In the master thesis we develop and implement power controller of welding laser with maximum power of 400 W based on image sensor, where the process of welding is being continuously estimated with convolutional neural network. Based on learnt models with total accuracy of 94 % on test dataset we are able to estimate heat input in overlaying AISI 304 metal sheet with cumulative thickness of 1,5 mm. Controller developed from PID controller and convolutional neural network is responsively and stably controlling laser power output. Controller in certain cases stabilizes within 0,46 s. Based on additional tests we propose additional possible improvements to increase controller's performance.
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