Flame Resistance Prediction for Polymer Composites
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Solution Overview
Problem
The development of composite materials with flame resistance is inefficient due to the need for trial and error in formulating multiple additives, making it difficult to interpret effective combinations for flame resistance.
Innovation Solution
A flame resistance predicting device that uses a machine learning-based prediction model to forecast the flame resistance of polymer composite materials from input material formulation information, including resin and additive ratios, structures, and molding processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If trial and error method is used to determine formulation by repeating trial production, then flame resistance can be achieved, but development efficiency is poor
Solution Approach 1:
The patent applies preliminary action by using a prediction model to forecast flame resistance before actual trial production. The model predicts flame resistance based on formulation information (resin type, additive types and amounts, processing conditions) so that optimal formulations can be identified in advance, eliminating the need for repeated trial and error production cycles.
Solution Approach 2:
The patent replaces the mechanical trial-and-error production system with an information-based prediction system. Instead of physically producing multiple batches to test flame resistance, the system uses a prediction model that processes formulation data and outputs flame resistance predictions, substituting physical experimentation with computational analysis.
2Reliability
If multiple additives are used in combination to satisfy various characteristics, then flame resistance and other characteristics can be improved, but it becomes difficult to interpret what has been effective to cause flame resistance
Solution Approach 1:
The patent applies segmentation by dividing the complex formulation into distinct components: resin type, additive types (flame retardants, nucleating agents, etc.), and processing conditions. The prediction model processes each component separately and evaluates their combined effect, making it possible to identify which specific additives contribute most to flame resistance despite using multiple substances in combination.
Solution Approach 2:
The prediction model acts as an intermediary between the complex formulation and the desired flame resistance outcome. It processes the formulation information (resin type, additive amounts, processing conditions) and outputs flame resistance predictions, serving as a mediator that translates complex multi-component formulations into interpretable predictions about flame resistance behavior.
Data Source
AI summary
A flame resistance predicting device (100) includes: an information acquisition unit (111) that receives an input of material formulation information regarding a material of a polymer composite material; and a prediction unit (113) that predicts information regarding flame resistance of the polymer composite material from the input material formulation information of the polymer composite material using a flame resistance prediction model (prediction model (121)) that predicts the information regarding the flame resistance of the polymer composite material. Examples of the material formulation information include information regarding the kind of resin, the kind of additive, a ratio between the resin and the additive, a structure of the resin, a structure of the additive, and a molding process of the polymer composite material.