Hybrid thermal modeling of additive manufacturing processes using physics-informed neural networks for temperature prediction and parameter identification

Liao, Shuheng; Xue, Tianju; Jeong, Jihoon; Webster, Samantha; Ehmann, Kornel F.; Cao, Jian

doi:10.1007/s00466-022-02257-9

Cited by 18 publications

(3 citation statements)

References 31 publications

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“…Finally, in the realm of algorithms, exploring time-series algorithms like recurrent neural networks (RNNs) and long shortterm memory (LSTM) networks is advisable because the distribution of residual stresses is essentially time-dependent. Moreover, given the complexity and the need for explainable AI models, the investigation of the use of physics-informed neural networks (PINNs) for prediction of residual stresses holds significant promise [128]. PINNs integrate physics and governing equations directly into the machine learning model.…”

Section: Future Trendsmentioning

confidence: 99%

Experimental, Computational, and Machine Learning Methods for Prediction of Residual Stresses in Laser Additive Manufacturing: A Critical Review

Wu,

Tariq,

Joy

et al. 2024

Materials

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In recent decades, laser additive manufacturing has seen rapid development and has been applied to various fields, including the aerospace, automotive, and biomedical industries. However, the residual stresses that form during the manufacturing process can lead to defects in the printed parts, such as distortion and cracking. Therefore, accurately predicting residual stresses is crucial for preventing part failure and ensuring product quality. This critical review covers the fundamental aspects and formation mechanisms of residual stresses. It also extensively discusses the prediction of residual stresses utilizing experimental, computational, and machine learning methods. Finally, the review addresses the challenges and future directions in predicting residual stresses in laser additive manufacturing.

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Section: Future Trendsmentioning

confidence: 99%

Experimental, Computational, and Machine Learning Methods for Prediction of Residual Stresses in Laser Additive Manufacturing: A Critical Review

Wu,

Tariq,

Joy

et al. 2024

Materials

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“…wave propagation [86], nano-optics [87], AM [88], and biomaterials [89]. To facilitate the usage of PINNs in the research community, Lu et al implemented various PINN algorithms in an open-source Python library called DeepXDE [90].…”

Section: Physics-informed Neural Network (Pinns)mentioning

confidence: 99%

Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review

Jin¹,

Zhang²,

Espinosa³

2023

Preprint

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For many decades, experimental solid mechanics has played a crucial role in characterizing and understanding the mechanical properties of natural and novel materials. Recent advances in machine learning (ML) provide new opportunities for the field, including experimental design, data analysis, uncertainty quantification, and inverse problems. As the number of papers published in recent years in this emerging field is exploding, it is timely to conduct a comprehensive and up-to-date review of recent ML applications in experimental solid mechanics. Here, we first provide an overview of common ML algorithms and terminologies that are pertinent to this review, with emphasis placed on physics-informed and physics-based ML methods. Then, we provide thorough coverage of recent ML applications in traditional and emerging areas of experimental mechanics, including fracture mechanics, biomechanics, nano-and micro-mechanics, architected materials, and 2D material. Finally, we highlight some current challenges of applying ML to multi-modality and multi-fidelity experimental datasets and propose several future research directions. This review aims to provide valuable insights into the use of ML methods as well as a variety of examples for researchers in solid mechanics to integrate into their experiments.

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“…Go [40] proposed a model for simulating real-time temperature fields based on the PINN, which is capable of accurately estimating the temperature distribution and heat flux of a given heat source, even with limited temperature data. A PINN-based hybrid thermal model was developed by Liao [41], which utilizes partially observable data obtained by an infrared camera to predict the temperature distribution and estimate the unknown material and process parameters that are not directly observable. Zhang [42] divided the composite material into multiple subdomains and then used PINN to acquire the temperature distribution.…”

Section: Introductionmentioning

confidence: 99%

Physics-Informed Neural Network (PINN) for Solving Frictional Contact Temperature and Inversely Evaluating Relevant Input Parameters

Xia,

Meng

2024

Lubricants

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Ensuring precise prediction, monitoring, and control of frictional contact temperature is imperative for the design and operation of advanced equipment. Currently, the measurement of frictional contact temperature remains a formidable challenge, while the accuracy of simulation results from conventional numerical methods remains uncertain. In this study, a PINN model that incorporates physical information, such as partial differential equation (PDE) and boundary conditions, into neural networks is proposed to solve forward and inverse problems of frictional contact temperature. Compared to the traditional numerical calculation method, the preprocessing of the PINN is more convenient. Another noteworthy characteristic of the PINN is that it can combine data to obtain a more accurate temperature field and solve inverse problems to identify some unknown parameters. The experimental results substantiate that the PINN effectively resolves the forward problems of frictional contact temperature when provided with known input conditions. Additionally, the PINN demonstrates its ability to accurately predict the friction temperature field with an unknown input parameter, which is achieved by incorporating a limited quantity of easily measurable actual temperature data. The PINN can also be employed for the inverse identification of unknown parameters. Finally, the PINN exhibits potential in solving inverse problems associated with frictional contact temperature, even when multiple input parameters are unknown.

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Hybrid thermal modeling of additive manufacturing processes using physics-informed neural networks for temperature prediction and parameter identification

Cited by 18 publications

References 31 publications

Experimental, Computational, and Machine Learning Methods for Prediction of Residual Stresses in Laser Additive Manufacturing: A Critical Review

Experimental, Computational, and Machine Learning Methods for Prediction of Residual Stresses in Laser Additive Manufacturing: A Critical Review

Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review

Physics-Informed Neural Network (PINN) for Solving Frictional Contact Temperature and Inversely Evaluating Relevant Input Parameters

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