A parametric energy consumption model for manufacturing components based on machine learning techniques

Due to growing concerns about environmental sustainability and rising energy prices, energy efficiency-oriented design is becoming increasingly important in the industrial sector. The most optimal energy efficiency decisions are made early in the design process. During the initial design phase, the design team must evaluate energy implications in a timely and approximate manner. Consequently, a technique is needed that can quickly and with few parameters evaluate the energy effect of a product during the conceptual design phase. Machine Learning (ML) has established itself as a field of study in industrial research. Approximating the energy effect using high-level data is accelerated by incorporating machine learning into the conceptual design. This paper suggests a way to create a parametric model to evaluate the energy impact of parts made on lathes in the early design stages. The results of the predictive model could be used to determine the energy efficiency indexes of an industrial plant.