A multi-mode excitation hardness prediction method based on Controlled Laser Air-Force Detection (CLAFD) technique

A novel material hardness testing method was proposed based on controlled laser air-force detection (CLAFD) technique. Polyurethane was chosen as the study object. Multi-mode excitation was adopted. Partial least square as the modeling method was used to build the hardness prediction model on the data of laser displacement. Different preprocessing methods were carried out for eliminating the noise of the original data. The results showed the multiplicative scattering correction (MSC) had the best performance. Among four modes, the relationship coefficients of the prediction set (Rp) was above 0.90, and the residual prediction deviation (RPD) was more than 2. This result demonstrated that all four modes could be carried out to test the hardness of polyurethane. Furthermore, the Rp of the transient was 0.93, the RPD was 2.51, the excitation time was 1 s, showing that the transient mode performed with precision in high-speed hardness detection. The highest precision was based on the stress relaxation mode, so we did further study on the interval modeling analysis for the data of stress relaxation mode. The results showed that the hardness could not be predicted if only one single interval was used. However, the performance improved with the increase in the number of intervals. The Rp was up to 0.96, the coefficient of calibration set (Rc) was up to 0.99, and the RPD was 3.54 when the time of the stress relaxation mode lasted 60 s. Based on the results above, the prediction ability would improve further when the relaxation time is increased. The study will provide a new real-time, non-destruction and cross-contamination free hardness detection method for material science, especially for those materials such as artificial biological tissue, function food products, etc.