Integration Of Strength and Process Modelling of Friction-Stir-Welded Fuselage Panels

In order to reduce potential uncertainties and conservatism in welded panel analysis procedures, understanding of the relationships between welding process parameters and static strength is required. The aim of this study is to determine and characterize the key process induced properties of advanced welding assembly methods on stiffened panel local buckling and collapse performance. To this end, an in-depth experimental and computational study of the static strength of a friction stir welded fuselage skin-stiffener panel subjected to compression loading has been undertaken. Four welding process effects, viz. the weld joint width, the width of the weld Heat Affected Zone, the strength of material within the weld Heat Affected Zone and the magnitude of welding induced residual stress, are investigated. A fractional factorial experiment design method (Taguchi) has been applied to identify the relative importance of each welding process effect and investigate effect interactions on both local skin buckling and crippling collapse performance. For the identified dominant welding process effects, parametric studies have been undertaken to identify critical welding process effect magnitudes and boundaries. The studies have shown that local skin buckling is principally influenced by the magnitude of welding induced residual stress and that the strength of material in the Heat Affected Zone and the magnitude of the welding induced residual stress have the greatest influence on crippling collapse behavior.


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Process Modelling for Control of Product Wall Thickness in Thermoforming

The present paper describes the results of an investigation into the modelling of plug assisted thermoforming. The objective of this work was to improve the finite element modelling of thermoforming through an enhanced understanding of the physical elements underlying the process. Experiments were carried out to measure the effects on output of changes in major parameters and simultaneously simple finite element models were constructed. The experimental results show that the process creates conflicting and interrelated contact friction and heat transfer effects that largely dictate the final wall thickness distribution. From the simulation work it was demonstrated that a high coefficient of friction and no heat transfer can give a good approximation of the actual wall thickness distribution. However, when conduction was added to the model the results for lower friction values were greatly improved. It was concluded that further work is necessary to provide realistic measurements and models for contact effects in thermoforming.

Modelling and visualisation of material flow in friction stir spot welding

Abstract The material flow in friction stir spot welding of aluminium to both aluminium and steel has been investigated, using pinless tools in a lap joint geometry. The flow behaviour was revealed experimentally using dissimilar Al alloys of similar strength. The effect on the material flow of tool surface features, welding conditions (rotation speed, plunge depth, dwell time), and the surface state of the steel sheet (un-coated or galvanized) have been systematically studied. A novel kinematic flow model is presented, which successfully predicts the observed layering of the dissimilar Al alloys under a range of conditions. The model and the experimental observations provide a consistent interpretation of the stick-slip conditions at the tool-workpiece interface, addressing an elusive and long-standing issue in the modelling of heat generation in friction stir processing.

Extending BPM Environments of Your Choice with Performance Related Decision Support

What-if Simulations have been identified as one solution for business performance related decision support. Such support is especially useful in cases where it can be automatically generated out of Business Process Management (BPM) Environments from the existing business process models and performance parameters monitored from the executed business process instances. Currently, some of the available BPM Environments offer basic-level performance prediction capabilities. However, these functionalities are normally too limited to be generally useful for performance related decision support at business process level. In this paper, an approach is presented which allows the non-intrusive integration of sophisticated tooling for what-if simulations, analytic performance prediction tools process optimizations or a combination Of Such solutions into already existing BPM environments. The approach abstracts from process modelling techniques which enable automatic decision support spanning processes across numerous BPM Environments. For instance, this enables end-to-end decision support for composite processes modelled with the Business Process Modelling Notation (BPMN) on top of existing Enterprise Resource Planning (ERP) processes modelled with proprietary languages.

A novel approach to dynamic modelling of polymer extrusion for improved process control

Polymer extrusion is a complex process and the availability of good dynamic models is key for improved system operation. Previous modelling attempts have failed adequately to capture the non-linearities of the process or prove too complex for control applications. This work presents a novel approach to the problem by the modelling of extrusion viscosity and pressure, adopting a grey box modelling technique that combines mechanistic knowledge with empirical data using a genetic algorithm approach. The models are shown to outperform those of a much higher order generated by a conventional black box technique while providing insight into the underlying processes at work within the extruder.

Enhancing process insight in polymer extrusion by grey box modelling

Due to the complexity and inherent instability in polymer extrusion there is a need for process models which can be run on-line to optimise settings and control disturbances. First-principle models demand computationally intensive solution, while ‘black box’ models lack generalisation ability and physical process insight. This work examines a novel ‘grey box’ modelling technique which incorporates both prior physical knowledge and empirical data in generating intuitive models of the process. The models can be related to the underlying physical mechanisms in the extruder and have been shown to capture unpredictable effects of the operating conditions on process instability. Furthermore, model parameters can be related to material properties available from laboratory analysis and as such, lend themselves to re-tuning for different materials without extensive remodelling work.

Neural modelling, control and optimisation of an industrial grinding process

This paper describes the development of neural model-based control strategies for the optimisation of an industrial aluminium substrate disk grinding process. The grindstone removal rate varies considerably over a stone life and is a highly nonlinear function of process variables. Using historical grindstone performance data, a NARX-based neural network model is developed. This model is then used to implement a direct inverse controller and an internal model controller based on the process settings and previous removal rates. Preliminary plant investigations show that thickness defects can be reduced by 50% or more, compared to other schemes employed. (c) 2004 Elsevier Ltd. All rights reserved.