Research on Intelligent Sensing Machine Learning Prediction Model of Material Interface Properties

This paper designs an intelligent prediction model using machine learning technology. By collecting data and extracting features of various parameters of material interface properties (such as surface energy, adhesion and interface roughness, etc.), the prediction model is trained to achieve accurate prediction of interface properties. This project proposes a multi-layer learning architecture based on regression and classification, and uses various optimization algorithms to improve the accuracy and credibility of the model. Different types of samples were tested in simulation experiments, and the results showed that the established prediction model has high accuracy. The mathematical model established in this paper can well predict the interface properties of materials, with an average absolute error of less than 5%. In addition, this method can effectively reduce the response speed of the sensor and reduce the error rate, and has good practical value. The machine learning prediction model based on intelligent sensing proposed in this paper provides an effective, fast and low-cost method for analyzing material interface properties, which can be widely used in the field of materials science and engineering.