A Metamodel for the Management of Large Databases: Toward Industry 4.0 in Metal Forming

Metal forming machines can be servo-controlled and their numerical control can easily record and monitor several on-line process signals and loading curves, as a function of time, with a given sampling frequency. If these signals are collected and stored inside a designed database, any single forming process can generate a large amount of information that can be preciously used for process improvement and optimization. The database can be generated and stored locally at the machine, but it becomes more useful if it is transferred to a centralized big database. In both cases, if sampling frequencies are selected in order to follow the actual process dynamics, the amount of data stored can very easily and rapidly reach unpractical and unfeasible dimensions, in the order of magnitude of terabytes. A smart, designed and comprehensive strategy is therefore required for large data collecting and processing. Such a comprehensive strategy is difficult to be generalized for any forming process, but it must be specifically designed for each type of operation. In this paper, a framework for the management of large databases in the rotary draw tube bending process is proposed. A metamodeling technique for data compression is described and tested with actual process data, obtained during several rotary-draw bending of round stainless-steel tubes. Tube bending tests have been performed with CN controlled benders, where 12 axes can be actuated and 16 simultaneous output signals or loading curves have been recorded. The proposed method is able, for some of the selected signals, to reduce the amount of required data with a 75:1 ratio, with no significant loss of technological information.