Neural Nets Modelling for Automotive Welding Process

R.A. Ramírez Mendoza, R. Morales-Menéndez, and F.J. Cantú-Ortiz (Mexico)


Artificial Neural Networks, Automotive Welding Process, Modelling, Industrial Applications,


In this paper the authors study the applicability of Ar tificial Neural Networks for the modelling of a widely used particular welding process in automotive industry: pulsed gas metal welding process (GMAW-P). Applying this arti ficial intelligence technology requires the introduction of input and output data to the network. To achieve this, an experiment was designed and performed. The main func tions of the proposed model are: to simulate the process for purposes of training operators; to improve welding process performance by identifying regions that are insensible to variations on input parameters; and finally to increase the flexibility of a robotic welding cell. The concrete bene fits obtained from the GMAW-P process model develop ment are: optimization of critical variables of the weld ing process, support for development of virtual process and prototypes, definition of a robust welding procedure, quick response to product change and support in welding train ing.

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