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This paper provides a review of the fatigue properties reported in the open literature for die cast magnesium-based alloys. Recently developed fatigue data, in the form of stress versus number of cycles to failure for bending fatigue (R=-1), are presented for die cast AM60B and AZ91D alloy specimens with thicknesses between 1 and 10 mm. The effects of specimen thickness and macrostructural features, such as porosity distributions and surface features (parting line and ejection pin marks), on the fatigue data are discussed.

Wavelets have been established as an essential tool for the analysis of non-stationary signals. This paper presents a novel approach to the analysis of non-intrusive pressure traces using wavelets. It starts with a background to wavelets, how they might be useful for the analysis of signals from internal combustion engines and some results obtained from the analysis of the non-intrusive in-cylinder pressure data. The signals obtained from the non-intrusive in-cylinder pressure sensors contain random and statistical noise. They are also contaminated with reflections and resonances from the structures within the engine. Results presented at SAE99 [ref 1] show that the signals contain useful information for the determination of timing and pressures. The purpose of this paper is to present a novel approach to the processing and analysis of these signals.

Reported tensile and fatigue properties of die cast AZ91D and AM60B magnesium alloys indicate that those values depend on the size and shape of the test samples and their global porosities. This paper reviews the mechanical properties reported in the open literature for these die cast alloys and indicates that section thickness and global porosity are inadequate for predicting the tensile and fatigue properties of die cast AZ91D and AM60B magnesium alloys.

Die cast AZ91D and AM60 magnesium alloy components are finding increasing usage in automotive applications. Both hot and cold chamber die cast components of these alloys generally exhibit several common microstructural features, including “skin”, porosity banding, and porosity distributed about the component centerline. Methods for quantitatively characterizing these microstructural features are described and representative values for skin thicknesses, porosity band dimensions and porosity band locations from selected die castings will be presented. The expected influence of these common microstrucutral features on mechanical properties and acceptability of die cast magnesium components for given applications are discussed.

The monitoring of in-cylinder pressure on internal combustion engines is a costly and intrusive process. Current methods include high cost, miniature pressure transducers mounted in the cylinder head. The transducers have direct access to cylinder pressure, and give a high degree of accuracy. The disadvantage of using this intrusive method is the cost of the sensors and modification of the cylinder head. The monitoring of in-cylinder pressure is usually restricted to research engines or large marine diesels. However, the information carried in the pressure trace is significant and is of benefit in condition monitoring and control. The aim of this research is to produce low cost, non-intrusive sensors to enable the monitoring of in-cylinder pressure, on internal combustion engines. This would enable the use of pressure information to be extended into small in-service internal combustion engines.

In both the marine and power industries there are now a choice of off-the-shelf condition monitoring systems available that utilise artificial intelligence techniques to analyse engine performance data. These systems are proving to be a valuable aid in optimising performance and reducing down-time by assisting with maintenance planning. These systems rely on careful monitoring of an engine's performance, for instance engine speed, fuelling, boost pressure, turbine inlet pressure, turbocharger speed, and exhaust temperature. With this data, they utilise a variety of interpolation and pattern recognition algorithms to compare it with previously recorded data stored in lookup tables. This paper describes how a neural network approach can be used as a cheap alternative for the analysis of this data, greatly reducing the need for such large lookup tables and complex pattern recognition programs.