Measurement Error Correction and Thermal Property Prediction Based on Raman Scattering and Neural Networks

With the rapid development of materials science and engineering, the measurement and prediction of thermal properties have become a key research focus.Traditional methods for thermal property testing, while providing accurate experimental data, often require complex experimental conditions and lengthy testing periods, limiting their widespread application in real-time detection and efficient usage.Raman scattering, as a nondestructive and highly sensitive analytical technique, can effectively capture molecular structure and thermal property information of samples, making it increasingly popular in the field of thermal property measurement.However, Raman scattering signals are susceptible to interference from factors such as environmental temperature and laser power, leading to experimental errors that affect the accuracy of thermal property predictions.Currently, methods for predicting thermal properties based on Raman scattering signals primarily employ machine learning and deep learning techniques, but there remains significant room for improvement in accuracy, particularly when dealing with complex samples and highly nonlinear signals.Furthermore, existing error correction methods lack real-time adaptability, limiting their application in dynamic environments.To address these issues, this paper proposes a neural network-based model for Raman scattering measurement error correction and thermal property prediction.By constructing a thermal property prediction model and error correction model based on Random Forest (RF)-temporal convolutional network (TCN)-self-attention (SA), this study effectively improves prediction accuracy and the reliability of experimental data, providing theoretical support and technical assurance for the precise application of Raman scattering technology.