Manufacturing is concerned with the making of products, and it dates back to early civilizations several thousand years ago, but even so it remains one of the main pillars of modern society.
IDA Mechanical has invited Professor, Phd., dr.techn., R, Jesper Hattel from DTU to join us and give us inspiration into Multiphysics modelling of manufacturing processes before the general meeting at IDA Mechanical. The presentation is based on Prof. Jesper Hattels doctoral defense originally held in December 2024.
With respect to a Multiphysics Modelling approach of Manufacturing Processes an inherent precondition is, and has been, the ability to gather empirical information from just observing the processes or employ more dedicated experiments to characterize the processes in terms of which materials and process parameters to use for ensuring the sufficient product quality and at the end of the day – productivity and profit. This naturally has given rise to the wish of being able to predict the consequences of one’s choices. In general, this can be based on either prior observation (e.g. specifically designed experiments or directly from production) or more theoretical considerations like mathematical models based on knowledge of the underlying physics (typically 1st principles in combination with some sort of material descriptions).
The present thesis evolves around the latter and is based on 34 scientific articles published in international journals during the period 1998 to 2021. They all one way or the other represent scientific highlights and cover the topic of how to mathematically model the fundamental multiphysical phenomena which take place during manufacturing processes.
All models involve elements from the three generic pillars of numerical heat transfer, computational fluid dynamics as well as computational solid mechanics for the following manufacturing processes: Metal casting, spray forming, friction stir welding, tape casting, pultrusion as well as additive manufacturing.
This is then put into proper context with respect to the ever-increasing ability of obtaining large amounts of data and the following data-driven models as well as the increasing wish to establish actual digital twins of the manufacturing processes.
