Rate-dependent elastic hysteresis during the peeling of Pressure Sensitive Adhesives
Richard Villey (SIMM, FAST), Costantino Creton (SIMM), Pierre-Philippe, Cortet (FAST), Marie-Julie Dalbe (Phys-ENS, ILM), Thomas Jet (SIMM), Baudouin, Saintyves (SIMM, FAST), St\'ephane Santucci (Phys-ENS), Lo\"ic Vanel (ILM),, David Yarusso, Matteo Ciccotti (SIMM)

TL;DR
This study investigates how peeling angle and rate-dependent elastic hysteresis influence the adherence energy of pressure sensitive adhesives, revealing new insights into the mechanics of adhesive debonding.
Contribution
It provides the first systematic demonstration of adherence energy dependence on peeling angle and highlights the significant role of large strain rheology in PSA peeling.
Findings
Adherence energy depends on peeling angle, separable from velocity effects.
Large strain rheology significantly influences adherence energy.
Adherence energy is linked to viscous friction and elastic hysteresis, not interfacial stress singularity.
Abstract
The modelling of the adherence energy during peeling of Pressure Sensitive Adhesives (PSA) has received much attention since the 1950's, uncovering several factors that aim at explaining their high adherence on most substrates, such as the softness and strong viscoelastic behaviour of the adhesive, the low thickness of the adhesive layer and its confinement by a rigid backing. The more recent investigation of adhesives by probe-tack methods also revealed the importance of cavitation and stringing mechanisms during debonding, underlining the influence of large deformations and of the related non-linear response of the material, which also intervenes during peeling. Although a global modelling of the complex coupling of all these ingredients remains a formidable issue, we report here some key experiments and modelling arguments that should constitute an important step forward. We first…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
