N. A. McPherson, BAE Systems, Glasgow, United Kingdom
A great deal of effort has been put into the study of thin plate distortion within steel structures. Much of this work has been academic based and has produced a number of key findings. However, there is a view that the application of some of these findings into industrial practice is limited and often understated as a significant number of the basic root causes of distortion have not been tackled. It is almost akin to having the rocket to go to the Moon but not having the launch pad.
This particular work will focus on a managed approach taken within a UK shipyard building naval vessels with a very significant proportion of thin plate (<8mm) in the structure.
It describes how the known factors were addressed, while at the same time parallel research work was being carried out using modelling techniques such as Artificial Neural Networks (ANN) and decoupled thermomechanical Finite Element Modelling(FEM). It illustrates the need to challenge long held conventions and improve on them : it shows the benefits of the modelling techniques as being useful indicative tools to determine the directions to be taken : lastly it shows in a quantitative manner how the application of the overall findings can produce very significant reductions in the rework required to rectify thin plate distortion.
Summary: A great deal of effort has been put into the study of thin plate distortion within steel structures. Much of this work has been academic based and has produced a number of key findings. However, there is a view that the application of some of these findings into industrial practice is limited and often understated as a significant number of the basic root causes of distortion have not been tackled. It is almost akin to having the rocket to go to the Moon but not having the launch pad.
This particular work will focus on a managed approach taken within a UK shipyard building naval vessels with a very significant proportion of thin plate (<8mm) in the structure.
It describes how the known factors were addressed, while at the same time parallel research work was being carried out using modelling techniques such as Artificial Neural Networks (ANN) and decoupled thermomechanical Finite Element Modelling(FEM). It illustrates the need to challenge long held conventions and improve on them : it shows the benefits of the modelling techniques as being useful indicative tools to determine the directions to be taken : lastly it shows in a quantitative manner how the application of the overall findings can produce very significant reductions in the rework required to rectify thin plate distortion.
