Optimization of Adhesive Composition Using Machine Learning

Abstract

There are various types of adhesives made from epoxy resin or the like. Different physical properties are manifested depending on the materials and the blending amount. Conventionally, the blending amount has been optimized to achieve the desired physical properties based on the knowledge and intuition of researchers. The effects of each material on the physical properties have not been fully revealed, and a number of experiments are necessary until the new adhesives is invented. In order to solve this problem, machine learning models that predicts the target physical properties from the composition of the adhesive was built. In this presentation, we will describe the results of building models for glass transition temperature, moisture permeability, and adhesive strength, which are examples of physical properties that are of interest for adhesives. We also report on the result of experiments to verify the composition predicted to appear the desired physical properties by generating a large number of candidate compositions.