2 publications

• Published Subscription Original Research Special issue 10 Jun 2026

  Machine Learning-Based Quantification of Polymer Structure Property Relationships for Predictive Material Design

  By Kalpana G. Joshi, Shanthi Kumaraguru, Prashant M. Yawalkar, P. William, Prashant V. Thokal, Jaikumar M. Patil

  Abstract: Polymer structures exhibit complex, hierarchical arrangements that strongly influence macroscopic properties, yet consistent quantification remains challenging due to nonlinear interactions and limited unified modeling strategies. Existing approaches inadequately capture generalized structure–property mappings across diverse polymer systems. This research aims to establish a machine learning-based quantification model for polymer structure–property relationships to support predictive material design. A Polymer Structure Property Dataset of 5,000 polymer samples includes structural descriptors and experimentally measured …

  Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 737–754 Read article →

• Published Subscription Original Research 18 May 2026

  Data-Driven Digital Twin Model for Real-Time Strength Estimation in Polymeric Materials

  By Makrand V. Kulkarni, Kalpana G. Joshi, Shanthi Kumaraguru, Vinit Kotak, Prashant V. Thokal, Prashant M. Yawalkar, Vijay More

  Abstract: The real-time prediction of mechanical properties in polymeric materials is essential for ensuring quality, consistency, and operational efficiency in modern manufacturing systems. As industrial processes become increasingly complex, traditional trial-and-error approaches to material characterization are no longer sufficient to meet the demands of high-throughput production environments. This study introduces a digital twin-integrated machine learning approach for the real-time estimation of tensile strength in polymeric materials by combining simulation-driven insights with …

  Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 246–257 Read article →