YAG: Ce3+ phosphor/silicone composites are widely used in solid-state lighting as a light converter to achieve white lighting. However, because of high thermal resistance and low thermal stability, the luminous performance of YAG: Ce3+ phosphor/silicone composite deteriorates rapidly when excited by high-power-density blue-laser. To explore the potential of blue laser-excited YAG: Ce3+ phosphor/silicone composites, the luminous performances under different blue laser power conditions were characterized by both the reflective and transmissive excitations using a self-built three-integrating-sphere system. Furthermore, the Monte-Carlo Ray-tracing simulation was used to illustrate the light-transmission and energy conversion mechanism in the phosphor/silicone composites. The results showed that: (1) The YAG: Ce3+ phosphor/silicone composite could be excited by the 0.292W laser light with the peak wavelength of 445nm, excessive laser power will cause phosphor thermal quenching and silicone carbonization. (2) The luminous flux of the composite under both the reflective and transmissive excitations gradually increased with the increase of phosphor concentration; correspondingly, the color coordinate moved to the yellow region, and the Correlated Color Temperature (CCT) gradually decreased. (3) The simulation results indicated that under the same phosphor concentration, the luminous flux obtained by reflection excitation was largely higher than that by the transmission excitation, as the light re-conversion and strong back-scattering were occurred in the reflective and transmissive laser excitation respectively.

This paper presents a convenient and efficient method to predict the mechanical solutions of a laminated Liquid Crystal Elastomers (LCEs) system subjected to combined thermo-mechanical load, based on a back propagation (BP) neural network which is trained by machine learning from a database established by analytical solutions. Firstly, the general solutions of temperature, displacement, and stress of any single layer in the LCEs system are obtained by solving the two-dimensional (2D) governing equations of both heat conduction and thermoelasticity. Then, the unknown coefficients in above general solutions are determined by a transfer-matrix method based on the continuity condition at the interface of adjacent layers and the combined thermo-mechanical loads condition at the surface of the LCEs system. The formula derivation and calculator program are verified through convergence studies and comparisons with FEM results. Finally, a database with displacements of LCEs system in a temperature field subjected to 561 sets of mechanical loads is established based on the presented analytical model. The BP neural network based on above database is further applied to establish the relationship between deformation and mechanical load to predict the elastic deformation of the LCEs system in a temperature field subjected to a mechanical load. Moreover, the BP network can also inverse the coefficients of mechanical load which induces the specific deformation in a temperature field. The numerical examples show that: (1) The deformation of a laminated LCEs system due to thermal load is limited within the range of human temperature changes from 36 °C to 40 °C. (2) The thickness of the LCE is a sensitive parameter on the deformation at the bottom surface of the system. (3) The accuracy of predicted displacements induced by the thermo-mechanical load and the inversed mechanical load based on deformation of the LCEs system in a temperature field using BP neural network reaches 99.6% and 98.5% respectively.