Evidence map›Paper›PMID 41717007›Full record

ReviewiScience2026

A comprehensive review on rubber wear modeling with focus on tire applications.

Raffaele Maglione, Flavio Farroni, Andrea Genovese, Guido Napolitano Dell'Annunziata, Francesco Timpone, Aleksandr Sakhnevych

Abstract readReview
In one paragraph

Review in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Raffaele MaglioneDepartment of Industrial Engineering, University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy.
Flavio FarroniDepartment of Industrial Engineering, University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy.
Andrea GenoveseDepartment of Industrial Engineering, University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy.
Guido Napolitano Dell'AnnunziataDepartment of Industrial Engineering, University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy.
Francesco TimponeDepartment of Industrial Engineering, University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy.
Aleksandr SakhnevychDepartment of Industrial Engineering, University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tire wear arises from coupled viscoelastic deformation, interfacial friction, temperature, and material fatigue, with growing relevance for performance, durability, and environmental impact. This review synthesizes the main families of tire-wear models, including empirical, analytical, numerical, and data-driven approaches, and examines their physical assumptions, predictive range, computational cost, and applicability to realistic tire road conditions. Emphasis is placed on friction and wear interactions, rubber specific complexities, and the absence of a common tribological framework. This work offers three key contributions: a systematic classification of modeling approaches and their assumptions; integration of environmental and regulatory drivers relevant to tire wear assessment; and an evaluation of model applicability across validation contexts, from laboratory tests to indoor rigs and real world conditions. Remaining challenges include transient dynamics, thermo-mechanical coupling, and model generalization. Future directions highlight the need for physically grounded and efficient models able to bridge scales, materials, and applications.

Indexed as

computational materials sciencematerials sciencemechanics of materials

Identifiers

PMID41717007
PMCPMC12915245

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.