Detection and Analysis of Molten Aluminium Cleanliness Using a Pulsed Ultrasound System
| dc.contributor.advisor | Mishra, Amit | |
| dc.contributor.author | Matlala, Kgothatso | |
| dc.date.accessioned | 2023-03-30T14:17:34Z | |
| dc.date.available | 2023-03-30T14:17:34Z | |
| dc.date.issued | 2022 | |
| dc.date.updated | 2023-03-29T13:34:43Z | |
| dc.description.abstract | 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. | |
| dc.identifier.apacitation | Matlala, K. (2022). <i>Detection and Analysis of Molten Aluminium Cleanliness Using a Pulsed Ultrasound System</i>. (). ,Faculty of Engineering and the Built Environment ,Department of Electrical Engineering. Retrieved from http://hdl.handle.net/11427/37580 | en_ZA |
| dc.identifier.chicagocitation | Matlala, Kgothatso. <i>"Detection and Analysis of Molten Aluminium Cleanliness Using a Pulsed Ultrasound System."</i> ., ,Faculty of Engineering and the Built Environment ,Department of Electrical Engineering, 2022. http://hdl.handle.net/11427/37580 | en_ZA |
| dc.identifier.citation | Matlala, K. 2022. Detection and Analysis of Molten Aluminium Cleanliness Using a Pulsed Ultrasound System. . ,Faculty of Engineering and the Built Environment ,Department of Electrical Engineering. http://hdl.handle.net/11427/37580 | en_ZA |
| dc.identifier.ris | TY - Master Thesis AU - Matlala, Kgothatso AB - 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. DA - 2022_ DB - OpenUCT DP - University of Cape Town KW - Electrical Engineering LK - https://open.uct.ac.za PY - 2022 T1 - Detection and Analysis of Molten Aluminium Cleanliness Using a Pulsed Ultrasound System TI - Detection and Analysis of Molten Aluminium Cleanliness Using a Pulsed Ultrasound System UR - http://hdl.handle.net/11427/37580 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/37580 | |
| dc.identifier.vancouvercitation | Matlala K. Detection and Analysis of Molten Aluminium Cleanliness Using a Pulsed Ultrasound System. []. ,Faculty of Engineering and the Built Environment ,Department of Electrical Engineering, 2022 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/37580 | en_ZA |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | Department of Electrical Engineering | |
| dc.publisher.faculty | Faculty of Engineering and the Built Environment | |
| dc.subject | Electrical Engineering | |
| dc.title | Detection and Analysis of Molten Aluminium Cleanliness Using a Pulsed Ultrasound System | |
| dc.type | Master Thesis | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationlevel | MSc (Eng) |