TH-SOF-1713-A Machine Learning Framework for Improving Diagnosability of a Reconfigurable Manufacturing System (Record no. 576851)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01941nam a22001217a 4500 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 200 THE |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | MUHAMMAD FAISAL RANA |
| 9 (RLIN) | 69659 |
| 245 ## - TITLE STATEMENT | |
| Title | TH-SOF-1713-A Machine Learning Framework for Improving Diagnosability of a Reconfigurable Manufacturing System |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | ISLAMABAD |
| Name of publisher, distributor, etc. | NUST COLLEGE OF EME |
| Date of publication, distribution, etc. | 2017 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | MS-MECH-84 MSTHESIS ABSTRACT. Reconfigurable Manufacturing Systems (RMS) effectively respond to fluctuating market needs and customer demands for finished product. Diagnosability is a supporting characteristic of RMS that has a say in the quality of finished product. Cost and time taken for manufacturing are also considerably affected if proper diagnosability measures are not taken. Previous studies on Diagnosability of RMS have been studied from Axiomatic System Theory as such Design For Diagnosability (DFD). Nevertheless Diagnosability remains to be the least studied characteristic of RMS. With the availability of digitized data, Machine Learning approaches to advance manufacturing have proven to be considerably effective. A research gap existed for the application of Machine Learning techniques in improving the Diagnosability of RMS. A framework of Machine Learning has been proposed to address this gap. The working of the framework has been illustrated by two demonstrations from the available datasets, one in identifying proper signals in semi-conductor manufacturing to predict excursions, and the second in predicting machine failures due to a variety of factors. The framework is rendered in a concurrent-engineering fashion. The framework is tested against two available manufacturing datasets. Increase in Diagnosability will decrease the cost and time taken to production. Key Words: Reconfigurable Manufacturing Systems, Machine Learning, Artificial Intelligence, Preventive Maintenance, Intelligent Manufacturing |
| 9 (RLIN) | 69660 |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Supervisor DR SAJID ULLAH BUTT |
| 9 (RLIN) | 69661 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Computer Files |
| Withdrawn status | Lost status | Damaged status | Not for loan | Permanent Location | Current Location | Shelving location | Date acquired | Total Checkouts | Full call number | Barcode | Date last seen | Price effective from | Koha item type |
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| College of Electrical & Mechanical Engineering (CEME) | College of Electrical & Mechanical Engineering (CEME) | Reference | 11/05/2019 | 200 THE | TH-SOF-1713 | 06/28/2021 | 06/28/2021 | Computer Files |