This thesis documents a PhD research program undertaken at Swinburne
University of Technology between the years 2000 and 2004. The research was
funded by the Cooperative Research Centre for Cast Metals Manufacturing and was
undertaken in collaboration with Nissan Casting Plant Australia Pty Ltd and the Ford
Motor Company Australia Limited. This thesis reports on the investigation of the
possibility of using an ultrasonic sensing-based, non-destructive testing system to
detect gas porosity defects in aluminium die casting parts with rough surfaces. The
initial intention was to develop a procedure to obtain ultrasonic signals with the
maximum possible amplitude from defects within the rough surface areas of the
castings. A further intention was to identify defects with the application of a suitable
signal processing technique to the raw ultrasonic signal. The literature review has
indicated that ultrasonic techniques have the potential to be used to detect subsurface
defects in castings. The possibility of classifying very weak ultrasonic signals
obtained from rough surface sections of castings through a neural network approach
was also mentioned in the literature. An extensive search of the literature has
indicated that ultrasonic sensing techniques have not been successfully used to detect
sub-surface defects in aluminium die castings with rough surfaces.
Ultrasonic inspection of castings is difficult due to the influence of
microstructural variations, surface roughness and the complex shape of castings. The
design of the experimental set-up used is also critical in developing a proper
inspection procedure. The experimental set-up of an A-scan ultrasonic inspection rig
used in the research is described in this thesis. Calibration of the apparatus used in
the inspection rig was carried out to ensure the reliability and repeatability of the
results. This thesis describes the procedure used to determine a suitable frequency
range for the inspection of CA313 aluminium alloy castings and detecting porosity
defects while accommodating material variations within the part. The results
obtained from ultrasonic immersion testing indicated that focused probes operating at
frequencies between 5 MHz and 10 MHz are best suited for the inspection of
castings with surface roughness Ra values varying between 50 [micro milli] and 100 [micro milli]. For
the purpose of validating the proposed inspection methodology, gas porosity defects
were simulated through side-drilled holes in the in-gate section of selected sample
castings. Castings with actual porosity defects were also used in this research.
One of the conclusions of this research was that it was extremely difficult to
detect defects in castings with surface roughness above 125 [micro milli]. Once the ultrasonic
signal data was obtained from the sample aluminium die castings with different
surface roughness values ranging from 5 [micro milli] to 150 [micro milli] signal analysis was carried
out. Signal feature extraction was achieved using Fast Fourier Transforms (FFT),
Principal Component Analysis (PCA) and Wavelet Transforms (WT) prior to passing
the ultrasonic signals into a neural network for defect classification. MATLAB tools
were used for neural network and signal pre-processing analysis. The results
indicated that poor classification (less than 75%) was achieved with the WT, PCA
and combination of FFT/PCA and WT/PCA pre-processing techniques for rough
surface signals. However, the classification of the signals pre-processed with the
combination of WT/FFT, FFT/WT and FFT/WT/PCA classifiers provided much
better classification of more than 90% for smooth surface signals and 78% to 84%
for rough surface signals. The results obtained from ultrasonic testing of castings
with both real and simulated defects were validated with X-ray analysis of the
sample castings. The results obtained from this research encourage deeper
investigation of the detection and characterisation of sub-surface defects in castings
at the as-cast stage. Implications for the industrial application of these findings are
discussed and directions for further research presented in this thesis.
| Identifer | oai:union.ndltd.org:ADTP/216586 |
| Date | January 2006 |
| Creators | Palanisamy, Suresh, n/a |
| Publisher | Swinburne University of Technology. Industrial Research Institute Swinburne |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
| Rights | http://www.swin.edu.au/), Copyright Suresh Palanisamy |
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