Automatic generation of digital-twins in advanced manufacturing: a feasibility study

A promising approach to improve the efficiency in manufacturing processes is to build digital twins of the main assets from the available data to estimate and predict important metrics such as the energy consumption of each machine. Such predictions can then be exploited in process optimization. However, generating the digital twins from data is challenging in Industry 4.0: the high dimensionality of the data and the complexity of the manufacturing process makes it difficult and time-consuming for a human to model all important assets in the line. To address this problem, this paper investigates the feasibility of a framework for automatic generation of digital twins from historical data, which incorporates automatic selection of variables and data-driven system identification. For that, different versions of an algorithm for variable selection were exhaustively combined with several data-driven system identification methods. The framework was applied in the generation of digital twins for an electrical motor in a state-of-the-art production line of wooden fibreboards. In the estimation and prediction of the motor power, several of the automatically-generated digital twins presented acceptable accuracy, performing similarly to a simple manually designed physics-based model. The results suggest that the automatic framework is feasible, deserving therefore further effort to enhance it and provide a more extensive validation.