Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Manufacturing Engineering | en_US |
| dc.creator | Mok, Siu-lung | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/4284 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | - |
| dc.rights | All rights reserved | en_US |
| dc.title | An intelligent system for the determination of initial process parameter setting for injection moulding | en_US |
| dcterms.abstract | The global competition that demands high quality plastic products and short time-to-market has made the current trial and error practice in the determination of initial process parameters for injection moulding become inadequate. According to the nature of the problem in initial process parameter setting for injection moulding, case based reasoning (CBR) is deemed to be a promising technique to handle the experience-based problems. In this research, a hybrid neural network and genetic algorithm (NNGA) approach was introduced to complement the CBR approach in the determination of initial process parameters for injection moulding, from which a Hybrid System for Injection Moulding (HSIM) was developed. In the system, initial process parameters of injection moulding are generated in two attempts. In the first attempt, initial process parameters are generated based on CBR approach. If there is no workable solution to be obtained from the first attempt, the second attempt in the generation of initial process parameters for injection moulding is performed based on hybrid NN-GA approach. HSIM was validated by using a commercial simulation package for injection moulding. Results of the system validation indicate that HSIM can generate a set of initial process parameters for injection moulding that can lead to the production of good quality moulded parts. Implementation of HSIM has also demonstrated that the time for the determination of initial process parameters for injection moulding can be greatly reduced, daily experience of moulding personnel in initial process parameter setting can be captured, and self-learning capability can be facilitated. | en_US |
| dcterms.extent | xiii, 139, 20 leaves : ill. ; 30 cm | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2000 | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.educationalLevel | M.Phil. | en_US |
| dcterms.LCSH | Injection molding of plastics -- Data processing | en_US |
| dcterms.LCSH | Artificial intelligence -- Industrial applications | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | open access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b15353850.pdf | For All Users | 5.34 MB | Adobe PDF | View/Open |
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