Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/104521
| Title: | Modelling and prediction of the effect of cutting strategy on surface generation in ultra-precision raster milling | Authors: | Wang, S Chen, X To, S Chen, X Liu, Q Liu, J |
Issue Date: | 2017 | Source: | International journal of computer integrated manufacturing, 2017, v. 30, no. 9, p. 895-909 | Abstract: | This paper studies the effect of cutting strategy on surface generation in ultra-precision raster milling (UPRM). By adding the influences of shift length and tool-interference on surface generation, a holistic surface roughness prediction model is built which takes into account the effect of cutting parameters, tool path generation, geometry parameters of diamond tool, the size of the workpiece and machine characteristics. The optimal shift ratio can be achieved by changing factors involved in developing cutting strategy to improve surface quality without decreasing machining efficiency. Conditions for the presence of tool-interference in UPRM are presented. Based on the holistic surface generation model, an integrated system is developed to automatically generate the numerical control program, and predict surface quality and machining efficiency. A series of cutting experiments has been conducted to verify the proposed surface generation model and test the performance of the integrated system. The experimental results agree well with the predicted results from the model and the integrated system. | Keywords: | cutting strategy surface generation surface roughness ultra-precision raster milling (UPRM) |
Publisher: | Taylor & Francis | Journal: | International journal of computer integrated manufacturing | ISSN: | 0951-192X | EISSN: | 1362-3052 | DOI: | 10.1080/0951192X.2016.1239029 | Rights: | © 2016 Informa UK Limited, trading as Taylor & Francis Group This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Computer Integrated Manufacturing on 01 Oct 2016 (published online), available at: http://www.tandfonline.com/10.1080/0951192X.2016.1239029. |
| Appears in Collections: | Journal/Magazine Article |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| To_Modelling_Prediction_Effect.pdf | Pre-Published version | 2.24 MB | Adobe PDF | View/Open |
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