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Insects and small animals often utilize structured surfaces to create friction during their movements. These surfaces typically consist of pillar-like fibrils that interact with a counter surface. Understanding the mechanical interaction between such surfaces is crucial for designing structured surfaces for engineering applications. In the first part of our study, we examined friction between poly(dimethylsiloxane) (PDMS) samples with surfaces patterned with pillar-arrays. We observed that sliding between these surfaces occurs through the interfacial glide of dislocation structures. The frictional force that resists this dislocation glide is a result of periodic single pillar-pillar contact and sliding. Hence, comprehending the intricate interaction between individual pillar contacts is a fundamental prerequisite for accurately modeling the friction behavior of the pillar array. In this second part of the study, we thoroughly investigated the contact interaction between two pillars located on opposite sides of an interface, with different lateral and vertical offsets. We conducted experiments using PDMS pillars to measure both the reaction shear and normal forces. Contact interaction between pillars was then studied using finite element (FE) simulations with the Coulomb friction model, which yielded results that aligned well with the experimental data. Our result offers a fundamental solution for comprehending how fibrillar surfaces contact and interact during sliding, which has broad applications in both natural and artificial surfaces.
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Mine the Gap: Gap Estimation and Contact Detection Information via Adjacent Surface Observation
In general, conventional computer vision techniques suffer from an inability to detect hidden surface contacts due to line-of-sight visibility problems. Rather than fitting models to scene objects and estimating inter-object gaps, our approach is to leverage the fact that light passing between and reflecting off the surfaces can offer valuable information as it alters the appearance of nearby surfaces. For a proof of concept demonstration, we employed a machine learning approach to classifying adjacent surface imagery to estimate hidden surface distances and contact locations in a controlled setting under ambient lighting conditions. Our proofof- concept results demonstrate relatively high accuracy for the estimation of gap size and the detection of contact between hidden surfaces. We envision such measures could someday provide complementary information to be combined with traditional visible-surface methods, to obtain more precise and robust estimates of hidden surface relationships.
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- Award ID(s):
- 1564065
- PAR ID:
- 10105873
- Date Published:
- Journal Name:
- International Conference on Pattern Recognition and Artificial Intelligence
- Page Range / eLocation ID:
- 54 to 58
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3243250.3243260
