We introduce Textile IR, a bidirectional intermediate representation that connects manufacturing-valid CAD, physics-based simulation, and lifecycle assessment for fashion design. Unlike existing siloed tools where pattern software guarantees sewable outputs but understands nothing about drape, and physics simulation predicts behaviour but cannot automatically fix patterns, Textile IR provides the semantic glue for integration through a seven-layer Verification Ladder -- from cheap syntactic checks (pattern closure, seam compatibility) to expensive physics validation (drape simulation, stress analysis). The architecture enables bidirectional feedback: simulation failures suggest pattern modifications; material substitutions update sustainability estimates in real time; uncertainty propagates across the pipeline with explicit confidence bounds. We formalise fashion engineering as constraint satisfaction over three domains and demonstrate how Textile IR's scene-graph representation enables AI systems to manipulate garments as structured programs rather than pixel arrays. The framework addresses the compound uncertainty problem: when measurement errors in material testing, simulation approximations, and LCA database gaps combine, sustainability claims become unreliable without explicit uncertainty tracking. We propose six research priorities and discuss deployment considerations for fashion SMEs where integrated workflows reduce specialised engineering requirements. Key contribution: a formal representation that makes engineering constraints perceptible, manipulable, and immediately consequential -- enabling designers to navigate sustainability, manufacturability, and aesthetic tradeoffs simultaneously rather than discovering conflicts after costly physical prototyping.

翻译：本文提出纺织中间表示（Textile IR），这是一种连接制造验证型CAD、基于物理的仿真与时尚设计生命周期评估的双向中间表示。与现有孤立工具（纸样软件能保证可缝合输出但完全不了解悬垂性，物理仿真能预测行为却无法自动修正纸样）不同，纺织中间表示通过七层验证阶梯——从低成本语法检查（纸样闭合性、接缝兼容性）到高成本物理验证（悬垂仿真、应力分析）——为集成提供语义粘合剂。该架构支持双向反馈：仿真失败可建议纸样修改；材料替换实时更新可持续性评估；不确定性在流程中传播时附带显式置信边界。我们将时尚工程形式化为三个领域的约束满足问题，并展示纺织中间表示的场景图表示如何使AI系统能够将服装作为结构化程序而非像素阵列进行操作。该框架解决了复合不确定性问题：当材料测试中的测量误差、仿真近似与LCA数据库缺口相互叠加时，若无显式不确定性追踪，可持续性声明将变得不可靠。我们提出六项研究重点，并探讨在时尚中小型企业中的部署考量——集成工作流可降低专业工程需求。核心贡献：一种使工程约束可感知、可操作且即时反馈的形式化表示，使设计师能同时权衡可持续性、可制造性与美学需求，而非在昂贵的实体打样后才发现冲突。