Previously for the analysis of Iyashi structure, the mapping function which is obtained from fixtures of facial expressions to the judge by the subject whether the data is healed or not to the subject. This accuracy is determined by the type of inputs and it is not always the general way to select the input data. In recent years, deep learning has been a great concern, because the input data itself is possibly to be provided from the big amounts of data. However, the amount and quality of big data depend on the problem and not yet general-purpose ones are obtained. In this study, the healing structure is given as an example to consider the quality and quantity in the big data.

Computer aided engineering (CAE) including structural analysis has become increasingly important for industrial product development. Various types of CAE software have been recently developed and utilized. However, some software cannot be introduced into development process briefly with technical barrier. In this study, we develop an all-in-one cloud CAE service using parallel structural analysis open-source software “FrontISTR”. The system has utilized WebGL for visualization 3D model. Then it can use by only general web browser. They consist only of open-source software. Since the cloud CAE service is a web-based service, every user can use any device which has a web browser. Therefore, the cloud CAE service is able to make start up user's CAE work easier and more efficient.

In this paper, we proposed a simulation approach based on the dynamic explicit finite element method to evaluate the flowability of resin pellets. Using the proposed simulation method, we clarified that the shape of the resin pellet had a large influence on its flowability, and found an optimal pellet shape that can improve the flowability of the resin pellets.