Development of an Online Monitoring Device for the Mixing Ratio of Two-Part Epoxy Adhesives Using an Electrical Impedance Spectroscopy Technique and Machine Learning

Abstract

Two-part epoxy adhesives are widely used in a range of industries. Two-part epoxy adhesive is composed of a resin and a hardener. Both materials remain stable in the general environment but curing begins when mixed in the specified mixing ratio. However, it has the disadvantage of requiring a specific mixing device. In addition, if the mixing ratio is different from the specified ratio due to the error of the mixing system, it has a fatal effect on the adhesion performance. The dielectric constant is a characteristic constant of a material. Therefore, it represents the mixing ratio of mixed two-part epoxy adhesives. With the electrical impedance spectroscopy technique, it can be measured indirectly by measuring impedance according to frequency and temperature. In this study, a sensor and embedded device for an online monitoring of its integrity using a regression method among machine learning are developed, which can acquire impedance data with frequency and temperature data according to the change in the mixing ratio of a two-part epoxy adhesive. The experimentally collected data were used as training data for the machine learning algorithm. It was found that the learned machine learning algorithm effectively estimates the mixing ratio of the two-part epoxy with an arbitrary value.