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In the present paper, a method of defining the industrial process parametersfor a new product using machine learning algorithms will be presented. Thestudy will describe how to go from the product characteristics till theprediction of the suitable machine parameters to produce a good quality of thisproduct, and this is based on an historical training dataset of similarproducts with their respective process parameters. In the first part of ourstudy, we will focus on the ultrasonic welding process definition, weldingparameters and on how it operate. While in second part, we present the designand implementation of the prediction models such multiple linear regression,support vector regression, and we compare them to an artificial neural networksalgorithm. In the following part, we present a new application of ConvolutionalNeural Networks (CNN) to the industrial process parameters prediction. Inaddition, we will propose the generalization approach of our CNN to anyprediction problem of industrial process parameters. Finally the results of thefour methods will be interpreted and discussed.

Prediction of Industrial Process Parameters using Artificial Intelligence Algorithms