Yang, Nan and Suhatril, Meldi and Mohammed, Khidhair Jasim and Ali, H. Elhosiny (2023) Application of adaptive neuro-fuzzy system in prediction of nanoscale and grain size effects on formability. Advances in Nano Research, 14 (2). pp. 155-164. ISSN 2287-237X, DOI https://doi.org/10.12989/anr.2023.14.2.155.
Full text not available from this repository.Abstract
Grain size in sheet metals in one of the main parameters in determining formability. Grain size control in industry requires delicate process control and equipment. In the present study, effects of grain size on the formability of steel sheets is investigated. Experimental investigation of effect of grain size is a cumbersome method which due to existence of many other effective parameters are not conclusive in some cases. On the other hand, since the average grain size of a crystalline material is a statistical parameter, using traditional methods are not sufficient for find the optimum grain size to maximize formability. Therefore, design of experiment (DoE) and artificial intelligence (AI) methods are coupled together in this study to find the optimum conditions for formability in terms of grain size and to predict forming limits of sheet metals under bi-stretch loading conditions. In this regard, a set of experiment is conducted to provide initial data for training and testing DoE and AI. Afterwards, the using response surface method (RSM) optimum grain size is calculated. Moreover, trained neural network is used to predict formability in the calculated optimum condition and the results compared to the experimental results. The findings of the present study show that DoE and AI could be a great aid in the design, determination and prediction of optimum grain size for maximizing sheet formability.
| Item Type: | Article |
|---|---|
| Funders: | Scientific Research Project of Qiqihar University (145209130) |
| Uncontrolled Keywords: | Artificial intelligence (AI); Design of experiment (DoE); Formability; Forming limits diagram (FLD); Grain size |
| Subjects: | Q Science > Q Science (General) T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Engineering > Department of Civil Engineering |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 03 Jul 2023 07:08 |
| Last Modified: | 03 Jul 2023 07:08 |
| URI: | http://eprints.um.edu.my/id/eprint/38749 |
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