利用物理信息神经网络预测生物材料支架冷冻铸造中的定向凝固。

IF 1.3 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Biomedical Physics & Engineering Express Pub Date : 2024-09-11 DOI:10.1088/2057-1976/ad7960
Amir Rouhollahi,Milad Rismanian,Amin Ebrahimi,Olusegun J Ilegbusi,Farhad R Nezami
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引用次数: 0

摘要

冷冻铸造是一种广泛应用于生物医学领域的制造技术,用于制造生物材料支架,由于其高度非线性行为和复杂的工艺参数相互作用,为预测定向凝固带来了挑战。传统的数值方法,如计算流体动力学(CFD),需要充分和准确的边界条件知识,由于技术限制,限制了其在真实世界瞬态凝固应用中的实用性。在本研究中,我们通过开发物理信息神经网络(PINNs)模型来预测冷冻铸造工艺中的定向凝固,从而应对这一挑战。PINNs 模型将物理约束与神经网络预测相结合,与 CFD 相比,需要的预定边界条件大大减少。通过与 CFD 模拟的比较,PINNs 模型在预测温度分布和凝固模式方面的准确性不相上下。这一前景广阔的模型仅用了 5000 个空间和时间数据点(相当于 25 万个时间步)就取得了这样的性能,展示了其高精度预测凝固动态的能力。这项研究的主要贡献在于深入揭示了冻铸支架制造过程中的凝固模式,有助于设计具有微调微结构的生物材料支架,这对各种组织工程应用至关重要。此外,PINNs 模型降低了计算要求,为支架制造节省了潜在的成本和时间,有望推动生物医学工程的研究和发展。
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Prediction of directional solidification in freeze casting of biomaterial scaffolds using physics-informed neural networks.
Freeze casting, a manufacturing technique widely applied in biomedical fields for fabricating biomaterial scaffolds, poses challenges for predicting directional solidification due to its highly nonlinear behavior and complex interplay of process parameters. Conventional numerical methods, such as computational fluid dynamics (CFD), require adequate and accurate boundary condition knowledge, limiting their utility in real-world transient solidification applications due to technical limitations. In this study, we address this challenge by developing a physics-informed neural networks (PINNs) model to predict directional solidification in freeze-casting processes. The PINNs model integrates physical constraints with neural network predictions, requiring significantly fewer predetermined boundary conditions compared to CFD. Through a comparison with CFD simulations, the PINNs model demonstrates comparable accuracy in predicting temperature distribution and solidification patterns. This promising model achieves such a performance with only 5000 data points in space and time, equivalent to 250,000 timesteps, showcasing its ability to predict solidification dynamics with high accuracy. The study's major contributions lie in providing insights into solidification patterns during freeze-casting scaffold fabrication, facilitating the design of biomaterial scaffolds with finely tuned microstructures essential for various tissue engineering applications. Furthermore, the reduced computational demands of the PINNs model offer potential cost and time savings in scaffold fabrication, promising advancements in biomedical engineering research and development.
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来源期刊
Biomedical Physics & Engineering Express
Biomedical Physics & Engineering Express RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
2.80
自引率
0.00%
发文量
153
期刊介绍: BPEX is an inclusive, international, multidisciplinary journal devoted to publishing new research on any application of physics and/or engineering in medicine and/or biology. Characterized by a broad geographical coverage and a fast-track peer-review process, relevant topics include all aspects of biophysics, medical physics and biomedical engineering. Papers that are almost entirely clinical or biological in their focus are not suitable. The journal has an emphasis on publishing interdisciplinary work and bringing research fields together, encompassing experimental, theoretical and computational work.
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