A systematic approach using machine learning and optimization techniques to improve manufacturing process efficiency
Efficiency in modern manufacturing process is very crucial. Manufactures these days want to have more control over the performance of their machines. This Final Year Project aims to using machine learning techniques to predict the outcome of a certain manufacturing process in order to improve the ma...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2014
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| Online Access: | http://hdl.handle.net/10356/60419 |
| _version_ | 1811682774140059648 |
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| author | Wei, Lai |
| author2 | Er Meng Joo |
| author_facet | Er Meng Joo Wei, Lai |
| author_sort | Wei, Lai |
| collection | NTU |
| description | Efficiency in modern manufacturing process is very crucial. Manufactures these days want to have more control over the performance of their machines. This Final Year Project aims to using machine learning techniques to predict the outcome of a certain manufacturing process in order to improve the manufacturing efficiency. This is a joint project together with other SIMTech scientists and staff. The manufacture process studied this project is a coating process of a thin plastic substrate. A coating machine was used with two controllable inputs namely the coating material flow rate and substrate rolling speed. Normally, the thickness of coating material is difficult to control due to the limitation of the machine. In this project, machine learning methods were studied and developed to predict the coating thickness. By using the historical coating thickness data, Matlab programs were developed to predict the coating thickness. Therefore, coating thickness can be controlled and the production efficiency can be improved. Besides, physical models were also developed to predict the coating thickness and give physical explanation at the same time. |
| first_indexed | 2024-10-01T04:02:11Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/60419 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:02:11Z |
| publishDate | 2014 |
| record_format | dspace |
| spelling | ntu-10356/604192023-07-07T16:41:48Z A systematic approach using machine learning and optimization techniques to improve manufacturing process efficiency Wei, Lai Er Meng Joo School of Electrical and Electronic Engineering A*STAR SIMTech DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Efficiency in modern manufacturing process is very crucial. Manufactures these days want to have more control over the performance of their machines. This Final Year Project aims to using machine learning techniques to predict the outcome of a certain manufacturing process in order to improve the manufacturing efficiency. This is a joint project together with other SIMTech scientists and staff. The manufacture process studied this project is a coating process of a thin plastic substrate. A coating machine was used with two controllable inputs namely the coating material flow rate and substrate rolling speed. Normally, the thickness of coating material is difficult to control due to the limitation of the machine. In this project, machine learning methods were studied and developed to predict the coating thickness. By using the historical coating thickness data, Matlab programs were developed to predict the coating thickness. Therefore, coating thickness can be controlled and the production efficiency can be improved. Besides, physical models were also developed to predict the coating thickness and give physical explanation at the same time. Bachelor of Engineering 2014-05-27T04:23:01Z 2014-05-27T04:23:01Z 2014 2014 Final Year Project (FYP) http://hdl.handle.net/10356/60419 en Nanyang Technological University 47 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Wei, Lai A systematic approach using machine learning and optimization techniques to improve manufacturing process efficiency |
| title | A systematic approach using machine learning and optimization techniques to improve manufacturing process efficiency |
| title_full | A systematic approach using machine learning and optimization techniques to improve manufacturing process efficiency |
| title_fullStr | A systematic approach using machine learning and optimization techniques to improve manufacturing process efficiency |
| title_full_unstemmed | A systematic approach using machine learning and optimization techniques to improve manufacturing process efficiency |
| title_short | A systematic approach using machine learning and optimization techniques to improve manufacturing process efficiency |
| title_sort | systematic approach using machine learning and optimization techniques to improve manufacturing process efficiency |
| topic | DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence |
| url | http://hdl.handle.net/10356/60419 |
| work_keys_str_mv | AT weilai asystematicapproachusingmachinelearningandoptimizationtechniquestoimprovemanufacturingprocessefficiency AT weilai systematicapproachusingmachinelearningandoptimizationtechniquestoimprovemanufacturingprocessefficiency |