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By: Heena T Shaikh and Kazi Kutubuddin Sayyad Liyakat.
1 Asst. Professor, Department of Electronics and Telecommunication Engineering,
Brahmdevdada Mane, Institute of Technology, Solapur, Maharashtra, India.
2 Professor, Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane, Institute of Technology, Solapur, Maharashtra, India.
The integration of Artificial Intelligence (AI) and Machine Learning (ML) into the thermal
analysis of polymers and composite materials has transitioned from experimental curiosity to a
cornerstone of material science. By replacing costly and time-consuming trial-and-error
laboratory testing with predictive modeling, AI is accelerating the development of advanced
thermal interface materials, fire-retardant polymers, and aerospace-grade composites. The
prediction and optimization of the thermal behavior of polymers and composite materials
represent a cornerstone of modern materials science, particularly in aerospace, electronics, and
automotive industries. Traditionally, characterizing phenomena such as glass transition
temperatures, thermal degradation, and heat deflection under varying environmental conditions
has relied on computationally expensive multi-scale modeling or exhaustive experimental trial-
and-error. This paper explores the transformative role of Artificial Intelligence
(AI)—specifically deep learning architectures, physics-informed neural networks (PINNs), and
machine learning regression models—in deciphering complex thermal dynamics. We
demonstrate how AI frameworks can integrate sparse experimental datasets with fundamental
thermodynamics to predict nonlinear thermal responses with unprecedented speed and accuracy.
By mapping the relationship between microscopic compositional variables and macroscopic
thermal performance, AI not only accelerates the discovery of high-performance materials but
also provides a predictive lens for evaluating long-term structural integrity under thermal stress,
effectively bridging the chasm between raw empirical data and predictive material design.
Keywords: Artificial Intelligence, Polymer, Composite material, Machine Learning, Thermal
Behaviour.
Citation:
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