, during such non-contact processes. As Gaikward et al in [ 201 ] presented the temperature distribution of parts, predicted based on a graph–theoretical computational heat transfer approach and subsequently combined it with an SVM model in order to detect potential quality faults in printing processes. Another attempt of grey box modeling for build quality in dependency of process parameters and in situ sensor signatures was proposed in [ 202 ] by Gaikward et al, where the a priori knowledge of physical processes was incorporated into three sequentially connected shallow ANNs and consequently achieved better performance in comparison with purely data-driven methods (CNN, LSTM, RNN, among others).…”

Section: Sustainable Resilient Manufacturingmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Huang

Shen²,

et al. 2021

Sensors

190

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Digital twin (DT) and artificial intelligence (AI) technologies have grown rapidly in recent years and are considered by both academia and industry to be key enablers for Industry 4.0. As a digital replica of a physical entity, the basis of DT is the infrastructure and data, the core is the algorithm and model, and the application is the software and service. The grounding of DT and AI in industrial sectors is even more dependent on the systematic and in-depth integration of domain-specific expertise. This survey comprehensively reviews over 300 manuscripts on AI-driven DT technologies of Industry 4.0 used over the past five years and summarizes their general developments and the current state of AI-integration in the fields of smart manufacturing and advanced robotics. These cover conventional sophisticated metal machining and industrial automation as well as emerging techniques, such as 3D printing and human–robot interaction/cooperation. Furthermore, advantages of AI-driven DTs in the context of sustainable development are elaborated. Practical challenges and development prospects of AI-driven DTs are discussed with a respective focus on different levels. A route for AI-integration in multiscale/fidelity DTs with multiscale/fidelity data sources in Industry 4.0 is outlined.

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Section: Discussionmentioning

confidence: 99%

Section: Sustainable Resilient Manufacturingmentioning

confidence: 99%

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Huang

Shen²,

et al. 2021

Sensors

190

View full text Add to dashboard Cite

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“…Some research has focused on the identification of the main features that should be included in the cyber-physical world (e.g. thermal behavior, melt-pool dynamics, distortions, geometry prediction) [16,17]. However, one of the major limitations of the current models is the large amount of computational resources associated to their use, which make them impractical for real-time or interactive applications [16].…”

Section: Digital Twins In Additive Manufacturingmentioning

confidence: 99%

2D linear finite element simulation of laser metal heating for digital twins

Montoya-Zapata

Rodríguez

Moreno

et al. 2021

Int. J. Simul. Multidisci. Des. Optim.

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In the context of laser-based additive manufacturing, the thermal behavior of the substrate is relevant to define process parameters vis-à-vis piece quality. The existing literature focuses on two process variables: (a) lumped laser power and (b) process speed. However, this literature does not consider other variables, such as those related to the laser power distribution. To fill this vacuum, this manuscript includes the laser power spatial distributions (Gaussian, uniform circular and uniform rectangular) in addition to (a) and (b) above in 2D linear substrate heating simulations. The laser energy is modeled as a time dependent heat flux boundary condition on top of the domain. The total laser delivered power was identical for all spatial distributions. The results show that the laser intensity spatial distribution strongly affects the maximum temperature, and the depth and width of the heat affected zone. These 2D finite element simulations prove to be good options for digital twin based design environments, due to their simplicity and reasonable temperature error, compared to non-linear analysis (considered as ground truth for this case). Future publications address non-linear finite element simulations of the laser heating process (including convection and radiation and temperature dependent substrate properties).

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“…Overall, the ultimate aim is to contribute to the in-situ detection of process-induced defects for improving part quality and even assisting in efforts to create a digital twin of AM processes (Redding et al, 2017;Gaikwad et al, 2020). At optimal process-parameters, L-PBF of Ti6Al4V ELI samples can show very high density (>99.9%) and only randomly distributed small pores (Yadroitsev et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Monitoring of Laser Powder Bed Fusion by Acoustic Emission: Investigation of Single Tracks and Layers

et al. 2021

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Quality concerns in laser powder bed fusion (L-PBF) include porosity, residual stresses and deformations during processing. Single tracks are the fundamental building blocks in L-PBF and their shape and geometry influence subsequent porosity in 3D L-PBF parts. The morphology of single tracks depends primarily on process parameters. The purpose of this paper is to demonstrate an approach to acoustic emission (AE) online monitoring of the L-PBF process for indirect defect analysis. This is demonstrated through the monitoring of single tracks without powder, with powder and in layers. Gas-borne AE signals in the frequency range of 2–20 kHz were sampled using a microphone placed inside the build chamber of a L-PBF machine. The single track geometry and shape at different powder thickness values and laser powers were studied together with the corresponding acoustic signals. Analysis of the acoustic signals allowed for the identification of characteristic amplitudes and frequencies, with promising results that support its use as a complementary method for in-situ monitoring and real-time defect detection in L-PBF. This work proves the capability to directly detect the balling effect that strongly affects the formation of porosity in L-PBF parts by AE monitoring.

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Toward the digital twin of additive manufacturing: Integrating thermal simulations, sensing, and analytics to detect process faults

Cited by 136 publications

References 31 publications

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

2D linear finite element simulation of laser metal heating for digital twins

Monitoring of Laser Powder Bed Fusion by Acoustic Emission: Investigation of Single Tracks and Layers

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