Sugeno-Fuzzy Expert System Modeling for Quality Prediction of Non-Contact Machining Process
Modeling can be categorised into four main domains: prediction, optimisation, estimation and calibration. In this paper, the Takagi-Sugeno-Kang (TSK) fuzzy logic method is examined as a prediction modelling method to investigate the taper quality of laser lathing, which seeks to replace traditional...
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| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IOP Publishing
2018
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/23924/1/Sivaraos_2018_IOP_Conf._Ser.__Mater._Sci._Eng._318_012066.pdf http://umpir.ump.edu.my/id/eprint/23924/ https://doi.org/10.1088/1757-899X/318/1/012066 |
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| Summary: | Modeling can be categorised into four main domains: prediction, optimisation, estimation and calibration. In this paper, the Takagi-Sugeno-Kang (TSK) fuzzy logic method is
examined as a prediction modelling method to investigate the taper quality of laser lathing, which seeks to replace traditional lathe machines with 3D laser lathing in order to achieve the desired cylindrical shape of stock materials. Three design parameters were selected: feed rate, cutting speed and depth of cut. A total of twenty-four experiments were conducted with eight sequential runs and replicated three times. The results were found to be 99% of accuracy rate
of the TSK fuzzy predictive model, which suggests that the model is a suitable and practical method for non-linear laser lathing process. |
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