Abstract: A method controls sheet metal processing of a sheet metal component to be manufactured with a plurality of processing steps. The method includes: A) reading in machine tool data and data of the sheet metal component to be manufactured, which includes (in any order): a) reading in the processing steps of the sheet metal component to be manufactured; b) reading in property parameters of the sheet metal component to be manufactured; c) reading in at least one basic processing capability of the machine tools; and d) reading in capability parameters of the machine tools. The method also includes B) creating a machine tool matrix made from machine tools suitable for manufacturing the sheet metal component; C) determining an optimized processing sequence in a form of a sequence of machine tools from the machine tool matrix; and D) outputting the optimized processing sequence.

Abstract: A method for optimizing production of sheet-metal parts, the production comprising cutting out and singularizing the sheet-metal parts and bending the sheet-metal parts, wherein the method includes: (A) training a neural network, which is executed on a Monte Carlo tree search framework, by means of supervised learning and self-play with reinforcement learning; (B) recording constraints for the sheet-metal parts, the constraints comprising geometric data of the sheet-metal parts; (C) creating an optimized production schedule by way of the neural network; and (D) outputting the production schedule.

Abstract: A method for optimizing production of sheet-metal parts, the production comprising cutting out and singularizing the sheet-metal parts and bending the sheet-metal parts, wherein the method includes: (A) training a neural network, which is executed on a Monte Carlo tree search framework, by means of supervised learning and self-play with reinforcement learning; (B) recording constraints for the sheet-metal parts, the constraints comprising geometric data of the sheet-metal parts; (C) creating an optimized production schedule by way of the neural network; and (D) outputting the production schedule.

Abstract: The disclosure relates to digital models of a production apparatus. The digital models can generate simulations of production sequences of the production apparatus, and a controller can access the simulation to improve operations of the production apparatus. The digital model uses data of a locating system to create the simulation. The locating system monitors carriers for transporting components. The controller can compare parameters of the simulation results with corresponding parameters of earlier simulation results and/or actually obtained parameters of earlier production sequences, which can be stored in a model library. The disclosure further relates to corresponding production control methods.