Engineer design process assisted by explainable deep learning network.

ABSTRACT: Engineering simulation accelerates the development of reliable and repeatable design processes in various domains. However, the computing resource consumption is dramatically raised in the whole development processes. Making the most of these simulation data becomes more and more important in modern industrial product design. In the present study, we proposed a workflow comprised of a series of machine learning algorithms (mainly deep neuron networks) to be an alternative to the numerical simulation. We have applied the workflow to the field of dental implant design process. The process is based on a complex, time-dependent, multi-physical biomechanical theory, known as mechano-regulatory method. It has been used to evaluate the performance of dental implants and to assess the tissue recov

SUBMITTER: Hsu CW 

PROVIDER: S-EPMC8602721 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature