Gemelos digitales basados en física para fabricación de soldaduras en tiempo real


    Esta publicación solo está disponible en inglés.

    Authored by Mahyar Asadi, Mohammad Mohseni, Majid Tanbakeui Kashani, Michael Fernandez, and Mathew Smith; and presented at the NAFEMS NORDIC Virtual Seminar.

    Physics-based digital twins offer a critical core-competency that enables the smarting through limited data. The current data-driven digital twins that use machine learning take a significant initial data set to mature for making decisions. In most manufacturing processes, there is no such data set to draw on. The majority of SMEs need cyber-manufacturing systems that work with limited data and physics based digital twins. However, physics-based digital twins that are entirely built on deploying simulation tools such as finite element analysis (FEA) cannot be responsive enough for real-world applications. A solution is to use machine learning (ML) algorithms that emulate the time-consuming FEA-solver behaviour at a much shorter time. We build a hybrid physics-based digital-twin that takes advantage of data-driven digital twins for quick response while gaining fidelity through adaptive learning with FEA simulation tools. We use our hybrid digital-twin to explore various weld sequences in real-time to form a platform for smart welding fabrication. This tool enables engineers to analyze and compare different patterns to assess fabrication scenarios without computational time delay.

    Read the complete paper here.

    Applus+ utiliza cookies propias y de terceros para fines analíticos y para mostrarte publicidad personalizada en base a un perfil elaborado a partir de tus hábitos de navegación (por ejemplo, páginas visitadas). Clica AQUÍ para más información. Puedes aceptar todas las cookies pulsando el botón “Aceptar” o configurarlas o rechazar su uso clicando aquí.

    Panel de configuración de cookies