Surface roughness prediction in high speed end milling using adaptive neuro-fuzzy inference system
One of the significant characteristics in machining process is final quality of surface. The best measurement for this quality is the surface roughness. Therefore, estimating the surface roughness before the machining is a serious matter. The aim of this research is to estimate and simulate the aver...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Trans Tech Publications, Switzerland
2015
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/42930/ http://irep.iium.edu.my/42930/ http://irep.iium.edu.my/42930/ http://irep.iium.edu.my/42930/1/42930_-_Surface_roughness_prediction_in_high_speed_end_milling_using.pdf |
| Summary: | One of the significant characteristics in machining process is final quality of surface. The best measurement for this quality is the surface roughness. Therefore, estimating the surface roughness before the machining is a serious matter. The aim of this research is to estimate and simulate the average surface roughness (Ra) in high speed end milling. An experimental work was conducted to measure the surface roughness. A set of experimental runs based on box behnken design was conducted to machine carbon steel using coated carbide inserts. Moreover, the Adaptive Neuro-Fuzzy Inference System (ANFIS) has been used as one of the unconventional methods to develop a model that can predict the surface roughness. The adaptive-network-based fuzzy inference system (ANFIS) was found to be capable of high accuracy predictions for surface roughness within the range of the research boundaries. |
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