2 publications

• Published Subscription Original Research 29 Aug 2026

  Thermo-Structural Machine Learning Framework for Malicious Sample Detection and Validation in Polymer Composite Materials

  By Mahesh T. Dhande, Nilesh V. Ingale, Pankaj Deshmukh, Ritesh S. Fegade, Ashwini L. Patil, Bhausaheb Varpe, Vithoba Tale, Rupendra Nehete

  Abstract: The increased use of polymer composites in aerospace, automotive, biomedical and industrial applications has increased the urgency of developing dependable methods to detect malicious samples with counterfeited resins, unauthorized additives, recycled components, hidden flaws, or purposefully degraded physical properties. Most current machine learning techniques have focused either on isolated spectral analysis or detecting flaws in materials; as such, they are unable to perform joint verification of both chemical authenticity and …

  Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article →

• Published Subscription Original Research 26 Aug 2026

  Digital Twin Assisted Intelligent Prediction of Polymer Composite Degradation Under Environmental Exposure

  By Nilesh V. Ingale, Mahesh T. Dhande, Pankaj Deshmukh, Ritesh S. Fegade, Ashwini L. Patil, Bhausaheb Varpe, Vithoba Tale, Rupendra Nehete

  Abstract: Polymer matrix composites (PMCs) deployed in aerospace, marine, automotive, and renewable-energy structures are continuously subjected to coupled environmental stressors — ultraviolet (UV) radiation, moisture ingress, thermal cycling, and mechanical loading — that progressively degrade their mechanical performance. Conventional accelerated ageing tests and empirical lifetime models are time-consuming, destructive, and poorly suited to in-service, asset-specific degradation forecasting. This paper proposes a Digital Twin (DT) assisted intelligent prediction framework that fuses a …

  Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article →