1,720,985 research outputs found
Material Response to Continuously Varying Rate of Straining During Hot Forging Operations
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Real-time prediction of geometrical distortion of hot-rolled steel rings during cooling
No full textThe paper deals with the application of neural network modelling to the real-time prediction of the geometrical distortion of hot rolled steel rings during cooling from rolling to room temperature. The neural network model was designed and developed to be part of a new modular system for the in-line monitoring and real-time control of the geometrical quality of rings, even those with a complex profile, during hot and warm ring rolling operations. The data utilised to train the neural network were generated by numerical simulations of the cooling phase. In order to do these simulations, an FIE model capable of coupling thermal, mechanical and metalllurgical events was accurately calibrated. The proposed model was then applied to an industrial case that is described in the paper
Integrating Physical and Numerical Simulation Techniques to Design the Hot Forging Process of Stainless Steel Turbine Blades
No full texthe paper presents a joint application of finite-element-based numerical simulation and real-material-based physical simulation techniques for design and optimisation of the hot forging operations to manufacture high strength stainless steel turbine blades. 2D simulations of the forging steps carried out using a suitably calibrated finite element model are combined with systematic analysis of microstructure evolution during forging experiments, with particular care to formation of the brittle δ-ferrite phase at high temperatures. A correlation is established between microstructure and thermal and mechanical parameters characterising forging operations. On the bases of numerical and experimental results, the actual forging process is re-designed, reducing the total number of forging steps. Industrial trials, conducted with the optimised process parameters, demonstrate the effectiveness of the developed procedure
Application of neural networks to represent the rheological behaviour of nickel-based superalloys under varying hot deformation conditions
No full textIn this work, neural networks are employed to represent the rheological behaviour of nickelbased
superalloys under varying hot deformation conditions, that approximate thermo-mechanical cycles of
industrial hot forging operations. A feed-forward back-propagation neural network has been trained and then
tested on rheological data, obtained through hot compression experiments, where the strain rate has been
varied continuously during the deformation step. A good agreement between calculated and experimental data
has been obtained, proving the feasibility of this new approach
ANALYSIS OF VARYING STRAIN RATE CONDITIONS ON THE MATERIAL FLOW STRESS IN HOT FORGING OPERATIONS
No full textSensitivity of the flow stress to varying strain rate conditions in hot forging of Nimonic 80A
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