Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Industrial and Systems Engineering | en_US |
| dc.creator | Cheng, Ka-po | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/422 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | - |
| dc.rights | All rights reserved | en_US |
| dc.title | Use of data farming for the determination of a manufacturing process window | en_US |
| dcterms.abstract | Many Hong Kong household electrical appliance manufacturers believe that the survival of their industry can only rely on the competence of a new product design and development process. However, if a new product cannot be manufactured in an effective and competitive way, the benefits that generated from the migration from original equipment manufacturing (OEM) to original design manufacturing (ODM) cannot be reaped. Currently, local manufacturing companies still rely largely on the traditional human experience driven approach to determine the process setting of a manufacturing process. As a result, product quality variations and inconsistency occur according to the tacit knowledge owned by different engineers and technicians. Even though a number of mathematical and statistical methodologies do exist for the determination of a process window, they are seldom used because of the huge consumption of both time and experimental resources whilst a certain extent of personnel expertise still required such as the choice of levels and settings in the Taguchi Method. Recently, the focus of research in process window design has been targeted on the exploration of the use of Artificial Intelligence (AI) algorithms to rid the dependency of human expertise. However, any change of a product geometry and its mould design will lead to the recreation of the knowledge database that make such approach impractical for real application at the moment. In order to cope with the above scenario, the research study "Use of Data Farming for the Determination of a Manufacturing Process Window" worked under the Teaching Company Scheme that funded by the Hong Kong Government's Innovation Technology Fund and the partnered "G.E.W. Corporation Limited" was set up. The research project was aimed to provide a structure workflow to find out the process window of a dedicated mould design/tooling for a particular manufacturing process that can eliminate the dependency of experience. The argument of the research project is that through the use of data farming, the tacit knowledge for the determination of a manufacturing process window can be farmed through the use of a set of fertilizing, cultivating, planting and harvest processes. The scope of the research study was intended to cover all sorts of manufacturing processes that possess the fundamental axioms with corresponding equipment/facility requirement(s). Due to the time and resource constraints, the verification of the proposed model could only be limited to the plastic injection moulding process. The research methodology adopted the data farming philosophy that consist of four different stages: (i) Feasibility study/Justification for the use of data farming of a certain manufacturing process (Fertilization); (ii) Exhibition of process parameters, axioms collection and formation of axioms database (Cultivation); (iii) Sequencing of process parameters and embedment of axioms (Planting); and (iv) Deployment of the process parameter setting window system (Harvest). Since the injection moulding process for plastic part fabrications can be regarded as one of the most complex manufacturing process, it was selected for the verification process and study of effectiveness of the suggested model. Two toaster parts that include dust cover (a thin walled plastic part) and moving bracket (a thick walled plastic part) were selected for the reconstruction of process windows and benchmarked with their original settings. It was found that significant reduction of both manufacturing cycle time (11% to 27%) and material consumption due to overpacking (4.7% to 5%) whilst all the critical dimensional tolerances were improved. It was also evidenced that the proposed methodology is capable to disclose the knowledge of a manufacturing process. Based on the result of the project study, the partnered company agreed that the use of data farming was very successful in farming tacit knowledge for the determination of the injection moulding process window and transforming the tacit knowledge to explicit knowledge through the construction of a structure workflow. However, the methodology will be limited by the availability of axiom(s) and equipment(s)/facility(ies). | en_US |
| dcterms.extent | xiv, 148 leaves : ill. (some col.) ; 30 cm | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2006 | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.educationalLevel | M.Phil. | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.LCSH | Manufacturing processes -- Automation | en_US |
| dcterms.LCSH | Production management -- Data processing | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b19579263.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 5.78 MB | Adobe PDF | View/Open |
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