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1. Li, B. and Hoo Fatt, M.S., “Use of a Cohesive Zone Model to Predict Dynamic Tearing of Rubber,” Tire Science and Technology, Vol. 43, No. 4, pp. 297-334, 2015.

   Li, B. and (July 2015, Tire science & technology)

   ABSTRACT: Tire failures, such as tread separation and sidewall zipper fracture, occur when internal flaws (cracks) nucleate and grow to a critical size as result of fatigue or cyclic loading. Sudden and catastrophic rupture takes place at this critical crack size because the strain energy release rate exceeds the tear energy of the rubber in the tire. The above-mentioned tire failures can lead to loss of vehicle stability and control, and it is important to develop predictive models and computational tools that address this problem. The objective of this article was to develop a cohesive zone model for rubber to numerically predict crack growth in a rubber component under dynamic tearing. The cohesive zone model for rubber was embedded into the material constitutive equation via a user-defined material subroutine (VUMAT) of ABAQUS. It consisted of three parts: (1) hyperviscoelastic behavior before damage, (2) damage initiation based on the critical strain energy density, and (3) hyperviscoelastic behavior after damage initiation. Crack growth in the tensile strip and pure shear specimens was simulated in ABAQUS Explicit, and good agreement was reported between finite element analysis predictions and test results 

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2. AI-dente: an open machine learning based tool to interpret nano-indentation data of soft tissues and materials

   https://doi.org/10.1039/D3SM00402C  

   Giolando, Patrick; Kakaletsis, Sotirios; Zhang, Xuesong; Weickenmeier, Johannes; Castillo, Edward; Dortdivanlioglu, Berkin; Rausch, Manuel K. (October 2023, Soft Matter)

   Nano-indentation is a promising method to identify the constitutive parameters of soft materials, including soft tissues. Especially when materials are very small and heterogeneous, nano-indentation allows mechanical interrogation where traditional methods may fail. However, because nano-indentation does not yield a homogeneous deformation field, interpreting the resulting load–displacement curves is non-trivial and most investigators resort to simplified approaches based on the Hertzian solution. Unfortunately, for small samples and large indentation depths, these solutions are inaccurate. We set out to use machine learning to provide an alternative strategy. We first used the finite element method to create a large synthetic data set. We then used these data to train neural networks to inversely identify material parameters from load–displacement curves. To this end, we took two different approaches. First, we learned the indentation forward problem, which we then applied within an iterative framework to identify material parameters. Second, we learned the inverse problem of directly identifying material parameters. We show that both approaches are effective at identifying the parameters of the neo-Hookean and Gent models. Specifically, when applied to synthetic data, our approaches are accurate even for small sample sizes and at deep indentation. Additionally, our approaches are fast, especially compared to the inverse finite element approach. Finally, our approaches worked on unseen experimental data from thin mouse brain samples. Here, our approaches proved robust to experimental noise across over 1000 samples. By providing open access to our data and code, we hope to support others that conduct nano-indentation on soft materials. 

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3. Sliding friction of a pillar array interface: part I

   https://doi.org/10.1039/d3sm01323e  

   Kaur, Jasreen; Xiao, Xuemei; Khripin, Constantine; Hui, Chung-Yuen; Jagota, Anand (February 2024, Soft Matter)

   Biology is replete with examples, at length scales ranging from the molecular (ligand–receptor binding) to the mesoscopic scale (wing arresting structures on dragonflies) where shape-complementary surfaces are used to control interfacial mechanical properties such as adhesion, friction, and contact compliance. Related bio-inspired and biomimetic structures have been used to achieve unique interfacial properties such as friction and adhesion enhancement, directional and switchable properties. The ability to tune friction by altering surface structures offers advantages in various fields, such as soft robotics and tire manufacturing. Here, we present a study of friction between polydimethylsiloxane (PDMS) samples with surfaces patterned with pillar-arrays. When brought in contact with each other the two samples spontaneously produce a Moire´ pattern that can also be represented as an array of interfacial dislocations that depends on interfacial misorientation and lattice spacing. Misorientation alone produces an array of screw dislocations, while lattice mismatch alone produces an array of edge dislocations. Relative sliding motion is accompanied by interfacial glide of these patterns. The frictional force resisting dislocation glide arises from periodic single pillar–pillar contact and sliding. We study the behavior of pillar–pillar contact with larger (millimeter scale) pillar samples. Inter-pillar interaction measurements are combined with a geometric model for relative sliding to calculate frictional stress that is in good agreement with experiments. 

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4. Mechanics of Regular-Shape Nanomeshes for Transparent and Stretchable Devices

   https://doi.org/10.1115/1.4047777  

   Vinnikova, Sandra; Fang, Hui; Wang, Shuodao (October 2020, Journal of Applied Mechanics)

   null (Ed.)

   Abstract Open nanomesh structures with nano/micro-scale geometric dimensions are important candidates for transparent, soft, and stretchable microelectrodes. This study developed analytical and numerical mechanics models for three types of nanomeshes that consist of regular polygons and straight traces. The analytical models described the transparency, effective stiffness, and stretchability of the nanomeshes and agree with the finite element analysis. The mechanical performances of the nanomeshes are compared based on the same level of transparency. The validated analytical expressions provide convenient guidelines for designing the nanomeshes to have levels of transparency and mechanical properties suitable for bio-integrated applications. 

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5. Should a Vehicle Always Deviate to the Tire Blowout Side?—A New Tire Blowout Model With Toe Angle Effects

   https://doi.org/10.1115/1.4051034  

   Li, Ao; Chen, Yan; Du, Xinyu; Lin, Wen-Chiao (October 2021, Journal of Dynamic Systems, Measurement, and Control)

   Abstract As a severe tire failure, tire blowout during driving can significantly threaten vehicle stability and road safety. Tire blowout models were developed in the literature to conclude that a vehicle always deviates to the tire blowout side. However, this conclusion is proved to be inaccurate in this paper, since one important factor was largely ignored in the existing tire blowout models. Toe angle, as a basic and widely applied setup on ground vehicles, can provide preset and symmetric lateral tire forces for normal driving. However, when tire blowout occurs, different toe angle setups can impact vehicle motions in different ways. For the first time, the toe angle is explicitly considered and integrated into a tire blowout model in this paper. For different tire blowout locations, driving maneuvers, and drivetrain configurations, the impacts of different toe angle setups on the variations of tire friction forces and vehicle motions are analyzed. The developed tire blowout model with toe angles is validated through both high-fidelity carsim simulation results and experimental results of a scaled test vehicle. Both simulation and experimental results show that a vehicle may not deviate to the tire blowout side, depending on the toe angle setups and driving maneuvers. Moreover, the experimental results also validate that the proposed tire blowout model can accurately evaluate the tire blowout impacts on vehicle dynamics. 

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