材料热物理性能

Q=rlE;r
(2)
where q. E, and I are arc efficiency, arc voltage, and current, respectively. Heat loss was modeled using both convection and radiation heat transfer. The convective coefficient was allowed to vary with temperature.
0-7803-7697-8/03/$3 7.00 Q 2003 IEEE.
Fig. I-a W d d oftwo straight plates at a 90 deg.
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n
where s is the distance perpendicular to the weld line, z is the distance along the direction of the weld line, v is the speed rate of the electrode, F is the region in which 95% of the heat flus is deposited, and Q is given by:
1. INTRODUCTION
This paper reports further progress following a previous paper by the authors [I], in whch the proposed concept of using an artificial neural network (ANN) for the prediction of the thermo-elasto-plastic response of steel welds was demonstrated. The ANN was trained using finite element simulations in virtual Iahoratoly environment. As previously reported [I], the issues related to residual stresses and distortions resulting from welding have been a major research topic in the design and fabrication of large steel stmctures [2,3]. Several welding manuals provide guidelines on general welding practice [4,5]. Also, a number works have been devoted to the control of distortions and residual stresses in steel welds [6,7] and significant contributions have been made to minimize welding effects in the fabrication of large steel structures
In the finite element analysis, direct use of enthalpy and latent heat is made to reflect the phase change of the weld pool throughout the process. Then a non-linear elastoplastic response is calculated to produce the dynamic stress-deformation response, which in tnm leads to the "final state" of residual stresses and distortions. In the welding process simulation, thermal expansion of the parts being welded is included and the effects of these expansions on the welding response is assessed. In contrast to the previous paper by the authors [I], herein two plates with curvature are welded at a 90 dag. angle. Figs.1-a and I-b helow show the basic geometric configuration used in this study. The most significant "inputs" affecting the welding analysis are: the heat input (range from 600 W to 825 W), the moving speed of the heat source (range from 1.50 "/sec to 2.25 mm/sec) and the restraints. A secondary factor which also affects the result is the characteristic radius of the heat flux distribution (r 4 0 8 mm, radius where 95% of the heat flus is dqosited). Relevant material
Lane Department of Computer Science&Electrical Eng., West Virginia University, P.O. Box 6109, Morgantown, WV 26506-6109, USA
V.H. Mucino M. Awang
Department of Mechanical and Aerospace Eng. West Virginia University, P.O. Box 6106, Morgantown, WV 26506-6106, USA
Key Words: Steel Welding Process, Finite Element Simulation. Neural Network
Abstract- In large steel fabrication industries such as shipbuilding, and high-speed train guideways, the problem of residual stresses and overall distortion has been and continues to he a major issue. I n the last few decades, various research efforts have heen directed at the 'control of the welding process parameters aiming to reduce distortions and residual stresses. Yet. in actual practice, large amounts of resources are still required to rework welds. These costs increase production costs and delay work completion.
Use of ANN to Simulate the Effects of Welding Process Parameters in Curved Steel Plates: Residual Stresses, Strains and Distortions
R. L. Klein P. Klinkhachorn
Fig. 1-6 Weld ofhvo curvedplates a / a 90 deg properties are shown in Table I . The temperature properties of mild steel are taken from S. Brown and H. Song [SI. The Gaussian heat flus distribution on the surface of the weld, in the direction parallel to the electrode, is given by [31:
11. FINITE ELEMENT SIMULATION
In the work reported here the Finite Element Method ( F E W is used to simulate the welding process in two-steps; first a non-linear heat transfer step that yields the dynamic temperature distribution throughout the weld seam and the plates, and second, the elasto-plastic analysis, which yields the residual stresses, strains, and the displacements. The responses focused upon "ere those along the lon@tudinal cross sectiuns after the welded piece had cooled down to room temperature. An artificial neural network is trained using FEM simulation data for a wide variety of geomelric and prucess parameter combinations. Then, the resulting neural networks is shown to he capable of predicting the welding response without having t o carry out n cumputationally complex, time consuming full finite element analysis. This concept is shown x a highly effective and efficient way to predict welding to l responscs for welding process desi@ purposes.
A. Material Properties.
The temperature dependent properties of the plate and weld materials are given in Table 1 below. Table 2 shows the specific quantitative parameter values used in the finite element simulation for beat flus,speed rate and number of restraints. The effects of latent heat, convection and radiation boundary conditions have been included in the model to allow phase change within the material. The ANSYS FEM software [I21 was used in this the analysis. For the heat transfer analysis, the following three assumptions were made:
[8,9]. More recently, various research efforts have been made to control welding process parameters, aiming at reducing the distortions and residual stress effects [10,11].