Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/85438
DC FieldValueLanguage
dc.contributorDepartment of Industrial and Systems Engineering-
dc.creatorCheng, Mei-na-
dc.identifier.urihttps://theses.lib.polyu.edu.hk/handle/200/2398-
dc.language.isoEnglish-
dc.titleOptimization of surface generation in ultra-precision multi-axis raster milling-
dc.typeThesis-
dcterms.abstractOver the last few decades, the optics industry has grown from a skill and manual based industry to one that is based on advanced optics design and manufacture. Nowadays, products in the optics industry are becoming more and more specialized and complicated to meet the increasing demands of customers. The more high-value-added end of the product spectrum has shifted to the design and fabrication of complex freeform surfaces with features and functional requirements which are crucial to the development of complex and micro-optical-electro-mechanical devices used in many photonics and telecommunication products and systems. The trend in the optics industry is to produce high quality surfaces using ultra-precision machining. The fabrication of high quality freeform surfaces is based on ultra-precision raster milling which allows direct machining of the freeform surfaces with sub-micrometric form accuracy and nanometric surface finish. Ultra-precision raster milling is an emerging manufacturing technology for the fabrication of high precision and high quality components with a surface roughness of less than 10 nm and a form error of less than 0.2 um without the need for any additional post-processing. It is frequently employed for the machining of ductile materials such as aluminum and copper. Moreover, the quality of a raster milled surface is based on a proper selection of cutting conditions and cutting strategies. However, the achievement of the high precision surface finish still relies heavily on the experience and skills of the machine operators by using an expensive trial-and-error approach when new work materials, new design of surfaces or new machine tools are used. Moreover, factors affecting the surface generation in ultra-precision raster milling are more complex and different, as compared with single-point diamond turning and conventional milling. In addition, the surface quality depends largely on the selection of cutting conditions and cutting strategies such as to whether cut in only one direction, or both, or, whether to perform raster cuts or spiral cuts, etc. As the optimal cutting conditions and cutting strategies depend largely on the factors affecting the surface quality, machining environment, work materials and the geometry of surfaces being cut, there is a need for an optimization system which can simulate and predict the effect of different factors on the surface being generated. As a result, the proposed study aims to establish an optimization system for predicting the cutting performance as well as the optimal cutting strategies for the ultra-precision raster milling. In the present study, an experimental investigation of the process factors affecting the surface quality in ultra-precision raster milling has been carried out which focuses on the study of the effect of process factors and cutting strategies in ultra-precision raster milling. It is found that the step distance is the most critical factor affecting the surface quality in ultra-precision raster milling. The influence due to process factors can be minimized or even eliminated through a proper selection of cutting conditions and cutting strategies. A study of critical ranges of cutting parameters for the optimization of surface quality has been conducted on aluminum alloy and copper alloy. Regardless of the work materials and cutting strategies used, the critical ranges of cutting parameters for the optimization of surface quality in ultra-precision raster milling have been determined. On the other hand, theoretical model for surface roughness have been derived based on the cutting mechanics, surface generation mechanism, factors affecting the surface quality and critical ranges of cutting parameters under various cutting conditions and cutting strategies. Based on theoretical and experimental work, an optimization system has been developed for the analysis of surface generation in ultra-precision raster milling which takes into account the effect of cutting mechanics, the surface generation mechanism, tool geometry, process parameters, and cutting strategies. The outputs of the optimization system is the suggested optimum cutting conditions, recommended cutting strategies and the projected performance characteristics diagrams which depicts variations in surface roughness with the process parameters and cutting strategies being investigated. The system has been experimentally verified through a series of simulation and cutting experiments. It is interesting to note that the experimental results agree well with the trend of the theoretical results. As a whole, the present study contributes to a better understanding of the surface generation mechanism and factors affecting surface generation in ultra-precision raster milling. The successful development of the optimization system allows the optimization of cutting parameters and cutting strategies to be determined prior to the actual production. This helps to find the best surface quality that can be achieved under a given cutting condition. This avoids the need to conduct massive and costly trial-and-error cutting tests. Finally, some suggestions are also recommended to further study.-
dcterms.accessRightsopen access-
dcterms.educationLevelM.Phil.-
dcterms.extentxv, 181, [131] leaves : ill. ; 30 cm.-
dcterms.issued2007-
dcterms.LCSHHong Kong Polytechnic University -- Dissertations.-
dcterms.LCSHMachining.-
dcterms.LCSHMilling machinery.-
dcterms.LCSHOptical instruments.-
dcterms.LCSHSurfaces (Technology)-
Appears in Collections:Thesis
Show simple item record

Page views

49
Last Week
1
Last month
Citations as of Mar 24, 2024

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.