Full Text:   <2985>

Summary:  <1627>

Suppl. Mater.: 

CLC number: TP183

On-line Access: 2021-08-20

Received: 2020-09-08

Revision Accepted: 2021-02-24

Crosschecked: 2021-07-20

Cited: 0

Clicked: 3944

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Shivam Sharma

https://orcid.org/0000-0003-4148-1624

Pattabhi Ramaiah Budarapu

https://orcid.org/0000-0001-9884-1622

-   Go to

Article info.
Open peer comments

Journal of Zhejiang University SCIENCE A 2021 Vol.22 No.8 P.621-631

http://doi.org/10.1631/jzus.A2000403


Physics-informed neural networks for estimating stress transfer mechanics in single lap joints


Author(s):  Shivam Sharma, Rajneesh Awasthi, Yedlabala Sudhir Sastry, Pattabhi Ramaiah Budarapu

Affiliation(s):  School of Mechanical Sciences, Indian Institute of Technology, Bhubaneswar 752050, India; more

Corresponding email(s):   pattabhi@iitbbs.ac.in

Key Words:  Physics-informed neural networks (PINNs), Algorithmic differentiation, Artificial neural networks, Loss function, Single lap joint


Shivam Sharma, Rajneesh Awasthi, Yedlabala Sudhir Sastry, Pattabhi Ramaiah Budarapu. Physics-informed neural networks for estimating stress transfer mechanics in single lap joints[J]. Journal of Zhejiang University Science A, 2021, 22(8): 621-631.

@article{title="Physics-informed neural networks for estimating stress transfer mechanics in single lap joints",
author="Shivam Sharma, Rajneesh Awasthi, Yedlabala Sudhir Sastry, Pattabhi Ramaiah Budarapu",
journal="Journal of Zhejiang University Science A",
volume="22",
number="8",
pages="621-631",
year="2021",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A2000403"
}

%0 Journal Article
%T Physics-informed neural networks for estimating stress transfer mechanics in single lap joints
%A Shivam Sharma
%A Rajneesh Awasthi
%A Yedlabala Sudhir Sastry
%A Pattabhi Ramaiah Budarapu
%J Journal of Zhejiang University SCIENCE A
%V 22
%N 8
%P 621-631
%@ 1673-565X
%D 2021
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A2000403

TY - JOUR
T1 - Physics-informed neural networks for estimating stress transfer mechanics in single lap joints
A1 - Shivam Sharma
A1 - Rajneesh Awasthi
A1 - Yedlabala Sudhir Sastry
A1 - Pattabhi Ramaiah Budarapu
J0 - Journal of Zhejiang University Science A
VL - 22
IS - 8
SP - 621
EP - 631
%@ 1673-565X
Y1 - 2021
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A2000403


Abstract: 
With the explosive growth of computational resources and data generation, deep machine learning has been successfully employed in various applications. One important and emerging scientific application of deep learning involves solving differential equations. Here, physics-informed neural networks (PINNs) are developed to solve the differential equations associated with a specific scientific problem. As such, algorithms for solving the differential equations by embedding their initial and boundary conditions in the cost function of the artificial neural networks using algorithmic differentiation must also be developed. In this study, various PINNs are adopted to estimate the stresses in the tablets and the interphase of a single lap joint. The proposed model is represented by two fourth-order non-homogeneous coupled partial differential equations, with the axial stresses in the upper and lower tablets adopted as the dependent variables. The axial stresses are a function of the tablet length, which presents the independent variable. Therefore, the axial stresses in the tablets are estimated by solving the coupled partial differential equations when subjected to the boundary conditions, whereas the remaining stress components are expressed in terms of axial stresses. The results obtained using the developed methodology are validated using the results obtained via MAPLE software.

用于评估单搭接接头应力传递的物理神经网络

目的:开发物理神经网络,研究单搭接接头的应力传递机理.
创新点:1. 创建了一种新的基于物理神经网络(PINN)的深度机器学习(DML)方法来求解两个非齐次耦合四阶偏微分方程.2. 通过将开发的方法和闭合解(由MAPLE软件获得)进行对比,验证了结果的可靠性.
方法:1. 通过包含1个输入层、2到3个隐藏层和1个输出层的人工神经网络(ANN)实现本文提出的基于PINN的DML方法.2. 将边界和初始条件以及搭接接头组成部分的材料特性提供给输入层,在隐藏层中计算损失函数,并从输出层提取满足边界条件的σ1σ3应力值.
结论:1. 通过基于DML框架的PINN方法研究单个搭接接头的力学,以及对受边界条件影响的耦合四阶非齐次偏微分方程的求解,所提方法可被扩展到多基板及其相间的各种应力分量的估计.2. 通过用所提方法估计界面剪切应力并将其与精确解对比发现,基于DML的方法获得的结果可有效表征物理行为.

关键词:物理信息神经网络;算法微分;人工神经网络;损失函数;单搭接接头

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Abueidda DW, Almasri M, Ammourah R, et al., 2019. Prediction and optimization of mechanical properties of composites using convolutional neural networks. Composite Structures, 227:111264.

[2]Adams RD, Mallick V, 1992. A method for the stress analysis of lap joints. The Journal of Adhesion, 38(3-4):199-217.

[3]Ajayan PM, Schadler LS, Braun PV, 2003. Nanocomposite Science and Technology. Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, Germany.

[4]Anitescu C, Atroshchenko E, Alajlan N, et al., 2019. Artificial neural network methods for the solution of second order boundary value problems. Computers, Materials & Continua, 59(1):345-359.

[5]Barile C, Casavola C, Moramarco V, et al., 2020. Bonding characteristics of single- and joggled-lap CFRP specimens: mechanical and acoustic investigations. Applied Sciences, 10(5):1782.

[6]Barthelat F, 2014. Designing nacre-like materials for simultaneous stiffness, strength and toughness: optimum materials, composition, microstructure and size. Journal of the Mechanics and Physics of Solids, 73:22-37.

[7]Barthelat F, Espinosa HD, 2007. An experimental investigation of deformation and fracture of nacre–mother of pearl. Experimental Mechanics, 47(3):311-324.

[8]Barthelat F, Tang H, Zavattieri PD, et al., 2007. On the mechanics of mother-of-pearl: a key feature in the material hierarchical structure. Journal of the Mechanics and Physics of Solids, 55(2):306-337.

[9]Beliaev M, Zöllner D, Pacureanu A, et al., 2020. Quantification of sheet nacre morphogenesis using X-ray nanotomography and deep learning. Journal of Structural Biology, 209(1):107432.

[10]Bertoldi K, Bigoni D, Drugan WJ, 2008. Nacre: an orthotropic and bimodular elastic material. Composites Science and Technology, 68(6):1363-1375.

[11]Budarapu PR, Yb SS, Javvaji B, et al., 2014. Vibration analysis of multi-walled carbon nanotubes embedded in elastic medium. Frontiers of Structural and Civil Engineering, 8(2):151-159.

[12]Budarapu PR, Narayana TSS, Rammohan B, et al., 2015. Directionality of sound radiation from rectangular panels. Applied Acoustics, 89:128-140.

[13]Budarapu PR, Kumar S, Prusty BG, et al., 2019. Stress transfer through the interphase in curved-fiber pullout tests of nanocomposites. Composites Part B: Engineering, 165:417-434.

[14]Chen CT, Gu GX, 2019. Machine learning for composite materials. MRS Communications, 9(2):556-566.

[15]Chen M, Mao SW, Zhang Y, et al., 2014. Big data generation and acquisition. In: Chen M, Mao SW, Zhang Y, et al. (Eds.), Big Data: Related Technologies, Challenges and Future Prospects. Springer, Cham, Switzerland, p.19-32.

[16]Du GL, Mao AR, Yu JH, et al., 2019. Nacre-mimetic composite with intrinsic self-healing and shape-programming capability. Nature Communications, 10(1):800.

[17]Espinosa HD, Rim JE, Barthelat F, et al., 2009. Merger of structure and material in nacre and bone–perspectives on de novo biomimetic materials. Progress in Materials Science, 54(8):1059-1100.

[18]Fang ZW, Zhan J, 2020. A physics-informed neural network framework for PDEs on 3D surfaces: time independent problems. IEEE Access, 8:26328-26335.

[19]Gim J, Schnitzer N, Otter LM, et al., 2019. Nanoscale deformation mechanics reveal resilience in nacre of Pinna nobilis shell. Nature Communications, 10(1):4822.

[20]Goswami S, Anitescu C, Chakraborty S, et al., 2020. Transfer learning enhanced physics informed neural network for phase-field modeling of fracture. Theoretical and Applied Fracture Mechanics, 106:102447.

[21]Gunes R, Apalak MK, Yildirim M, 2011. Free vibration analysis of an adhesively bonded functionally graded tubular single lap joint. The Journal of Adhesion, 87(9):902-925.

[22]Guo HW, Zhuang XY, Rabczuk T, 2019. A deep collocation method for the bending analysis of Kirchhoff plate. Computers, Materials & Continua, 59(2):433-456.

[23]Gupta S, Modgil S, Gunasekaran A, 2020. Big data in lean six sigma: a review and further research directions. International Journal of Production Research, 58(3):947-969.

[24]Gupta TK, Budarapu PR, Chappidi SR, et al., 2019. Advances in carbon based nanomaterials for bio-medical applications. Current Medicinal Chemistry, 26(38):6851-6877.

[25]Her SC, Chan CF, 2019. Interfacial stress analysis of adhesively bonded lap joint. Materials, 12(15):2403.

[26]Jackson AP, Vincent JFV, Turner RM, 1988. The mechanical design of nacre. Proceedings of the Royal Society B Biological Sciences, 234(1277):415-440.

[27]Kadeethum T, Jørgensen TM, Nick HM, 2020. Physics-informed neural networks for solving nonlinear diffusivity and biot’s equations. PLoS One, 15(5):e0232683.

[28]Lambiase F, Grossi V, Paoletti A, 2020. Machine learning applied for process design of hybrid metal-polymer joints. Journal of Manufacturing Processes, 58:92-100.

[29]Magrini T, Bouville F, Lauria A, et al., 2019. Transparent and tough bulk composites inspired by nacre. Nature Communications, 10(1):2794.

[30]Morsali S, Qian D, Minary-Jolandan M, 2020. Designing bioinspired brick-and-mortar composites using machine learning and statistical learning. Communications Materials, 1(1):12.

[31]Ni Y, Song ZQ, Jiang HY, et al., 2015. Optimization design of strong and tough nacreous nanocomposites through tuning characteristic lengths. Journal of the Mechanics and Physics of Solids, 81:41-57.

[32]Oh WB, Yun TJ, Lee BR, et al., 2019. A study on intelligent algorithm to control welding parameters for lap-joint. Procedia Manufacturing, 30:48-55.

[33]Pan GR, Yao YM, Zeng XL, et al., 2017. Learning from natural nacre: constructing layered polymer composites with high thermal conductivity. ACS Applied Materials & Interfaces, 9(38):33001-33010.

[34]Raissi M, Perdikaris P, Karniadakis GE, 2019. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 378:686-707.

[35]Rangaswamy H, Sogalad I, Basavarajappa S, et al., 2020. Experimental analysis and prediction of strength of adhesive-bonded single-lap composite joints: Taguchi and artificial neural network approaches. SN Applied Sciences, 2(6):1055.

[36]Reinoso J, Durand P, Budarapu PR, et al., 2019. Crack patterns in heterogenous rocks using a combined phase field-cohesive interface modeling approach: a numerical study. Energies, 12(6):965.

[37]Sacco C, Radwan AB, Anderson A, et al., 2020. Machine learning in composites manufacturing: a case study of automated fiber placement inspection. Composite Structures, 250:112514.

[38]Samaniego E, Anitescu C, Goswami S, et al., 2020. An energy approach to the solution of partial differential equations in computational mechanics via machine learning: concepts, implementation and applications. Computer Methods in Applied Mechanics and Engineering, 362:112790.

[39]Thongprayoon C, Kaewput W, Kovvuru K, et al., 2020. Promises of big data and artificial intelligence in nephrology and transplantation. Journal of Clinical Medicine, 9(4):1107.

[40]Tosun E, Çalık A, 2016. Failure load prediction of single lap adhesive joints using artificial neural networks. Alexandria Engineering Journal, 55(2):1341-1346.

[41]Waheed H, Hassan SU, Aljohani NR, et al., 2020. Predicting academic performance of students from VLE big data using deep learning models. Computers in Human Behavior, 104:106189.

[42]Wang CH, Rose LRF, 2003. Stress analysis and failure assessment of lap joints. In: Tong LY, Soutis C (Eds.), Recent Advances in Structural Joints and Repairs for Composite Materials. Springer, Dordrecht, the Netherlands, p.1-26.

[43]Xu D, Liu PF, Li JG, et al., 2019. Damage mode identification of adhesive composite joints under hygrothermal environment using acoustic emission and machine learning. Composite Structures, 211:351-363.

[44]Yadav N, Yadav A, Kumar M, 2015. An Introduction to Neural Network Methods for Differential Equations. Springer, Dordrecht, the Netherlands.

[45]Yang RJ, Yu L, Zhao YJ, et al., 2020. Big data analytics for financial Market volatility forecast based on support vector machine. International Journal of Information Management, 50:452-462.

[46]Ye S, Li B, Li QY, et al., 2019. Deep neural network method for predicting the mechanical properties of composites. Applied Physics Letters, 115(16):161901.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE