Machine learning-based design support system for the prediction of heterogeneous machine parameters in industry 4.0

In the engineering practice, it frequently occurs that designers, final or intermediate users have to roughly estimate some basic performance or specification data on the basis of input data available at the moment, which can be time-consuming. There is the need for a tool that will fill the missing gap in the optimization problems in engineering design processes, by making use of the advances in the artificial intelligence field. This paper aims to fill this gap by introducing an innovative Design Support System (DesSS), originated from the Decision Support System, for the prediction and estimation of machine specification data such as machine geometry and machine design on the basis of heterogeneous input parameters. As the main core of the developed DesSS, we introduced different machine learning (ML) approaches based on Decision/Regression Tree, k-Nearest Neighbors, and Neighborhood Component Features Selection. Experimental results obtained on a real use case and using two different real datasets demonstrated the reliability and the effectiveness of the proposed approach. The innovative machine learning-based DesSS meant for supporting the designing choice, can bring various benefits such as the easier decision-making, conservation of the company's knowledge, savings in man-hours, higher computational speed and accuracy.