A Conceptual Framework for Dynamic Mechanical Analysis in High-Performance Material Selection

• Author(s): Musa Adekunle Adewoyin ; Enoch Oluwadunmininu Ogunnowo ; Joyce Efekpogua Fiemotongha ; Thompson Odion Igunma ; Adeniyi K. Adeleke
• Paper ID: 1708640
• Page: 137-155
• Published Date: 30-11-2020
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 4 Issue 5 November-2020

Abstract

The increasing demand for high-performance materials across diverse engineering applications necessitates advanced methodologies for accurate material selection. Traditional mechanical characterization techniques often fall short in capturing time-dependent behavior and viscoelastic properties essential for high-performance applications. This paper proposes a conceptual framework for integrating Dynamic Mechanical Analysis (DMA) into the material selection process, focusing on the mechanical performance of polymers, composites, and hybrid materials under varying frequencies and temperatures. The framework synthesizes theoretical foundations, experimental protocols, and decision-support tools to provide a robust, data-driven pathway for selecting materials that exhibit optimal stiffness, damping, and energy dissipation characteristics under dynamic loading conditions. At the core of the framework is the interpretation of storage modulus, loss modulus, and tan delta curves as quantitative indicators of material behavior. These parameters enable the discrimination of candidate materials not only based on static strength but also on fatigue resistance, thermal stability, and operational reliability in high-strain environments. The model emphasizes frequency-dependent and temperature-dependent testing conditions, enabling engineers to simulate real-world performance scenarios, such as automotive vibrations, aerospace load cycles, or biomedical implant fatigue. By embedding DMA outcomes into material databases and using multi-criteria decision-making tools like Analytical Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), the framework supports systematic comparison and ranking of materials. The paper also discusses the integration of artificial intelligence and machine learning models to predict DMA parameters for new or less-characterized materials, enhancing the predictive power and reducing the dependency on exhaustive testing. Furthermore, case studies in automotive and aerospace industries are presented to demonstrate the practical implementation and utility of the framework in optimizing design for performance, cost, and durability. This conceptual framework marks a transformative shift in mechanical material selection by emphasizing dynamic mechanical behavior as a critical determinant of performance. It offers a scalable, customizable approach suitable for researchers, design engineers, and materials scientists focused on high-stakes applications.

Keywords

Dynamic Mechanical Analysis (DMA), High-Performance Materials, Viscoelasticity, Material Selection, Storage Modulus, Loss Modulus, Tan Delta, Multi-Criteria Decision-Making, Artificial Intelligence, Temperature-Frequency Response.

Citations

IRE Journals:
Musa Adekunle Adewoyin , Enoch Oluwadunmininu Ogunnowo , Joyce Efekpogua Fiemotongha , Thompson Odion Igunma , Adeniyi K. Adeleke "A Conceptual Framework for Dynamic Mechanical Analysis in High-Performance Material Selection" Iconic Research And Engineering Journals Volume 4 Issue 5 2020 Page 137-155

IEEE:
Musa Adekunle Adewoyin , Enoch Oluwadunmininu Ogunnowo , Joyce Efekpogua Fiemotongha , Thompson Odion Igunma , Adeniyi K. Adeleke "A Conceptual Framework for Dynamic Mechanical Analysis in High-Performance Material Selection" Iconic Research And Engineering Journals, 4(5)