AI for CAD Tools
How aerospace teams use generative design to create lightweight certified parts. Real examples, certification challenges, and practical implementation guidance.
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10 min read
Michelle Ben-David
Michelle Ben-David is a mechanical engineer and Technion graduate. She served in an IDF elite technology and intelligence unit, where she developed multidisciplinary systems integrating mechanics, electronics, and advanced algorithms. Her engineering background spans robotics, medical devices, and automotive systems.
BOTTOM LINE
Generative design for aerospace delivers real weight savings, but the path from optimized geometry to certified flight hardware is demanding. The teams succeeding focus on secondary structures, constrain for manufacturable output, and plan certification from the start. The teams wasting money skip the most obvious step: checking whether a qualified part already exists.
Leo AI gives aerospace teams the ability to search their full PLM history with geometry-aware matching before any new design or optimization work begins. Reusing a qualified part eliminates entire certification campaigns. SOC-2 certified, GDPR compliant, and trained on over a million pages of engineering standards, with integrations across leading PLM platforms. Your IP stays protected and is never used to train any AI model.
In aerospace, every gram matters. A kilogram saved on a structural bracket, multiplied across hundreds of aircraft and thousands of flight hours, translates to measurable fuel savings over the life of a fleet. That fundamental reality is why aerospace has been the proving ground for generative design since the technology first became practical.
But aerospace also has the strictest certification requirements of any industry. A part that looks beautiful out of a topology optimizer means nothing if it cannot pass FAA or EASA certification. And that is where the generative design story gets complicated, because the path from optimized geometry to certified flight hardware is longer, more expensive, and more technically demanding than most case studies let on.
I have been tracking how aerospace engineering teams actually use generative design in production programs, not in showcase projects built for conference demos. The reality involves hard tradeoffs between weight savings and certifiability, and the teams succeeding are the ones who understand those tradeoffs from the start.
Why Aerospace and Generative Design Are a Natural Fit
The physics of flight makes weight the most valuable currency in aerospace design. Every structural component has a mass budget, and exceeding that budget cascades through the entire aircraft. Heavier brackets mean heavier support structures mean heavier fuel requirements mean smaller payload capacity.
Traditional aerospace design uses generous safety factors and conservative geometry because the cost of failure is catastrophic. Engineers add material everywhere the loads might be, creating parts that are structurally sound but heavier than they theoretically need to be. The safety factors are justified, but they also mean there is significant room for optimization if you can prove the lighter geometry is equally safe.
Generative design and topology optimization directly attack this problem. By defining the exact load cases, boundary conditions, material properties, and safety factors, the solver explores the design space and removes material from everywhere the structure does not need it. The result is organic, lattice-like geometry that carries the same loads with significantly less mass.
Airbus has publicly documented weight savings of 30 to 55 percent on optimized structural components. GE Aviation's LEAP engine fuel nozzle, which consolidated 20 parts into a single additively manufactured component, has become the textbook example. Boeing, Safran, and Lockheed Martin have all disclosed generative design programs at various stages of implementation.
These examples are real, but they represent the leading edge of what is possible with significant engineering investment. The experience of a typical aerospace supplier or Tier 2 manufacturer looks quite different.
IN PRACTICE
The search in Teamcenter has always been a weak point for us. If you don't know the exact part number or file name, you're basically not finding it. Leo changed that.
Verified User, Defense and Space Enterprise
The Certification Problem
Here is where the conference presentations stop and the real engineering begins. Aerospace certification authorities, whether FAA, EASA, or military equivalents, require that every structural component demonstrate compliance with detailed airworthiness requirements. The material properties must be characterized. The manufacturing process must be qualified. The part must be tested, often destructively, across a defined test matrix.
Generative design creates two specific certification challenges that traditional designs do not face.
First, the organic geometry itself is harder to analyze and test. A conventional machined bracket has well-understood stress concentrations at predictable locations. A topology-optimized bracket has stress distributions that follow the material pathways created by the optimizer, and those pathways can be counterintuitive. Inspectors and certification engineers need to verify that the FEA models accurately predict the real-world stress state, which means more testing, more correlation studies, and more documentation.
Second, most optimized aerospace components require additive manufacturing. Metal AM processes like laser powder bed fusion and electron beam melting are still being qualified across the industry. The material properties of AM parts can vary depending on build orientation, thermal history, and post-processing. Each new geometry potentially requires its own qualification campaign because the process-property relationship depends on the specific part shape.
Teams that underestimate certification cost end up with optimized parts that save weight in the FEA model but never make it onto an aircraft because the qualification program is too expensive or time-consuming to justify.
What Actually Works in Production
The aerospace teams getting generative design into production share some common approaches.
They start with secondary structure, not primary. Flight-critical primary structure (wing spars, fuselage frames, landing gear) has certification requirements that make generative design extremely expensive to implement. Secondary structures like brackets, mounts, fittings, and interior components have lower certification barriers while still offering significant weight savings at scale.
They constrain for manufacturable output from the beginning. Running a topology optimization without manufacturing constraints produces beautiful geometry that might be impossible to inspect, support during AM build, or post-machine to tolerance. Successful teams define build orientation, minimum feature size, support-free overhang angles, and machining access zones as optimization constraints before running the solver. The output is less theoretically optimal but actually producible.
They plan the certification program before the optimization. Knowing what tests will be required, what material allowables are available for the selected AM process, and what documentation the certification authority expects allows the engineering team to set up the generative design study with those endpoints in mind. Reverse-engineering a certification approach after the geometry is finalized is far more expensive.
They validate with heritage data where possible. The fastest path to certification is showing that your optimized component falls within the bounds of existing material property databases and test correlations. Teams that can demonstrate their AM process produces material properties consistent with published allowables reduce their qualification burden significantly.
The Overlooked Opportunity in Aerospace Design
While the industry focuses on generating new optimized geometry, there is an enormous opportunity being missed in aerospace engineering that does not require generative design at all: finding and reusing existing qualified parts and designs.
Aerospace programs generate massive libraries of qualified components over their lifespans. A commercial aircraft program running for 20 years accumulates tens of thousands of part designs, each with associated qualification data, test reports, stress analyses, and manufacturing specifications. When a new variant or derivative program begins, engineers frequently design new parts from scratch because finding the right existing part in the PLM system takes too long or returns too many irrelevant results.
I have seen teams spend weeks designing and qualifying a new bracket when a nearly identical bracket, already qualified and in production, existed three directory levels deep in Teamcenter under a naming convention from a project that ended five years ago. The PLM search returned 4,000 results for "bracket" and the engineer gave up on page two.
Leo AI addresses exactly this problem. It connects to PLM systems including PTC Windchill, Siemens Teamcenter, and other leading platforms, and searches across the full design history using geometry-aware matching and natural language. An engineer can describe what they need or upload a reference model, and Leo surfaces existing parts with similar geometry regardless of how they were named or categorized.
In aerospace, reusing a qualified part is not just faster. It eliminates an entire qualification campaign. A bracket that has flight qualification data from a previous program can often be used on a new program with a fraction of the certification effort required for a new design, optimized or not.
Building a Practical Generative Design Workflow for Aerospace
For teams looking to implement generative design in their aerospace programs, here is the workflow that produces results.
Start with a vault audit. Before running any optimization, search your existing qualified parts library. If a part with acceptable performance already exists, use it. Leo AI makes this search practical even across massive PLM databases.
When you do optimize, constrain aggressively for your manufacturing reality. If your supply chain is machining, constrain for machining. If you have qualified AM suppliers, constrain for their specific process parameters and build volumes. Unconstrained optimization produces geometry that lives only in FEA.
Plan your certification in parallel with design, not sequentially. Engage your certification team early so they can define the test matrix and documentation requirements while the design is still being developed. Late-stage certification surprises are the number one killer of generative design projects in aerospace.
Keep your engineering knowledge accessible. Design decisions, material selections, load case assumptions, and test correlations from optimized parts need to be documented and searchable for future programs. Leo AI's engineering Q&A with source citations and calculation transparency helps ensure that the knowledge generated during optimization is retained and accessible to the broader team.
FAQ
Search Your Qualified Parts First
Reuse a certified part instead of qualifying a new one.
Leo AI searches your PLM vault with geometry-aware matching to find existing qualified aerospace parts. Engineering Q&A with cited sources, traceable calculations. SOC-2 certified, IP fully protected.
Schedule a Demo →
#1 New AI Software Globally - G2 2026
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Search Your Qualified Parts First
Reuse a certified part instead of qualifying a new one.
Leo AI searches your PLM vault with geometry-aware matching to find existing qualified aerospace parts. Engineering Q&A with cited sources, traceable calculations. SOC-2 certified, IP fully protected.
Schedule a Demo →
#1 New AI Software Globally - G2 2026
Enterprise-grade security
Trusted by world-class engineering teams