MODELLING AND CONTROL OF ERSW PROCESSES BY NEURONAL NETWORK

A study on the use of artificial intelligence (AI) techniques for the modelling and subsequent control of an electric resistance spot welding process (ERSW) is presented. The ERSW process is characterized by the coupling of thermal, electrical, mechanical, and metallurgical phenomena. For this reason, early attempts to model it using computational methods established as the methods of finite differences, finite element, and finite volumes, ask for simplifications that lead the model obtained far from reality or very costly in terms of computational costs, to be used in a real-time control system. In this sense, the authors have developed an ERSW controller that uses fuzzy logic to adjust the energy transferred to the weld nugget. The proposed control strategies differ in the speed with which it reaches convergence. Moreover, their application for a quality control of spot weld through artificial neural networks (ANN) is discussed.

Mechanical strength evaluation for Nd-YAG laser and electric resistance spot weld (ERSW) joint under multiaxial loading

This work presents a comparison between laser weld (LBW) and electric resistance spot weld (ERSW) processes used for assemblies of components in a body-in-white (BIW) at a world class automotive industry. It is carried out by evaluating the mechanical strength modeled both by experimental and numerical methods. An ""Arcan"" multiaxial test was designed and manufactured in order to enable 0 degrees, 45 degrees and 90 degrees directional loadings. The welded specimens were uncoated low carbon steel sheets (S-y = 170 MPa) used currently at the automotive industry, with two different thicknesses: 0.80 and 1.20 mm. A numerical analysis was carried out using the finite element method (FEM) through LS-DYNA code. (c) 2007 Elsevier B.V. All rights reserved.