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Prediction of burr formation during face milling using an artificial neural network with optimized cutting conditions
- Authors
- Lee, S. H.; Dornfeld, D. A.
- Issue Date
- Dec-2007
- Publisher
- SAGE PUBLICATIONS LTD
- Keywords
- face milling; burr; optimization; cutting parameters; Taguchi method; ANOVA; ANN
- Citation
- PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE, v.221, no.12, pp 1705 - 1714
- Pages
- 10
- Indexed
- SCIE
SCOPUS
- Journal Title
- PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE
- Volume
- 221
- Number
- 12
- Start Page
- 1705
- End Page
- 1714
- ISSN
- 0954-4054
2041-1975
- Abstract
- Burrs formed during face milling operations are difficult to characterize because there are several parameters with complex interactions that affect the cutting process. In this paper, a combined artificial intelligence and optimization approach is introduced to predict burr types formed during face milling. The Taguchi method was selected for the optimization and an artificial neural network (ANN) was constructed for the machining of aluminium alloy 6061-T6. For the training of the ANN, the input was non-dimensionalized using the optimized results from the Taguchi method. The resulting ANN output was in agreement with experimental results, validating the proposed scheme.
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Related Researcher
- Lee, Seoung Hwan
- ERICA 공학대학 (DEPARTMENT OF MECHANICAL ENGINEERING)