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Workpiece-Based Signatures for Machine Diagnostics in Die Forging with Flash
Abstract:
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.
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37-46
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April 2026
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