Chinese Journal of Lasers, Volume. 45, Issue 8, 807002(2018)
Multivariate Nonlinear Regression Model of Laser Fusion in vitro Skin Tissue Incision Performance Based on Response Surface Methodology
Based on the design method of response surface methodology, pulsed laser welding process experiment of in vitro skin tissue is performed to obtain the tensile strength and peak temperature data of tissue incision. On the basis of single factor experiment, the multivariate nonlinear mathematical regression model is established by using laser power, spot moving speed and laser frequency as the three influencing factors. Correlation coefficients of the regression model are obtained by analysis of variance and regression analysis as follows: correlation coefficient of incision tensile strength is 0.9131, correlation coefficient of incision peak temperature is 0.9985. The results of model analysis show that the main effect and interactions of laser power, spot movement speed and laser frequency have a great influence on the incision performance. The main effect that has the greatest influence on the tensile strength of incision is laser power, and the interaction effect is laser power and spot movement speed. The main effect that has the greatest influence on the peak incision temperature is laser power, and the interaction effects are laser power and spot movement speed, laser power and laser frequency, and spot movement speed and laser frequency. Finally, the optimal combination of laser process parameters is obtained based on the regression model. The experimental results show that the response values of regression model are consistent with the experimental results, and the incision strength meets the requirements.
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Liu Qimeng, Huang Jun, Wang Kehong, Zhou Qi. Multivariate Nonlinear Regression Model of Laser Fusion in vitro Skin Tissue Incision Performance Based on Response Surface Methodology[J]. Chinese Journal of Lasers, 2018, 45(8): 807002
Category: biomedical photonics and laser medicine
Received: Dec. 11, 2017
Accepted: --
Published Online: Aug. 11, 2018
The Author Email: Jun Huang (huangjun0061@126.com)