Performance prediction of a specific wear rate in epoxy nanocomposites with various composition content of polytetrafluoroethylen (PTFE), graphite, short carbon fibers (CF) and nano-TiO2 using adaptive neuro-fuzzy inference system (ANFIS)
| Autor: | Saeid Nouri Khorasani, Dariush Semnani, Ali Haghighat Mesbahi |
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| Rok vydání: | 2012 |
| Předmět: |
Adaptive neuro fuzzy inference system
Materials science Mean squared error Artificial neural network Mechanical Engineering Epoxy Fuzzy control system Industrial and Manufacturing Engineering Mechanics of Materials visual_art Content (measure theory) Ceramics and Composites Performance prediction visual_art.visual_art_medium Graphite Composite material |
| Zdroj: | Composites Part B: Engineering. 43:549-558 |
| ISSN: | 1359-8368 |
| DOI: | 10.1016/j.compositesb.2011.11.026 |
| Popis: | Specific wear rate of composite materials plays a significant role in industry. The processes to measure it are both time and cost consuming. It is essential to suggest a modeling method to predict and analyze the effectiveness of parameters of specific wear rate. Nowadays, computational methods such as Artificial Neural Network (ANN), Fuzzy Inference System (FIS) and adaptive neuro-fuzzy inference system (ANFIS) are mainly considered as applicable tools from modeling point of view. ANFIS present integrate performance of neural network (NN) and fuzzy system (FS). Present paper investigates performance prediction of a specific wear rate of epoxy composites with various composition using ANFIS. The obtained results showed that ANFIS is a powerful tool in modeling specific wear rate. The obtained mean of squared error (MSE) for testing sets in present paper obtained 0.0071. |
| Databáze: | OpenAIRE |
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