From disruption narratives to visual scenario cards: A design-prior-guided AIGC framework for manufacturing resilience communication
DOI:
https://doi.org/10.4108/eetsis.15183Keywords:
manufacturing resilience, AIGC, information design, visual grammar, prompt compilation, human-AI collaborationAbstract
INTRODUCTION: In discrete and process manufacturing and their upstream supply networks, disruptions (natural hazards, cyber incidents, logistics blockages, geopolitical actions, facility failures, and demand shocks) reach decisionmakers as fragmented text narratives, slowing shared understanding across roles, while direct text-to-image generation leaves causal structure visually undifferentiated and design knowledge tacit. The research object is information-design knowledge (an art and design discipline) applied to manufacturing-disruption communication; it is not art manufacturing.
OBJECTIVES: To represent art and information-design knowledge as an explicit, inspectable v0.1 rule table and quantify the effect of a deterministic causal-organisation prompt manipulation under matched generation conditions.
METHODS: The dataset comprises 1,694 RSS-harvested manufacturing-disruption narratives, of which 758 passed deterministic lexical screening. Records were embedded with sentence-transformers/all-MiniLM-L6-v2, and a six-category query-derived taxonomy was audited with HDBSCAN and sensitivity analysis. Eighteen pre-generation design-prior records were stored in SQLite. A deterministic constructor emitted matched direct (D) and causal organisation (G) prompts whose verbatim text and hashes were recorded in a CSV manifest. Stable Diffusion XL produced 96 images for 24 scenarios under two conditions and two fixed seeds; outputs were assessed with CLIP image–text cosine probes, visual-complexity descriptors, and 192 label-blinded paired judgments from two independent raters using Wilson intervals, sign tests, and chance-corrected reliability statistics.
RESULTS: Clustering showed moderate taxonomy correspondence (NMI 0.578–0.613, ARI 0.456–0.592, noise 67.3–72.4%). The grammar condition won 97.4–100% of non-tied judgments across four dimensions (all sign tests p < .001; AC1 = 0.663) without improving CLIP alignment, while edge density and luminance entropy increased significantly after Holm correction; these are changes in structural complexity, not quality verdicts.
CONCLUSION: Design knowledge can operate as an auditable computational control layer whose benefit registers in expert-judged communication value rather than automatic similarity scores, offering a reusable template for knowledge-conditioned industrial AIGC. The evidence concerns resilience communication artefacts, not resilience performance or decision quality, and is bounded by one generation model, 24 scenarios, and two raters.
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