scholarly journals The CEL Method as an Alternative to the Current Modelling Approaches for Ti6Al4V Orthogonal Cutting Simulation

Procedia CIRP ◽  
2017 ◽  
Vol 58 ◽  
pp. 245-250 ◽  
Author(s):  
F. Ducobu ◽  
P.-J. Arrazola ◽  
E. Rivière-Lorphèvre ◽  
G. Ortiz de Zarate ◽  
A. Madariaga ◽  
...  
Materials ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4145
Author(s):  
Xiaohua Qian ◽  
Xiongying Duan

As a typical high specific strength and corrosion-resistant alloy, titanium alloy Ti6Al4V is widely used in the aviation, ocean, biomedical, sport, and other fields. The heat treatment method is often used to improve the material mechanical properties. To investigate the dynamic mechanical properties of titanium alloy Ti6Al4V after heat treatment, dynamic compressive experiments under high temperature and high strain rate were carried out using split Hopkinson press bar (SHPB) equipment. The stress–strain curves of Ti6Al4V alloy under different temperatures and strain rates were obtained through SHPB compressive tests. The Johnson–Cook (J–C) constitutive equation was used for expressing the stress–strain relationship of titanium alloy under large deformation. In addition, the material constants of the J–C model were fitted based on the experimental data. An orthogonal cutting simulation was performed to investigate the cutting of Ti6Al4V alloy under two different numerical calculation methods based on the established J–C model using the finite element method (FEM). The simulation results confirm that the adiabatic mode is more suitable to analyze the cutting of Ti6Al4V alloy.


2001 ◽  
Vol 40 (01) ◽  
pp. 18-24 ◽  
Author(s):  
S. S. Anand ◽  
P. W. Hamilton ◽  
J. G. Hughes ◽  
D. A. Bell

AbstractThe development of prognostic models for assisting medical practitioners with decision making is not a trivial task. Models need to possess a number of desirable characteristics and few, if any, current modelling approaches based on statistical or artificial intelligence can produce models that display all these characteristics. The inability of modelling techniques to provide truly useful models has led to interest in these models being purely academic in nature. This in turn has resulted in only a very small percentage of models that have been developed being deployed in practice. On the other hand, new modelling paradigms are being proposed continuously within the machine learning and statistical community and claims, often based on inadequate evaluation, being made on their superiority over traditional modelling methods. We believe that for new modelling approaches to deliver true net benefits over traditional techniques, an evaluation centric approach to their development is essential. In this paper we present such an evaluation centric approach to developing extensions to the basic k-nearest neighbour (k-NN) paradigm. We use standard statistical techniques to enhance the distance metric used and a framework based on evidence theory to obtain a prediction for the target example from the outcome of the retrieved exemplars. We refer to this new k-NN algorithm as Censored k-NN (Ck-NN). This reflects the enhancements made to k-NN that are aimed at providing a means for handling censored observations within k-NN.


Procedia CIRP ◽  
2017 ◽  
Vol 58 ◽  
pp. 578-583 ◽  
Author(s):  
L.A. Denguir ◽  
J.C. Outeiro ◽  
J. Rech ◽  
G. Fromentin ◽  
V. Vignal ◽  
...  

2016 ◽  
Vol 41 (39) ◽  
pp. 17713-17722 ◽  
Author(s):  
Giannina Giovannini ◽  
Andrés Donoso-Bravo ◽  
David Jeison ◽  
Rolando Chamy ◽  
Gonzalo Ruíz-Filippi ◽  
...  

2020 ◽  
Vol 47 ◽  
pp. 458-465 ◽  
Author(s):  
Nithyaraaj Kugalur-Palanisamy ◽  
Edouard Rivière-Lorphèvre ◽  
François Ducobu ◽  
Pedro-José Arrazola

Procedia CIRP ◽  
2013 ◽  
Vol 8 ◽  
pp. 152-157 ◽  
Author(s):  
Martin Madaj ◽  
Miroslav Píška

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