An Experimental Modelling and Numerical FE Analysis of Steel-Strip Ironing Process
This paper deals with analysis of steel strip ironing using experimental modelling and numerical FE simulation. Since both approaches are complementary to each other, numerous results were obtained that enable complete analysis of the processes with in deformation zone. Device for modelling of strip ironing has been developed for experimental testing. Die cone angle, holding force, contact friction conditions on the die and the punch side were varied, due to easily replace able working elements of the device. Friction coefficient values used in numerical FE simulations were determined based on measured values of forces on die and punch, as well as forming load on machine. Experimentally obtained results were confirmed by FE analysis that provided additional information about physics of the process itself within deformation zone. Presented research results point to coherent influences of process factors on tensile wall stress values, which represents key indicator for efficiency of forming, resulting in recommendations for efficient process management.
Prediction of technological parameters of sheet metal bending in two stages using feed-forward neural network
This paper describes sheet metal bending in two stages as well as predicting and testing of the final bend angle by means of a feed-forward neural network. The primary objective was to research the technological parameters of bending sheet metal in two stages and to develop an intelligent method that would enable the predicting of those technological parameters. The process of bending sheet metal in two stages is presented by demonstrating the various technological parameters and the test tool used to carry out tests and measurements. The results of the tests and measurements were of decisive guidance in the evaluation of individual technological parameters. Developed method for prediction of the final bend angle is based on a feed-forward neural network that receives signals at the input level. These signals then travel through the hidden level to the output level, where the responses to input signals are received. The input to the neural network is composed of data that affect the selection of the final bend angle. Only five different inputs are used for the total neural network. By choosing the desired final bend angle by means of the trained neural network, bending sheet metal in two stages is optimised and made more efficient.
Constitutive Equations for Elastic-Viscoplastic Strain-Hardening Materials
Abstract : A set of constitutive equations has been formulated to represent elastic-viscoplastic strain hardening material behavior for large deformations and arbitrary loading histories. An essential feature of the formulation is that the total deformation rate is considered to be separable into elastic and inelastic components which are functions of state variables at all stages of loading and unloading. The theory, therefore, is independent of a yield criterion or loading and unloading conditions. The deformation rate components are determinable from the current state which permits an incremental formulation of problems. Strain hardening is considered in the equations by introducing plastic work as the representative state variable. The problem of uniaxial straining has been examined for a number of histories that included straining at various rates, rapid changes of strain rate, unloading and reloading, and stress relaxation. The calculations were based on material constants chosen to represent commercially pure titanium. The results are in good agreement with corresponding experiments on titanium specimens. (Author)