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Detection and Analysis of Molten Aluminium Cleanliness Using a Pulsed Ultrasound System
This document presents the development of a solution for analysis and detection of molten metal quality deviations. The data is generated by an MV20/20, an ultrasound sensor that detects inclusions - molten metal defects that affect the quality of the product. The data is then labelled by assessing...
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| Format: | Thesis |
| Language: | English |
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Department of Electrical Engineering
2023
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| _version_ | 1867613909776596992 |
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| access_status_str | Open Access |
| author | Matlala, Kgothatso |
| author2 | Mishra, Amit |
| author_browse | Matlala, Kgothatso Mishra, Amit |
| author_facet | Mishra, Amit Matlala, Kgothatso |
| author_sort | Matlala, Kgothatso |
| collection | Thesis |
| description | This document presents the development of a solution for analysis and detection of molten metal quality deviations. The data is generated by an MV20/20, an ultrasound sensor that detects inclusions - molten metal defects that affect the quality of the product. The data is then labelled by assessing the sample using metallography. The analysis provides the sample outcome and dominant inclusion. The business objectives for the project include the real-time classification of anomalous events by means of a supervised classifier for the metal quality outcome, and a classifier for the inclusion type responsible for low quality. The adopted methodology involves descriptive, diagnostic and predictive analytics. Once the data is statistically profiled, it is standardised and scaled to unit variance in order to compensate for different units in the descriptors. Principal components analysis is applied as a dimensionality reduction technique, and it is found that the first three components account for 99.6% of the variance of the dataset. In order for the system to have predictive ability, two modelling approaches are considered, namely Response Surface Methodology and supervised machine learning. Supervised machine learning is preferred as it offers more flexibility than a polynomial approximator, and it is more accurate. Four classifiers are built, namely logistic regression, support vector machine, multi-layer perceptron and a radial basis function network. The hyperparameters are tuned using 10- fold repeated cross-validation. The multi-layer perceptron offers the best performance in all cases. For determining the quality outcome of a cast (passed or failed), all the models perform according to business targets for accuracy, precision, sensitivity and specificity. For the inclusion type classification, the multi-layer perceptron performs within 5% of the target metrics. In order to optimise the model, a grid search is performed for optimal parameter tuning. The results offer negligible improvement, which indicates that the model has reached a global maximum in the parameter optimisation in the hyperspace. It is noted that the source of variance in the inclusion type data respondent is attributed to operator error during labelling of the dataset, among several other sources of variance. It is therefore recommended that a Gage R&R be performed in order to identify sources of variation, among other improvement recommendations. From a research perspective, a vision system is recommended for assessing metal colour, texture and other visual properties in order to provide more insights. Another possible research extension recommended is the use of Fourier Transform Infrared Spectroscopy in determining signatures of the clean metal and different inclusions for detection. The project is regarded as a success, as the business metrics are met by the solution. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/37580 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:43:38.717Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Department of Electrical Engineering |
| publisherStr | Department of Electrical Engineering |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/37580 Detection and Analysis of Molten Aluminium Cleanliness Using a Pulsed Ultrasound System Matlala, Kgothatso Mishra, Amit Electrical Engineering This document presents the development of a solution for analysis and detection of molten metal quality deviations. The data is generated by an MV20/20, an ultrasound sensor that detects inclusions - molten metal defects that affect the quality of the product. The data is then labelled by assessing the sample using metallography. The analysis provides the sample outcome and dominant inclusion. The business objectives for the project include the real-time classification of anomalous events by means of a supervised classifier for the metal quality outcome, and a classifier for the inclusion type responsible for low quality. The adopted methodology involves descriptive, diagnostic and predictive analytics. Once the data is statistically profiled, it is standardised and scaled to unit variance in order to compensate for different units in the descriptors. Principal components analysis is applied as a dimensionality reduction technique, and it is found that the first three components account for 99.6% of the variance of the dataset. In order for the system to have predictive ability, two modelling approaches are considered, namely Response Surface Methodology and supervised machine learning. Supervised machine learning is preferred as it offers more flexibility than a polynomial approximator, and it is more accurate. Four classifiers are built, namely logistic regression, support vector machine, multi-layer perceptron and a radial basis function network. The hyperparameters are tuned using 10- fold repeated cross-validation. The multi-layer perceptron offers the best performance in all cases. For determining the quality outcome of a cast (passed or failed), all the models perform according to business targets for accuracy, precision, sensitivity and specificity. For the inclusion type classification, the multi-layer perceptron performs within 5% of the target metrics. In order to optimise the model, a grid search is performed for optimal parameter tuning. The results offer negligible improvement, which indicates that the model has reached a global maximum in the parameter optimisation in the hyperspace. It is noted that the source of variance in the inclusion type data respondent is attributed to operator error during labelling of the dataset, among several other sources of variance. It is therefore recommended that a Gage R&R be performed in order to identify sources of variation, among other improvement recommendations. From a research perspective, a vision system is recommended for assessing metal colour, texture and other visual properties in order to provide more insights. Another possible research extension recommended is the use of Fourier Transform Infrared Spectroscopy in determining signatures of the clean metal and different inclusions for detection. The project is regarded as a success, as the business metrics are met by the solution. 2023-03-30T14:17:34Z 2023-03-30T14:17:34Z 2022 2023-03-29T13:34:43Z Master Thesis Masters MSc (Eng) http://hdl.handle.net/11427/37580 eng application/pdf Department of Electrical Engineering Faculty of Engineering and the Built Environment |
| spellingShingle | Electrical Engineering Matlala, Kgothatso Detection and Analysis of Molten Aluminium Cleanliness Using a Pulsed Ultrasound System |
| thesis_degree_str | Master's |
| title | Detection and Analysis of Molten Aluminium Cleanliness Using a Pulsed Ultrasound System |
| title_full | Detection and Analysis of Molten Aluminium Cleanliness Using a Pulsed Ultrasound System |
| title_fullStr | Detection and Analysis of Molten Aluminium Cleanliness Using a Pulsed Ultrasound System |
| title_full_unstemmed | Detection and Analysis of Molten Aluminium Cleanliness Using a Pulsed Ultrasound System |
| title_short | Detection and Analysis of Molten Aluminium Cleanliness Using a Pulsed Ultrasound System |
| title_sort | detection and analysis of molten aluminium cleanliness using a pulsed ultrasound system |
| topic | Electrical Engineering |
| url | http://hdl.handle.net/11427/37580 |
| work_keys_str_mv | AT matlalakgothatso detectionandanalysisofmoltenaluminiumcleanlinessusingapulsedultrasoundsystem |