Artificial Intelligence to Expedite Thermal Index Testing and Accelerate Polymer Regulatory Approvals

With the increasing focus on sustainability, industries are rapidly adopting technologies such as 3D printing and making significant investments to achieve their environmental goals. This technological shift is driving a surge in the development of new polymers, including the revival of materials like aliphatic polyketone and the creation of innovative resins such as polyacrylates. These new materials, targeting at specific properties like flame retardancy and heat stabilization, require rigorous regulatory approval to be used in applications with strict safety standards, especially in electrical connectors and components. Validating short-term properties of these resins is relatively quick and straightforward. However, assessing their long-term thermal stability, particularly through tests like UL's Relative Thermal Index (RTI), is a complex and time-consuming process, often taking months. This lengthy testing period poses a significant challenge for new resins entering the market, due to the high time and cost involved. The advancement of artificial intelligence (AI) offers a transformative solution to this problem. This paper explores how AI can be utilized to predict long-term thermal performance accurately, effectively reducing the duration of RTI testing by up to 50% without compromising on reliability. More specifically, An AI-based methodology is proposed to estimate the RTI temperature by incorporating the physics of thermal degradation, where data from shorter testing periods at higher temperatures is used to predict the results of longer testing durations at lower temperatures. Through this innovative approach, AI enables faster regulatory approvals, allowing new polymer developments to reach the market more quickly and efficiently, keeping pace with the dynamic advancements in the field.