VGU RESEARCH REPOSITORY
Please use this identifier to cite or link to this item:
https://epub.vgu.edu.vn/handle/dlibvgu/1152
DC Field | Value | Language |
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dc.contributor.author | Vu Nhat Minh | en_US |
dc.date.accessioned | 2021-10-13T03:17:07Z | - |
dc.date.available | 2021-10-13T03:17:07Z | - |
dc.date.issued | 2021 | - |
dc.identifier.uri | http://epub.vgu.edu.vn/handle/dlibvgu/1152 | - |
dc.description.abstract | The quality and efficiency of production planning and scheduling is a critical success factor for manufacturing companies' long-term competitiveness. The increase in complex operations, multiple routing, and various features made traditional average calculation and derived from historical data is no longer effective. In consequence, deficiency of PPS increases risks of failure and lost competitive values. Manufacturers have faced an increasing need for machine learning approaches to overcome this challenge. Adopting new technologies is the compulsory option for those companies to survive and sustainable in the fast-changing world. In particular, this thesis focuses on solar manufacturing. We retrieved relevant studies to understand how to deploy one Machine Learning project from building the team to analyze the method and features used and provide the suggestion for further study. We create methods for continual prediction of product quality before final testing. In our modeling approach, with advances in machine learning algorithms and predictive analytics techniques, it is possible to measure the absolute power of panel after main manufacturing process. Based on the prediction model's results and accuracy, early corrective manufacturing actions maybe taken to increase the Watts power of underrating panels or decrease the Watts power of overrating panels. This prediction model also is used to initiate corrective production planning and scheduling actions based on forecast reports. The model is an essential step towards the realization of a fully integrated production system. Our models will select panels after predicted in a real manufacturing pilot application, and it will visualize prediction results and actual data. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Vietnamese-German University | en_US |
dc.subject | Solar manufacturing | en_US |
dc.subject | Machine learning | en_US |
dc.title | A case study of applying machine learning in solar manufacturing | en_US |
dc.type | Thesis | en_US |
item.grantfulltext | restricted | - |
item.fulltext | With Fulltext | - |
item.languageiso639-1 | other | - |
Appears in Collections: | Business Information Systems (BIS) |
Files in This Item:
File | Description | Size | Format | Existing users please Login |
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A case study of applying machine learning in solar manufacturing.pdf | 2.29 MB | Adobe PDF |
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