Effect of Adhesive Layer Thickness on Traction Separation Law for Mode-I and Mode-II Fracture.pptx

Effect of Adhesive Layer Thickness on Traction Separation Law for Mode-I and Mode-II Fracture Research Paper

1. Effect of Adhesive Layer Thickness on Traction Separation Law for Mode-I and Mode-II Fracture Department of Mechanical Engineering Indian Institute of Technology, Patna Presented by Mukesh Singh Under the supervision of Dr. Akhilendra Das

2. Table of Contents Background and Motivation Literature Review Problem Statement Objectives Theoretical Background Methodology Results Work Plan References

3. Background and Motivation Fracture mechanics is a fundamental field in materials science and engineering, and understanding how adhesive layer thickness affects fracture mechanics is crucial for applications ranging from aerospace engineering to adhesive material design. Traditionally, materials were joined using bolts and rivets. While these methods are quite effective in most scenarios, all of them face the issue of high stress concentrations at the joints. This is where adhesive joints are useful since they provide reduced stress concentration and a more even distribution of stress over the bonded area [4] . Further, they are also lighter than bolts or rivets, aiding in creating lightweight structures. Fig. 1 Finite element analysis of stress distribution in a bolted joint

4. Literature Review Linear Elastic Fracture Mechanics predicts infinity stress at the crack tip which is not the case in real life. Cohesive Zone Model is mathematical model which addresses this problem. Traction Separation Law along with the crack length information can be used to obtain fracture (G C ) energy which is useful for determining condition for unstable crack propagation. Most studies use equivalent crack length to calculate (G C ) due to the complexities of measuring actual crack length. We aim to make use of CNN for measuring the crack length information and calculate (G C ).

5. Problem Statement Analysing the impact of adhesive layer thickness on the Traction Separation Laws governing Mode-I and Mode-II fractures while employing Convolutional Neural Network (CNN) as a tool.

6. Objectives To estimate crack path and determine the crack length using Convolution Neural Network. To determine the fracture parameters and unstable crack propagation condition using the crack length obtained from CNN.

7. Mode-I and Mode-II Fracture Two fundamental modes of fracture, Mode I (tensile) and Mode II (shearing), are of particular interest. Mode-I fracture occurs when the crack propagates by opening or extending perpendicular to the direction of the applied tensile stress. Mode-II fracture occurs when the crack propagates parallel to the direction of the applied shear stress, resulting in sliding or shear displacement along the crack plane. Theoretical Background Fig. 2 Fracture Modes

8. Traction-Separation Law Traction : This refers to the stress acting on the crack surfaces, often characterized by components such as normal and shear stresses. Separation : This represents the relative displacement or opening between the crack surfaces. TSLs provide critical information about the forces required to initiate and propagate a crack. When the applied stress exceeds the traction described by the TSLs, crack growth occurs. TSLs help identify critical parameters like energy release rates, which are used to assess the stability of a crack. Theoretical Background Fig. 3 Cohesive Zone Model and Traction Separation Law

9. Overview of the adopted methodology Methodology

10. Adhesive Layer Thickness 0.3 mm 0.5 mm 1 mm 2 mm Experimental Details Fig 4

11. Machine-Learning Based Approach Machine learning models are a popular and effective approach when dealing with computer vision tasks where pattern recognition is involved. The particular problem that we are looking to solve is classified as an “image segmentation” problem. x Fig. 5 Sample output of CNN

12. Neural Networks Fig. 6 Representation of a neural network

13. ParallelNets Architecture Fig. 7 ParallelNets Architecture ParallelNets = U-Net + Deep Regression (Crack-tip detection) (Crack-path segmentation)

14. Sample Preparation Sample preparation using aluminum specimen and aerospace grade adhesive Surface roughening - For better adhesive bonding Acetone cleaning - To remove residue of adherent after surface roughening Surface waxing - To ease the process of removal of extra adhesive Adhesive layer formation - Usage of shim to maintain the desired adhesive layer thickness

15. CNN - Initial Training Results Training Results Training Time: 54 min Loss: 14.0477 Dice Loss: 0.9990 MSE Loss: 0.13048667 Deviation: 46.35 Reliability: 1.00 Test Results Deviation (mean) in mm: 11.556465775895484 Deviation (std) in mm: 4.75994907958686 Dice coefficient: 0.001019074888286453 Reliability in %: 99.63250306247447 Training Results (new) Training Time: 253 min Loss: 2.3633 Dice Loss: 0.9659 MSE Loss: 0.01397440 Deviation: 26.99 Reliability: 0.78

16. Work Plan

17. References 1. F. Czerwinski (2021): Current Trends in Automotive Lightweighting Strategies and Materials, Materials 2021, 14, 6631. 2. Alam, M.A. et al. (2022): Recent Advancements in Advanced Composites for Aerospace Applications: A Review. In: Mazlan, N., Sapuan, S., Ilyas, R. (eds) Advanced Composites in Aerospace Engineering Applications. Springer, Cham. 3. G. Jeevi, Sanjay Kumar Nayak & M. Abdul Kader (2019): Review on adhesive joints and their application in hybrid composite structures, Journal of Adhesion Science and Technology, 33:14, 1497-1520. 4. Barbosa, N.G.C., Campilho, R.D.S.G., Silva, F.J.G. et al. (2018): Comparison of different adhesively-bonded joint types for mechanical structures. Appl Adhes Sci 6, 15. 5. Watson, B., Nandwani, Y., Worswick, M.J. and Cronin, D.S. (2019): Metallic multi-material adhesive joint testing and modeling for vehicle lightweighting, International Journal of Adhesion and Adhesives, Vol. 95, pp. 102421. 6. Liu, X., Shao, X., Li, Q. and Sun, G. (2019): Experimental study on residual properties of carbon fibre reinforced plastic (CFRP) and aluminum single-lap adhesive joints at different strain rates after transverse pre-impact, Composites Part A: Applied Science and Manufacturing, Vol. 124, pp. 105372. 7. Liu, X., Shao, X., Li, Q. and Sun, G. (2019): Failure mechanisms in carbon fiber reinforced plastics (CFRP)/aluminum (Al) adhesive bonds subjected to low-velocity transverse pre-impact following by axial post-tension, Composites Part B: Engineering, Vol. 172, pp. 339-351. 8. Hart-Smith, L.J. (1980): Adhesive bonding of aircraft primary structures, SAE Transactions, pp. 3718-3732.

18. References 9. Chen, Q., Guo, H., Avery, K., Su, X. and Kang, H. (2017): Fatigue performance and life estimation of automotive adhesive joints using a fracture mechanics approach, Engineering Fracture Mechanics, Vol. 172, pp. 73-89 10. de Queiroz, H.F.M., Banea, M.D. and Cavalcanti, D.K.K. (2021): Adhesively bonded joints of jute, glass and hybrid jute/glass fibre-reinforced polymer composites for automotive industry, Applied Adhesion Science, Vol. 9, pp. 1-14. 11. Golaz, B., Michaud, V., Lavanchy, S. and Månson, J.A. (2013): Design and durability of titanium adhesive joints for marine applications, International Journal of Adhesion and Adhesives, Vol. 45, pp. 150-157. 12. Alia, C., Arenas, J.M., Suárez, J.C. and Pinilla, P. (2016): Mechanical behavior of polyurethane adhesive joints used in laminated materials for marine structures, Ocean Engineering, Vol. 113, pp. 64-74. 13. Ji, Y.M. and Han, K.S. (2014): Fracture mechanics approach for failure of adhesive joints in wind turbine blades, Renewable Energy, Vol. 65, pp. 23-28. 14. Jørgensen, J.B., Sørensen, B.F. and Kildegaard, C. (2019): The effect of residual stresses on the formation of transverse cracks in adhesive joints for wind turbine blades, International Journal of Solids and Structures, Vol. 163, pp. 139-156. 15. Zhou, W., Ji, X.L., Yang, S., Liu, J. and Ma, L.H. (2021): Review on the performance improvements and non-destructive testing of patches repaired composites, Composite Structures, pp. 113659. 16. Martinez, J.L., Cyrino, J.C., Vaz, M.A., Hernández, I.D. and Perrut, V.A. (2021): Composite patch repair of damaged tubular members from flare boom structures subjected to compressive loads, Composite Structures, Vol. 257, pp. 113168.

19. References 17. Shamsuddoha, M., Manalo, A., Aravinthan, T., Islam, M.M. and Djukic, L. (2021): Failure analysis and design of grouted fibre-composite repair system for corroded steel pipes, Engineering Failure Analysis, Vol. 119, pp. 104979. 18. Alireza Akhavan-Safar, Eduardo A.S. Marques, Ricardo J.C. Carbas, Lucas F.M. da Silva (2021): 6 - Stress analysis of adhesive joints, Welding and Other Joining Technologies in Woodhead Publishing Series, 159-192. 19. Gefu Ji, Zhenyu Ouyang, Guoqiang Li, Samuel Ibekwe, Su-Seng Pang (2010): Effects of adhesive thickness on global and local Mode-I interfacial fracture of bonded joints, International Journal of Solids and Structures, Vol. 47, Issues 18–19. 20. Ji, G., Ouyang, Z. & Li, G. (2011): Effects of bondline thickness on Mode-II interfacial laws of bonded laminated composite plate. Int J Fract 168, 197–207. 21. Yasmina Boutar, Sami Naïmi, Salah Mezlini, Lucas F.M. da Silva, Moez Ben Sik Ali (2017): Characterization of aluminium one-component polyurethane adhesive joints as a function of bond thickness for the automotive industry: Fracture analysis and behavior, Engineering Fracture Mechanics, Vol. 177, 45-60. 22. Tobias Strohmann, Denis Starostin-Penner, Eric Breitbarth, Guillermo Requena (2021): Automatic detection of fatigue crack paths using digital image correlation and convolutional neural networks, Fatigue & Fracture of Engineering Materials & Structures, 44:1336–1348. 23. Melching, D., Strohmann, T., Requena, G. et al. (2022): Explainable machine learning for precise fatigue crack tip detection. Sci Rep 12, 9513. 24. C.T. Sun, Z.-H. Jin (2012): Chapter 9 - Cohesive Zone Model, Fracture Mechanics. DOI: 10.1016/B978-0-12-385001-0.00009-2