A Neural Network Mobile Learning Application For Autonomous Improvement In A Flexible Manufacturing Environment
| dc.contributor.author | Siew, Jit Ping | |
| dc.date.accessioned | 2017-01-31T06:42:45Z | |
| dc.date.available | 2017-01-31T06:42:45Z | |
| dc.date.issued | 2016-08 | |
| dc.description.abstract | This study is focused on how an innovation based on telecommunication and computer technologies at a manufacturing facility “MF” is implemented to generate higher value returns. Modern manufacturing has evolved into a very competitive industry and wastages resulting from process defects are very costly. Based on a survey of the manufacturing floor activities, the stencil printing process (SPP) was selected as the area of research. This decision was based on literature reviews which indicated that at least 50% of defects in the printed circuit board (PCB) assembly originated from SPP, and actual defects data collected during the survey. Given the standing work environment of the machine operators who are continuously on the move, the challenge is therefore, to empower them with knowledge on their performances relative to defects with a mobile learning application, and to stimulate an autonomous process improvement. To attain this objective, a mobile device loaded with an Android app is used to present information that is processed by a neural network algorithm. The neural network algorithm is used to analyze the past performances of each crew relative to the tasks that are performed in a flexible manufacturing environment, and make prediction on the expected performance for each task. The core of the learning app is in the use of a graphical two-way table, introduced as an inferior-superior-neutral (ISN) matrix. This empowerment of knowledge, which leveraged on the extensive work experience of the manufacturing crews, led to two improvements in the SPP performance. Firstly, crew B achieved zero defects after 9 months of project implementation, while defect rates for crew A reduced by almost 90%. Secondly, the divergence between defect rates of crew A and B, as indicated by the regression model, reduced dramatically. This proved that the mobile learning application has been successful in reducing the knowledge gap and enabled a consistent performance between the two crews. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/3624 | |
| dc.subject | An innovation based on telecommunication and computer technologies | en_US |
| dc.subject | at a manufacturing facility. | en_US |
| dc.title | A Neural Network Mobile Learning Application For Autonomous Improvement In A Flexible Manufacturing Environment | en_US |
| dc.type | Thesis | en_US |
Files
License bundle
1 - 1 of 1
Loading...
- Name:
- license.txt
- Size:
- 1.71 KB
- Format:
- Item-specific license agreed upon to submission
- Description: