Design Standard and Constraint Rule

Design standards live in the heads of senior engineers. When a junior engineer asks 'what wall thickness should I use?' the answer is 'ask Dave, he's been here 20 years.' Industry standards (ASME, ISO) are referenced by name but the actual documents sit on a shelf that nobody reads. Prohibited materials, minimum radii, and tolerance conventions are institutional folklore passed down verbally.

AI cannot validate designs against standards or constrain generative design because no standards exist in any documented, accessible form.

Some design standards are documented in a company design guide or engineering handbook. The document covers the most critical rules — standard materials, minimum wall thicknesses, preferred fastener sizes. But the guide was written five years ago and only partially reflects current practice. Industry standard references are listed but not explained. New engineers read the guide but experienced engineers rely on their own knowledge, which may contradict the guide.

AI could reference the design guide as a knowledge source but cannot enforce rules because the guide is a static document with no machine-readable structure. Rules may be outdated or contradicted by actual practice.

Design standards are organized by category in a structured document or database. Each rule has a specific condition and requirement — 'for cast aluminum parts, minimum wall thickness is 3mm.' Industry standards are referenced with specific clause numbers. Prohibited materials are listed with reasons. Engineers can look up rules by category. But the rules are text-based — checking compliance requires an engineer to read the rule and manually compare it against the design.

AI can assist with standards lookups and identify potentially relevant rules for a given design context. Cannot perform automated compliance checking because rules are text descriptions rather than machine-evaluable logic.

Design standards are in a structured system with defined parameters. Each rule specifies the applicable condition, the design parameter, and the acceptable range in a queryable format. Standards link to the industry standard clauses they derive from. Compliance status for each product can be queried — 'show me all design rules that Product X violates or has not been checked against.' Rules are current and maintained through a formal review process.

AI can perform automated design rule checking against the standards database. Can identify non-compliant features, suggest compliant alternatives, and generate compliance reports. Cannot yet embed rules directly into the design process because rules are queried after the fact rather than enforced during design.

Design standards are schema-driven formal constraints with executable logic. Each rule is a machine-evaluable constraint — 'IF material_type = cast_aluminum AND feature_type = wall THEN thickness >= 3mm.' Rules integrate with CAD systems through APIs — the design tool evaluates rules in real-time as the engineer designs. Manufacturing process capability constraints (minimum bend radius for a given material and press brake) are encoded alongside geometric rules. An AI agent can constrain generative design algorithms using the complete standards model.

AI can enforce design standards in real-time during the design process. Generative design operates within the complete constraint space defined by company standards, industry standards, and manufacturing capabilities. Autonomous design validation is comprehensive.

Design standards are a living constraint system that evolves continuously. Manufacturing capability measurements auto-update process constraints. Industry standard revisions auto-propagate through the constraint model. Field performance data refines design rules — if a standard wall thickness proves inadequate in a specific application, the constraint model tightens for that context automatically. The standards are never static because they continuously learn from manufacturing capability and field results.

Fully autonomous design constraint management. AI maintains, enforces, and evolves design standards in real-time from all data sources. The constraint system is a living intelligence that gets more precise with every manufactured part and every fielded product.

Ceiling of the CMC framework for this dimension.