In this paper, we consider online calibration of a Digital Twin and its use for control and optimization in the assembly process of sheet metal parts. This calibration is done based on a feedback signal received by calculating the quality of the simulated assemblies as compared to the prediction made by the Digital Twin. We develop a Kalman filter-based approach for online calibration of the Digital Twin, which in turn is used by a one-step look-ahead optimizer to define an online control scheme. This control scheme balances directly predicted quality gains against reduced uncertainty whose purpose is to enable long-term quality gains. The usage of a calibrated model in a one-step look-ahead optimizer as a controller allows to incorporate the benefits of the usage of Digital Twins for individualized control, where the control parameters of a production cell are optimized in a Digital Twin based on measured properties of the production inputs, over nominal control, where control parameters are set with respect to some reference production inputs, in an approach which is able to use measured final production quality for feedback control. The proposed approach is evaluated by computer simulations of two industrial product assembly use cases. In the first case, it demonstrates significant gains in quality of the produced assemblies, while in the second case it shows negligible to small improvements. The second case is, however, rather insensitive to miscalibration, which enables only small gains.