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RISS 인기검색어
- 검색키워드
- 저자 : Fabio Di Trapani (검색결과 2 건)
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Modeling of surface roughness in electro-discharge machining using artificial neural networks
Cavaleri, Liborio,Chatzarakis, George E.,Trapani, Fabio Di,Douvika, Maria G.,Roinos, Konstantinos,Vaxevanidis, Nikolaos M.,Asteris, Panagiotis G. Techno-Press 2017 Advances in materials research Vol.6 No.2
Electro-Discharge machining (EDM) is a thermal process comprising a complex metal removal mechanism. This method works by forming of a plasma channel between the tool and the workpiece electrodes leading to the melting and evaporation of the material to be removed. EDM is considered especially suitable for machining complex contours with high accuracy, as well as for materials that are not amenable to conventional removal methods. However, several phenomena can arise and adversely affect the surface integrity of EDMed workpieces. These have to be taken into account and studied in order to optimize the process. Recently, artificial neural networks (ANN) have emerged as a novel modeling technique that can provide reliable results and readily, be integrated into several technological areas. In this paper, we use an ANN, namely, the multi-layer perceptron and the back propagation network (BPNN) to predict the mean surface roughness of electro-discharge machined surfaces. The comparison of the derived results with experimental findings demonstrates the promising potential of using back propagation neural networks (BPNNs) for getting a reliable and robust approximation of the Surface Roughness of Electro-discharge Machined Components.
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FE modeling of Partially Steel-Jacketed (PSJ) RC columns using CDP model
Marco F. Ferrotto,Liborio Cavaleri,Fabio Di Trapani 사단법인 한국계산역학회 2018 Computers and Concrete, An International Journal Vol.22 No.2
This paper deepens the finite element modeling (FEM) method to reproduce the compressive behavior of partially steel-jacketed (PSJ) RC columns by means of the Concrete Damaged Plasticity (CDP) Model available in ABAQUS software. Although the efficiency of the CDP model is widely proven for reinforced concrete columns at low confining pressure, when the confinement level becomes high the standard plasticity parameters may not be suitable to obtain reliable results. This paper deals with these limitations and presents an analytically based strategy to fix the parameters of the Concrete Damaged Plasticity (CDP) model. Focusing on a realistic prediction of load-bearing capacity of PSJ RC columns subjected to monotonic compressive loads, a new strain hardening/softening function is developed for confined concrete coupled with the evaluation of the dilation angle including effects of confinement. Moreover, a simplified efficient modeling approach is proposed to take into account also the response of the steel angle in compression. The prediction accuracy from the current model is compared with that of existing experimental data obtained from a wide range of mechanical confinement ratio.
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