Abstract:In this paper, the estimations for the optical property of touch panel (TP) decoration film with two layers coating are presented. The technique of neural network is used to develop an artificial intelligent (AI) TP transmittance estimator which is able to catch the complicated relationship between TP transmittance and its all possible influencing factors, such as the compositions of coating material, the thickness of coating, the rotation speed of evaporator?s holder and so on. This AI estimator then can provide the useful information which could help the engineer to set the relevant control parameters of evaporator properly before the evaporation process is taken. The simulation results show that such an AI system is quite promising to be developed.
This paper presents the transmittance estimations for touch panel (TP) film with Cr and Cr 2 O 3 coating by using neural network (NN) model. The NN model with quasi-Newton learning method was used to obtain the mapping between TP transmittance and its all possible influencing factors. This study tries to develop an artificial intelligent (AI) evaporation decision mechanism which can help the technician to set the related control parameters before the film's evaporation process is taken. The transmittance is one of important determination factors used for checking whether the quality of TP is qualified or not. Thus, a smart decision mechanism not only can help technician to improve the work efficiency, but also can reduce the running cost of the company due to the defective products.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.