Multi-mechanical properties comprehensive evaluation by single excitation mode using controlled laser air-force detection (CLAFD) technique

Abstract

ABSTRACT The controlled laser air-force detection (CLAFD) technique was developed to explore the feasibility of multi-mechanical property detection of polyurethane by single excitation mode. The adhesiveness, elastic modulus, hardness, resilience, and cohesiveness of polyurethane were predicted by the global variable partial least squares regression (Gv-PLSR) algorithm. Different preprocessing methods were used to preprocess the original laser data. The interval partial least squares regression (I-PLSR) algorithm was used to decrease the influences of the multicollinearity of the global laser variable and increase the stability of the multi-mechanical property prediction models. To further improve the prediction accuracy of the modeling of I-PLSR algorithm, the synergy interval PLSR (Si-PLSR) algorithm was used to combine the intervals with the higher evaluation index root-mean-square error of cross-validation (RMSECV) to predict the multi-properties. The results demonstrated that as a novel mechanical property detection technique, the CLAFD technique predicts in an efficient way. A suitable preprocessing method for the original laser data could greatly improve the effectiveness of prediction. The I-PLSR algorithm was used to improve the model’s stability significantly. Nevertheless, the prediction accuracy decreased. Comparing the I-PLSR algorithm with the Si-PLSR algorithm, the prediction accuracy and the modal stability were optimized by the latter. However, the accuracy was still lower than the Gv-PLSR algorithm. Therefore, the Gv-PLSR was the best algorithm to establish the multi-mechanical properties prediction model. This study provided a new comprehensive, nondestructive, and cross-contamination-free method to evaluate the comprehensive mechanical properties (adhesiveness, elastic modulus, hardness, resilience, and cohesiveness) of materials efficiently, especially for the soft materials such as biomaterial and food material.