As one of the oldest shaping manufacturing processes, forging and especially hot forging is characterized by extreme loads on the tool. The thermal load in particular is able to cause constant changes in the hardness of the surface layer, which in turn has a decisive influence on the numerical estimation of wear. Thus, also during numerical wear, modeling hardness changes need to be taken into account. Within the scope of this paper, a new implementation of a numerical wear model is presented, which, in addition to dynamic hardness models for the base material, can also take into account the properties of a nitride wear protection layer as a function of the wear depth. After a functional representation, the new model is applied to the wear calculation of a multi-stage industrial hot forging process. The applicability of the new implementation is validated by the evaluation of the occurring hardness, wear depths and the locally associated removal of the wear protection layer. Consecutively, a tool life calculation module based on the calculated wear depth is implemented and demonstrated. In general, a good agreement of the results is achieved, making the model suitable for detailed 2D as well as large 3D Finite Element calculations.

In forging, parallelism deviations between upper and lower dies lead to asymmetric flash formation and affect forming forces, die filling, and part integrity. The flash gap influences local flow resistance and is closely linked to flow behavior and dimensional precision. Conventional diagnostics often assess such deviations under no-load or quasi-static conditions and therefore may not capture the effective closing state at bottom dead centre (BDC) under process load. While modern approaches such as high-resolution optical tracking of ram deflection can provide valuable insight, they require dedicated and sensitive instrumentation and are often limited in scalability. In contrast, workpiece-based signatures inherently reflect process effects such as elastic deflections, guide clearances, frictional conditions, and thermal influences.This study investigates whether workpiece-related geometric features can serve as diagnostic signatures for detecting and quantifying closing-gap inclinations under load. The focus is on the locally resolved flash thickness, which reflects the effective closing gap at BDC. Because this gap results from both geometric alignment and load-dependent deformation, the evaluation targets the final load-bearing state. Comparative forging trials are performed on a press equipped with active parallelism control, where controlled misalignments are introduced. The resulting flash geometry is measured by laser triangulation to determine the resolution limit and to identify the deviation magnitude at which reproducible signatures can be detected under process-relevant conditions. In the investigated setup, flash-thickness asymmetry shows an increasing trend from closing-gap inclinations of ~0.25°, providing a markedly higher diagnostic sensitivity than the maximum forming force. Designed as a non-invasive and retrofit-capable method, the approach supports inline monitoring in high-volume forging. It further enables scalable, data-driven correlation of machine, process, and product data for condition-aware process optimization.

Die Warmmassivumformung stellt durch hohe Energieeinträge und kurze Taktzeiten besondere Anforderungen an die Prozessüberwachung. Ein multisensorisches Verfahren wird vorgestellt, das Körper- und Luftschallsignale synchron erfasst, in Frequenzbänder zerlegt und deren Bandenergien statistisch mit Prozessgrößen verknüpft. Eigenfrequenzverschiebungen und charakteristische Energiemuster ermöglichen die Klassifikation von Prozessabweichungen und liefern die Basis für Big-Data-gestützte Überwachungskonzepte.