Concept-Based Force-Signature Analysis of Tool-Parameter Effects in Fine Blanking

Fine blanking part quality is highly sensitive to tool parameterization, yet cause–effect relationships are obscured by substantial process noise. This limits the utility of both simulation and scalar process metrics for tool optimization and mechanistic understanding. At the same time, high-resolution force-signature data is information-rich and can be used as evidence for learning physically plausible relationships, provided that interpretability is retained. This paper presents a data-to-knowledge pipeline combining finite element simulation, neural network prediction, and concept extraction to derive interpretable patterns from force time series and formulate mechanistic hypotheses linking tool parameters to die-roll formation. Controlled tool-parameter variation generates approximately 27,000 strokes with die-roll metrology. Neural networks predict die-roll height and tool parameters with mean absolute percentage errors of 0.6% and 2.6%, respectively. Concept extraction (ECLAD-ts) and segment-aware attribution localize predictive information to peak-load and post-peak regimes. For die-clearance variation, piecewise-linear analysis within concept windows identifies two mechanism-sensitive slope descriptors (Cliff's |δ| ≥ 0.83) with ordered progressions mirroring the graded die-roll response. The rising-flank slope captures clearance-dependent shearing resistance, while the post-peak decay slope reflects thermo-mechanical softening once crack growth initiates. Smaller clearance yields higher resistance, faster decay, and reduced die-roll height, consistent with forming theory. The pipeline enables phase-local assessment of tool-parameter effects under process noise and supports testable mechanistic hypotheses for simulation refinement.