CAD Model and Design File
The digital product definition maintained in CAD systems — 3D models, 2D drawings, assemblies, geometric dimensions and tolerances (GD&T), revision history, and the parametric relationships that define how design features interact and constrain each other.
Why This Object Matters for AI
AI cannot perform generative design, automate DFM analysis, or mine design knowledge without structured access to CAD model data; without it, product geometry lives in proprietary file formats that only human engineers can interpret and modify.
Product Engineering & Development Capacity Profile
Typical CMC levels for product engineering & development in Manufacturing organizations.
CMC Dimension Scenarios
What each CMC level looks like specifically for CAD Model and Design File. Baseline level is highlighted.
Product geometry lives entirely in the lead engineer's head and on whiteboard sketches. When someone asks 'what are the dimensions of that bracket?' the answer is 'let me draw it for you' or 'check with Dave, he designed it.' New engineers reverse-engineer parts from physical samples because no design documentation exists.
AI cannot perform any design analysis, DFM checks, or generative design because no digital product definition exists in any form.
Create CAD models for active products in any CAD system — even basic 2D drawings in AutoCAD establish a starting point for digital design documentation.
CAD files exist but are scattered across individual engineers' laptops and personal network folders. File naming is inconsistent — 'bracket_v3_FINAL_FINAL.sldprt' sits next to 'bracket_rev_C.step.' Some models are in SolidWorks, others in Inventor, and legacy products are only available as 2D DXF files. When you need a model, you email the engineer who last worked on it.
AI could potentially index file names and locations, but cannot reliably extract geometry or perform cross-model analysis because file formats vary, versions conflict, and there is no way to know which file is current.
Consolidate all CAD files into a shared file server with a naming convention and folder structure organized by product or part number.
CAD models live in a structured shared drive or basic PDM vault organized by part number. Each part has a designated folder. Engineers check files in and out, and revision letters are tracked in the file name or a spreadsheet. GD&T is on the 2D drawing but not embedded in the 3D model as PMI. Finding the current revision of a part is straightforward, but understanding design intent requires opening the model and reading notes.
AI can search for parts by number and retrieve the correct revision. Basic geometry extraction is possible for standard formats. Cannot perform automated DFM analysis because tolerances, material assignments, and design rules are not machine-readable in the model metadata.
Implement a PLM system that manages CAD revisions with formal release states, embeds PMI (Product Manufacturing Information) in 3D models, and links models to their associated BOMs and change orders.
CAD models are managed in a PLM system with formal revision control and release states. Each model has defined metadata — material, mass, surface finish, tolerance stack-ups. 3D PMI replaces or supplements 2D drawings for key dimensions. Engineers can query 'show me all parts using aluminum 6061 with wall thickness under 2mm' and get a reliable answer. Design history is traceable.
AI can perform automated DFM analysis, material optimization, and tolerance stack-up calculations using embedded model metadata. Cross-product design reuse analysis is possible. Cannot yet do real-time generative design because parametric relationships are not exposed via API.
Expose parametric model definitions and design constraints through API-accessible schema so AI agents can read and modify design parameters programmatically.
CAD models are schema-driven digital twins with full parametric definitions accessible via API. Every dimension, constraint, material property, and design relationship is machine-readable. An AI agent can query 'what happens to the stress distribution if I increase wall thickness by 0.5mm on Part X?' and get a computed answer. Design knowledge is encoded in the model itself, not just in engineer interpretation.
AI can perform full generative design within defined constraints, automatically optimize geometry for manufacturing process parameters, and validate designs against all encoded standards. Autonomous design iteration is possible for routine modifications.
Implement real-time model streaming where design changes publish as events and simulation results update continuously as parameters change.
CAD models are living digital twins that continuously evolve. Design changes generate real-time simulation updates. Manufacturing feedback loops directly modify model parameters. The model documents itself — every design decision, constraint rationale, and performance prediction is captured automatically as part of the design stream. There is no separation between 'the model' and 'the design knowledge.'
Fully autonomous design-to-manufacture capability. AI agents create, validate, optimize, and release designs with minimal human intervention. The digital twin is a continuous stream of design intelligence, not a static file.
Ceiling of the CMC framework for this dimension.
Other Objects in Product Engineering & Development
Related business objects in the same function area.
Engineering Bill of Materials (EBOM)
EntityThe engineering-owned product structure defining components, sub-assemblies, and materials from a design perspective — including part numbers, revision levels, material specifications, make-versus-buy designations, and the effectivity dates that track which configuration is current.
Design Requirement Specification
EntityThe structured set of functional, performance, regulatory, and customer requirements that the product design must satisfy — including requirement IDs, acceptance criteria, priority, verification method, traceability links to test cases, and compliance status maintained through the development lifecycle.
Engineering Change Order
EntityThe formal record documenting a proposed or approved change to a product design — containing the change description, affected parts, reason for change, impact assessment (cost, schedule, tooling, inventory), approval signatures, and implementation status across engineering, manufacturing, and supply chain.
Test and Validation Record
EntityThe structured record of product testing activities and results — containing test plans, test procedures, pass/fail outcomes, measurement data, environmental conditions, traceability to requirements, and the engineering judgment on whether results support design release.
Material Specification
EntityThe engineering-approved definition of materials used in the product — containing material grades, mechanical properties, chemical composition limits, environmental compliance status (RoHS, REACH), approved suppliers, and the test data supporting material qualification for each application.
Field Performance Feedback Record
EntityThe structured collection of product performance data from the field — warranty claims, failure analysis reports, customer usage patterns, reliability metrics (MTBF, failure rates), and environmental exposure data fed back to engineering to inform design improvements and validate reliability models.
Design Release Decision
DecisionThe stage-gate judgment point where engineering leadership evaluates whether a design is ready to release to manufacturing — assessing requirements coverage, test completion status, DFM compliance, risk items, and the evidence package required to authorize the transition from development to production.
Engineering Change Approval Decision
DecisionThe recurring judgment point where a change review board evaluates whether to approve, defer, or reject an engineering change — weighing technical merit, cost impact, schedule impact, inventory disposition, customer notification requirements, and regulatory re-certification needs against the benefit of the change.
Design Standard and Constraint Rule
RuleThe codified engineering standards, design rules, and constraints that product designs must satisfy — including company design standards, industry standards (ASME, ISO), regulatory requirements, manufacturability constraints, and the prohibited-materials lists that bound the design space.
Engineering Change Process
ProcessThe end-to-end workflow governing how product changes are proposed, evaluated, approved, and implemented — defining change request submission, impact analysis steps, review board composition, approval routing, implementation coordination across engineering-manufacturing-supply chain, and effectivity cutover procedures.
What Can Your Organization Deploy?
Enter your context profile or request an assessment to see which capabilities your infrastructure supports.