Consideration of fuzzy components for prediction of machining performance: a review
This paper presents the application of artificial intelligence techniques especially fuzzy logic (FL) in predicting machining performance. FL is chosen because it is widely used to predict the machining performances such as surface roughness, cutting force and material removal rate. Previous works o...
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| Main Authors: | , , |
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| Format: | Article |
| Published: |
Elsevier BV
2011
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/44815/ |
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| Institution: | Universiti Teknologi Malaysia |
| Summary: | This paper presents the application of artificial intelligence techniques especially fuzzy logic (FL) in predicting machining performance. FL is chosen because it is widely used to predict the machining performances such as surface roughness, cutting force and material removal rate. Previous works on FL focusing on fuzzy components has been presented. The FL components are fuzzification, fuzzy rule, inference engine and defuzzification. The review shows that the FL components for fuzzification, which is logical operator, membership function (MF) and IF-THEN rule, is the necessary facts that must be considered before applying FL in prediction. Fuzzy rule that is derived from fuzzification process is important in the development of inference engine. Therefore, the defuzzification of the inference engine will give desired fuzzy system. The review also revealed that there are several types of defuzzification which include centroid, bisector, smallest of maximum, mean of maximum and largest of maximum. There are important facts that must be considered in FL development. To conclude, this paper revealed that MF and defuzzification is important in predicting machining performance. It shows that for MF and defuzzification, triangular and centroid are respectively mostly used in the prediction process. |
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