Autonomous quality monitoring for complex manufacturing process
This research investigates the possibilities of using different types of incremental learning algorithms and deep neural networks to monitor the quality of products produced from the complex manufacturing process through binary and multi-class classification to identify defects quickly. To find o...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
Nanyang Technological University
2020
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/138521 |
| _version_ | 1824455917031129088 |
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| author | Lee, Wen Siong |
| author2 | Mahardhika Pratama |
| author_facet | Mahardhika Pratama Lee, Wen Siong |
| author_sort | Lee, Wen Siong |
| collection | NTU |
| description | This research investigates the possibilities of using different types of incremental learning
algorithms and deep neural networks to monitor the quality of products produced from the complex
manufacturing process through binary and multi-class classification to identify defects quickly. To
find out which algorithm or neural network has the best result, I compared the performance of 4
different algorithms and 3 different neural networks using a set of time-series data collected as a
result of the production of a transparent mold from the Molding machine. The performance is done
by comparing with binary classification results from 4 different test settings. Additionally, the
neural networks will also be tested with multi-class classification.
The result has shown that deep neural networks generally performed better than incremental
learning algorithms with a prediction accuracy of >93% in all test settings in both binary and multi-
class classification. However, the prediction accuracy is slightly lower in the multi-class
classification. Therefore, future work such as combining Convolutional Neural Network (CNN)
and the deep neural network can be explored to boost the accuracy of the multi-class classification
further. |
| first_indexed | 2025-02-19T03:45:49Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/138521 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:45:49Z |
| publishDate | 2020 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1385212020-05-07T12:42:40Z Autonomous quality monitoring for complex manufacturing process Lee, Wen Siong Mahardhika Pratama School of Computer Science and Engineering Singapore Institute of Manufacturing Technology mpratama@ntu.edu.sg Engineering::Computer science and engineering This research investigates the possibilities of using different types of incremental learning algorithms and deep neural networks to monitor the quality of products produced from the complex manufacturing process through binary and multi-class classification to identify defects quickly. To find out which algorithm or neural network has the best result, I compared the performance of 4 different algorithms and 3 different neural networks using a set of time-series data collected as a result of the production of a transparent mold from the Molding machine. The performance is done by comparing with binary classification results from 4 different test settings. Additionally, the neural networks will also be tested with multi-class classification. The result has shown that deep neural networks generally performed better than incremental learning algorithms with a prediction accuracy of >93% in all test settings in both binary and multi- class classification. However, the prediction accuracy is slightly lower in the multi-class classification. Therefore, future work such as combining Convolutional Neural Network (CNN) and the deep neural network can be explored to boost the accuracy of the multi-class classification further. Bachelor of Engineering (Computer Science) 2020-05-07T12:42:40Z 2020-05-07T12:42:40Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/138521 en SCSE19-0075 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering Lee, Wen Siong Autonomous quality monitoring for complex manufacturing process |
| title | Autonomous quality monitoring for complex manufacturing process |
| title_full | Autonomous quality monitoring for complex manufacturing process |
| title_fullStr | Autonomous quality monitoring for complex manufacturing process |
| title_full_unstemmed | Autonomous quality monitoring for complex manufacturing process |
| title_short | Autonomous quality monitoring for complex manufacturing process |
| title_sort | autonomous quality monitoring for complex manufacturing process |
| topic | Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/138521 |
| work_keys_str_mv | AT leewensiong autonomousqualitymonitoringforcomplexmanufacturingprocess |