Influence of Manufacturing Tolerance on Aircraft Direct Operating Cost (DOC)

The influence of manufacturing tolerance on direct operating cost (DOC) is extrapolated from an engine nacelle to be representative of an entire aircraft body. Initial manufacturing tolerance data was obtained from the shop floor at Bombardier Aerospace Shorts, Belfast while the corresponding costs were calculated according to various recurring elements such as basic labour and overtime labour, rework, concessions, and redeployment; along with the non-recurrent costs due to tooling and machinery, etc. The relation of tolerance to cost was modelled statistically so that the cost impact of tolerance change could be ascertained. It was shown that a relatively small relaxation in the assembly and fabrication tolerances of the wetted surfaces resulted in reduced costs of production that lowered aircraft DOC, as the incurred drag penalty was predicted and taken into account during the optimisation process.

Parametric optimisation of manufacturing tolerances at the aircraft surface

Up until now, aircraft surface smoothness requirements have been aerodynamically driven with tighter manufacturing tolerance to minimize drag, that is, the tighter the tolerance, the higher is the assembly cost in the process of manufacture. In the current status of commercial transport aircraft operation, it can be seen that the unit cost contributes to the aircraft direct operating cost considerably more than the contribution made by the cost of block fuel consumed for the mission profile. The need for a customer-driven design strategy to reduce direct operating cost by reducing aircraft cost through manufacturing tolerance relaxation at the wetted surface without unduly penalizing parasite drag is investigated. To investigate this, a preliminary study has been conducted at 11 key manufacturing features on the surface assembly of an isolated nacelle. In spite of differences in parts design and manufacture, the investigated areas associated with the assembly of nacelles are typical of generic patterns in the assembly of other components of aircraft. The study is to be followed up by similar studies extended to lifting surfaces and fuselage

Dynamic Multivariate Statistical Process Control using Subspace Identification

This paper points out a serious flaw in dynamic multivariate statistical process control (MSPC). The principal component analysis of a linear time series model that is employed to capture auto- and cross-correlation in recorded data may produce a considerable number of variables to be analysed. To give a dynamic representation of the data (based on variable correlation) and circumvent the production of a large time-series structure, a linear state space model is used here instead. The paper demonstrates that incorporating a state space model, the number of variables to be analysed dynamically can be considerably reduced, compared to conventional dynamic MSPC techniques.

The Characterization of Friction Stir Welding Process Effects on Stiffened Panel Buckling Performance

The introduction of advanced welding methods as an alternative joining process to riveting in the manufacture of primary aircraft structure has the potential to realize reductions in both manufacturing costs and structural weight. However, welding processes can introduce undesirable residual stresses and distortions in the final fabricated components, as well as localized loss of mechanical properties at the weld joints. The aim of this research is to determine and characterize the key process effects of advanced welding assembly methods on stiffened panel static strength performance. This in-depth understanding of the relationships between welding process effects and buckling and collapse strength is required to achieve manufacturing cost reductions without introducing structural analysis uncertainties and hence conservative over designed welded panels. This current work is focused at the sub-component level and examines the static strength of friction stir welded multi stiffener panels. The undertaken experimental and computational studies have demonstrated that local skin buckling is predominantly influenced by the magnitude of welding induced residual stresses and associated geometric distortions, whereas panel collapse behavior is sensitive to the lateral width of the physically joined skin and stiffener flange material, the strength of material in the Heat Affected Zone as well as the magnitude of the welding induced residual stresses. Copyright © 2006 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.


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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.

Lean software process

This paper shows how the concepts of lean manufacturing can be successfully applied to software development. The key lean concept is to have a minimum of work in progress, which forces problems into the open. The time is then taken to fix the production system so the errors will not occur again.

A work-based research assessment of the impact of 'lean manufacturing' on health and safety education within an SME

Globalisation has had a major impact on the engineering industry as pacific Rim countries undercut manufacturing costs and provide a more cost-effective location for many businesses. Engineering in Nortehrn Ireland has mostly declined owing to increased competition from these countries. Engineering companies are now forced to streamline their production processes and employ cost-reducing practices in order to meet customer demands at reduced prices. This article aims to analyse the effects of one such streamlining endeavour which was first introduced after World War II in Japan- 'lean manufacturing' . 'Lean manufacturing' aims to reduce all wasteful activities within the production process in order to improve productivity, while reducing manufacturing costs. The work-based project under consideration was concerned with the impact 'lean manufacturing' may have on health and safety performance and education within an engineering company. The focus of the project was to determine through work-based research, and quantitative analysis, the employee perception on health and safety: has it changed (either positively or negatively), as a consequence of implementing 'lean manufacturing'.

Improved Nonlinear PCA for Process Monitoring Using Support Vector Data Description

Nonlinear principal component analysis (PCA) based on neural networks has drawn significant attention as a monitoring tool for complex nonlinear processes, but there remains a difficulty with determining the optimal network topology. This paper exploits the advantages of the Fast Recursive Algorithm, where the number of nodes, the location of centres, and the weights between the hidden layer and the output layer can be identified simultaneously for the radial basis function (RBF) networks. The topology problem for the nonlinear PCA based on neural networks can thus be solved. Another problem with nonlinear PCA is that the derived nonlinear scores may not be statistically independent or follow a simple parametric distribution. This hinders its applications in process monitoring since the simplicity of applying predetermined probability distribution functions is lost. This paper proposes the use of a support vector data description and shows that transforming the nonlinear principal components into a feature space allows a simple statistical inference. Results from both simulated and industrial data confirm the efficacy of the proposed method for solving nonlinear principal component problems, compared with linear PCA and kernel PCA.

The inferential monitoring of the screw disturbance torque to predict process fluctuations in polymer extrusion

Polymer extrusion is one of the major methods of processing polymer materials and advanced process monitoring is important to ensure good product quality. However, commonly used process monitoring devices, e.g. temperature and pressure sensors, are limited in providing information on process dynamics inside an extruder barrel. Screw load torque dynamics, which may occur due to changes in solids conveying, melting, mixing, melt conveying, etc., are believed to be a useful indicator of process fluctuations inside the extruder barrel. However, practical measurement of the screw load torque is difficult to achieve. In this work, inferential monitoring of the screw load torque signal in an extruder was shown to be possible by monitoring the motor current (armature and/or field) and simulation studies were used to check the accuracy of the proposed method. The ability of this signal to aid identification and diagnosis of process issues was explored through an experimental investigation. Power spectral density and wavelet frequency analysis were implemented together with a covariance analysis. It was shown that the torque signal is dominated by the solid friction in the extruder and hence it did not correlate well with melting fluctuations. However, it is useful for online identification of solids conveying issues.

The Influence of Friction Stir Welding Process Idealization on Residual Stress and Distortion Predictions for Future Airframe Assembly Simulations

The ability to accurately predict residual stresses and resultant distortions is a key product from process assembly simulations. Assembly processes necessarily consider large structural components potentially making simulations computationally expensive. The objective herein is to develop greater understanding of the influence of friction stir welding process idealization on the prediction of residual stress and distortion and thus determine the minimum required modeling fidelity for future airframe assembly simulations. The combined computational and experimental results highlight the importance of accurately representing the welding forging force and process speed. In addition, the results emphasize that increased CPU simulation times are associated with representing the tool torque, while there is potentially only local increase in prediction fidelity.