The welding technology must fit to the purpose of each construction. The includes a careful understanding of demands, the adapted materials and their behaviour subjected to welding. The manufacturing is very essential too. The education of welding and control personnel will change. The integration of robotics, sensing and automation in the production makes the role of personnel and their responsibility very different. The paper tries to give the state of the art and an estimation for this and possibly the next decade(Fig. 1)

CO2 laser had enabled accurate and effective cutting. But its application has been limited to thin and small parts. Development of a high power oscillator and oscillator built-in cutting machine have realized cutting of thick and large steel parts. This machine brings also possibility of fully automated cutting systems to practical steel parts such as for construction machinery.

The resistance spot welding process has been extensively used for joining of sheet metals, which are subject to variation of many process variables. Many qualitive analyses of sampled process variables have been attempted to predict nugget size. In this paper, dynamic resistance and electrode movement signal which is a good indicative of the nugget size was examined by introducing an artificial neural network estimator. An artificial neural feedforward network with back-propagation of error was applied for the estimation of the nugget size. The prediction by the neural network is in good agreement with the actual nugget size for resistance spot welding of galvanized steel. The results are quite promising in that the quantitative estimation of the invisible nugget size can be achieved without conventional destructive testing of welds.

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