The Engineering Guide
Generative Design

What is generative design? How does work? What are the most common applications of generative design in engineering? Which generative design software should you choose?

If you are looking to answer these questions and learn how you can apply a generative approach in your engineering product development process, this guide is for you.

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Download nTopology’s Engineering Guide to Generative Design. In this 20+ page document, we explain how you can use nTopology as a generative design platform to develop high-performance products.

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Part 1

The Basics

Begin Part 1 >

Part 2

Generative Techniques

Begin Part 2 >

Part 3


Begin Part 3 >

Part 4


Begin Part 4 >

Part 1

Generative Design 101

Generative design is an advanced engineering methodology that combines geometry generation, simulation, and design automation.

This section explains the basics of generative design, how it has evolved, and its benefits and limitations.

GenDes Guide - GenDes 101

What is Generative Design?

In engineering terms, generative design is a goal-oriented and simulation-driven design methodology that uses software and computational algorithms to generate high-performance geometry based on user-defined engineering requirements.

Simply put, every generative design model has three key components:

  • Geometry generation
  • Design analysis & evaluation
  • Automated iteration loops

The engineer first defines a set of design requirements, then selects (or creates) the generative design approach that best fits the application and goals, and finally selects and refines the result that fulfills the performance targets.

What is Generative Design?, Generative Design Components

The design requirements that you build into your generative design model can be either technical (such as a target weight, stiffness, or heat transfer coefficient) or non-technical (like cost constraints, manufacturability, or regulatory compliance).

The basic ideas behind generative design may not sound very different from the traditional iterative engineering design process.

However, generative design is the next frontier in mechanical engineering product development because it flips the paradigm of first creating and then evaluating the performance of a design candidate.

Generative design can dramatically shorten the entire product development process by augmenting the capabilities of design engineers with powerful design automation tools.

Learn more about the applications of generative design >

CAD History

A Short History of Generative Design

Generative Design has been the holy grail of CAD and CAE since their inception. The earliest mentions in the late ’70s focused on shipbuilding and architecture.

With the proliferation of CAD in the ’80s, the interest in generative design increased. The results were still limited by the computing power of the time.

In the ’90s and early ’00s, simulation-driven design, such as topology optimization, started to gain traction. The first structural optimization software hit the market.

In the ’10s, advancements in digital and additive manufacturing pushed companies to accelerate the development of commercial generative design solutions.

Today, generative design finds applications beyond structural optimization, enabled by the increased computational power and advanced engineering design software.

Generative Design Vs. Topology Optimization

Generative design and topology optimization are often (erroneously) used interchangeably. Both are valuable simulation-based engineering design terms, but they have distinctly different meanings.

  • Topology optimization is a simulation-driven structural optimization tool. Designers define the technical requirements, and the software removes material from the designated design space through iterative simulation steps.
  • Generative design is a broad design methodology that allows engineers and designers to build both technical and non-technical requirements into their models.

Generative design encompasses several design tools, including topology optimization. Other generative design tools include performance-driven lattice structures and field-driven design.

Learn more about generative design techniques >

Topology Optimization vs. Generative Design

Generative Design & Additive Manufacturing

Generative design enables the development of high-performance 3D printed products and is a near-necessity for any DfAM workflow.

One of the key benefits of industrial 3D printing is that it gives engineers the ability to manufacture highly complex and high-performance parts that are either impossible or prohibitively expensive to produce using traditional techniques.

However, modeling these complex and optimized geometries manually in traditional CAD software is a near-impossible task.  The digital toolset of generative design enables engineers to manage the complexity of additive manufacturing and use it to their advantage.

Learn more about generative design techniques >

CAD History

Benefits & Limitations of Generative Design

If used correctly, generative design can be a powerful tool that helps you accelerate every aspect of advanced product development.

Of course, the benefits and limitations of generative design greatly depend on how you apply this advanced engineering design methodology through software.

Learn more about generative design software >


Benefits of Generative Design

The digital capabilities of generative design can unlock a previously inaccessible design space. Using tools such as topology optimization, advanced lattice structures, and field-driven design, you can build lighter, higher-performing parts with increased functionality. Generative design has applications in every field of product development; from improving thermal management in electronic devices to developing more efficient rocket propulsion systems to reduce the cost of shooting payload into orbit.
Generative design accelerates all stages of product development, from concept design to manufacturing. Using its digital tools, engineers can quickly generate geometries with high complexity, from organic, freeflow parts to repetitive patterns with millions of elements. Since manufacturability can be taken into account early in the design process, the probability that time-consuming revisions are needed later on is much lower.
While designing new products, engineers tend to draw inspiration from their past projects and experiences. While this is exceptionally valuable, an algorithmic approach (such as a well-designed generative process) can produce unbiased results that may contradict preconceived notions. Combining these results with the engineer's experience leads to faster and more radical product innovation.

Limitations of Generative Design

Engineers frequently need to know just as much about the process as the resulting solution. Due to the complexity of generative algorithms, many software solutions operate using a “black box” approach. The engineer gives inputs and then is asked to evaluate the outputs without having visibility or control over the process that was followed in the backend. For mission-critical applications where design outputs must produce repeatable and reproducible results, this significantly hinders the adoption of specific generative design implementations.
When performing generative design, it is essential to remember that your solution is only as accurate as the simulations used to produce it. Many physical phenomena aren’t supported by most generative design software. This means that the result is optimized only for the limited set of design requirements that the software can handle. It is crucial to recognize that there are often many design requirements that may have not been taken into account during optimization.
Generative design still relies on the quality of information that an engineer can supply. Generative Design has two main input components: the design space and the loading conditions. To get optimized output, both the problem and the inputs need to be defined accuratly. It is the job of the engineer to define the input parameters and the goal. For this reason, you should think of generative design as a collaborative process between the engineer and the design software.

Applications of Generative Design


Aerospace companies are applying generative design to shape tomorrow’s greener, lighter and more efficient aircrafts, rockets, satellites and drones.

FDD Heat Exchanger

Example Applications

Heat Exchangers, Hydraulic and Pneumatic Systems, Landing Gear, Doors, Fuselage, Nacelles & Pylons


With objectives centered around weight reduction, safety, and style, the automotive industry is already using generative design to develop parts for both performance and aesthetics.

Lattice Car Seat

Example Applications

Uprights, Brake Caliper, Hydraulic Manifold, Seat Cushioning, Car Grilles, Customized Accessories

Medical Devices

With automated design analysis and geometry generation, biomedical engineers can design a wide variety of patient-specific medical devices with unrivaled speed and customization options.


Example Applications

Orthopedic Implants, Prostheses, Orthotics, Casts, Dental implants

Consumer Products

Generative design software gives engineers the ability to generate manufacturing-ready design candidates, saving valuable design time and giving you a differentiation advantage.


Example Applications

Sports Equipment, Luxury Products, Footwear, Protective Gear


Lightweighting and design automation, can enhance the efficiency any manufacturing process, from jigs & fixtures for large-scale assembly lines to customized 3D prints.

AM Build Plate

Example Applications

Jigs & Fixtures, Molds & Dyes, AM Build Preparation, Robotic End of Arm Tooling

Heavy Industry

Weight reduction of heavy machinery through generative design enables engineers to minimize cost, improve safety and reduce energy consumption during both assembly and operation.

Large Casting Mold

Example Applications

Trucks, industrial machinery, large metal casts, and forgings

Case Study: Generative Design in Robotics

The engineers of DMG MORI’s ADDITIVE INTELLIGENCE team redesigned their Robo2Go head for additive manufacturing using nTopology’s generative design capabilities. This key component connects the robotic arm to the robot end-effector, houses the electronic and pneumatic connections, and plays a crucial role in the handling precision of the system.

DMG MORI Robot End Effector

The team combined generative techniques like topology optimization and field-driven design to achieve this weight reduction.

The redesigned assembly weighs 0.7 kg (62% less than the original) and has 48 components (down from the original 79). Early tests showed that the new component’s higher stiffness to weight ratio helped improve the robotic system’s handling precision by a factor of 16x.

Read the full case study >

Part 2

Generative Design Techniques

Generative design offers a comprehensive set of digital design tools that you can use to tackle even the most challenging engineering problems.

In this section, you learn the basics of each of these techniques and how you can apply them to your product design workflows.

GenDes Guide - Techniques

Topology Optimization

Topology optimization is a well-established, simulation-driven structural optimization technique — primarily used for engineering concept design.

In topology optimization, the designer first defines a design space, the loading conditions, optimization target, and other non-technical requirements, such as manufacturing constraints. Then, the software removes material in areas where stresses are low through iterative simulations.

The result has a reduced weight and high stiffness that can still withstand the indicated loading conditions.

generative design technique: topology optimization

Topology optimization is a powerful engineering design tool. It can expedite the product design process, reduce part weight and manufacturing cost, and generate an array of initial design candidates to guide detailed design.

Moreover, you can combine topology optimization with other generative design techniques. Following a hybrid approach, you can develop, for example, hollow components with variable thickness that have increased impact resistance and still grasp some of the high stiffness-to-weight ratio benefits of pure topology optimization.

Keep in mind that the result of topology optimization is a solution proposal, not manufacture-ready parts. The raw outputs need to be smoothened and post-processed before they are ready for manufacturing or simulation. Modern topology optimization software platforms can automate this step, skipping a time-consuming manual task and expediting the process.

Learn more about topology optimization >

When to use Topology Optimization?

  • Early in the design process to generate concept design candidates
  • In structural optimization problems for parts with high stiffness-to-weight ratio
  • To reduce weight, material usage, and manufacturing costs

Performance-Driven Lattices

Fundamentally, lattice structures consist of unit cells that are repeated in space. These unit cells can be ordered beam or plate structures, like honeycombs, random foam-like patterns, such as Voronoi, or minimal surfaces, like gyroids.

Lattice structures are a potent design tool that you can use to reduce part weight, improve (additive) manufacturability, or achieve a tailored material response through precise design.

You can apply a generative design approach to optimize the properties of a lattice. Using an iterative simulation-driven process, you can create design processes to achieve specific performance targets (or other non-technical goals).

generative design technique: Performance-driven Lattices

Lattice structures are most commonly used for lightweighting to improve the strength and manufacturability of sandwich structures and shelled parts. With the rise of additive manufacturing processes and the need for sustainable material usage, lattice structures are the future of structural design.

In advanced product development, lattice structures are the basis of architected materials. By closely controlling the lattice parameters, you can create structures with behavior that exceeds the properties of the base material. A typical application of these meta-materials is the design of foam replacements.

Lattice structures pair well with the generative design technique we will examine next: field-driven design. Field-driven lattice design allows engineers to create structures with varying thicknesses and cell sizes to further optimize the lattice performance for each application.

When to use?

  • During optimization to reduce weight, material usage, and manufacturing time
  • In the R&D phase, to create materials with tailor properties
  • To create bio-spired, lightweight, yet strong parts with unique properties

Field-Driven Design

Field-driven design is a computational approach to generative design that augments the traditional iterative engineering process with powerful geometry generation capabilities.

Fields can represent any physical quantity: from simulation results and experimental data to 3D geometry — using implicit modeling. Field-driven design enables engineers to establish direct relationships between part geometry and fundamental physics principles.

For example, you can use structural FE simulation results to spatially vary the thickness of a shell or a lattice, increasing the value where stresses are higher. Or you can combine CFD and thermal FE simulations to generate compact heat exchangers with high efficiency and low pressure drop.

generative design technique: field-driven-design

The characteristics of Field-driven design make it uniquely suited to solve complex, multiphysics engineering design problems where other generative design methods may fall short.

In simpler terms, it allows simulation results and test data to be used as inputs to drive design features such as lattice or shell thickness. Field-driven design can also be combined with topology optimization to further refine the results and compensate for design objectives that were not considered by the optimization algorithm.

Notably, the output of field-driven design is not only an optimized part but also an engineering workflow. Once the workflow is verified, it can be pushed to production and used repeatedly to automatically generate new part designs.

When to use?

  • In multi-physics optimization to directly drive geometry from simulation
  • Post topology optimization to reinforce parts with variable shells and lattices
  • To incorporate field measurements and test data in generative workflows

Part 3

Applications of Generative Design

Generative design and its unique capabilities give you powerful tools to create solutions that are tailored to the specific needs of each application

In this section, you will learn how generative design is used to develop high-performance products — from car parts and satellite components to implants and prosthetics.

GenDes Guide - Applications


Weight reduction is one of the main applications of generative design. There are two main reasons for lightweighting:

Reduce weight to increase performance. This is especially relevant in aerospace or automotive application, for example, where every extra ounce increases fuel consumption. It is also relevant in medical applications, such as prosthetic devices, where a lighter extremity can increase patient comfort.

Reduce weight to lower manufacturing costs. Material consumption is an important factor when determining the production cost of both additive manufacturing (like industrial 3D printing) and formative processes (like injection molding and casting).

CAD History

Using generative design tools you can replace solid structures with lattices or remove them entirely to create lighter designs that consume less material.

The main generative design tools used for lightweighting are lattice structures and topology optimization. Field-Driven Design can also be used to drive the geometry of “traditional” design elements, such as shells, isogrid ribs, and perforation patterns.

Key Benefits

  • Improve product performance
  • Reduce manufacturing costs
  • Improve energy efficiency

Relevant Industries

  • Aerospace
  • Automotive
  • Medical devices

Architected Materials

One of the most unique applications of generative design is the development of architected materials. Architected materials are cellular structures with a topology that is optimized to achieve specific functions or properties.

Architected materials are complex lattice structures with a controllable and customizable material response.

Architected materials can improve a part’s structural, thermal, acoustic, and electromagnetic characteristics — and even spatially vary them — without altering the original design’s outer shape. This makes it a powerful tool with applications from prosthetics to consumer goods.

CAD History

The main benefit of architected materials is that they give you the flexibility to change the behavior of the product just by changing the digital design — not the material used for manufacturing. Additionally, using architected materials you can create material property combinations that cannot be found in nature.

The process of developing an architected material is iterative and requires close control of lattice geometry. Typically, the engineering team runs a design of experiments to determine the effect of each design parameter on material property response of interest. The generated structure can be evaluated either experimentally or through simulation.

The result is a library of digital materials that can be applied to products using Field-Driven Design techniques and customize the products to the user’s specific needs.

Key Benefits

  • Customized impact absorption
  • Bio-compatible medical devices
  • 3D printed protective foams

Relevant Industries

  • Medical Devices
  • Consumer Products
  • Automotive

Thermal Management

Heat exchangers, heat sinks, cold plates are devices that transfer heat through conduction or convection (and less often through radiation) from a solid or fluid to a cooling medium. These thermal management systems are essential to the function of engineering products because they improve performance, increase reliability, prolong service time and ensure safe operation.

The effectiveness of heat transfer is determined to a great extent by the surface area in contact with the cooling medium. In liquid-based cooling systems, the pressure drop is also an important performance indicator because it determines the size of the pump.

CAD History

Lattice structures are well suited for heat exchanger design. Lattices provide a large surface area at a compact size. TPMS structures, like the gyroid, are commonly used for liquid-based cooling because they can easily separate the flow into two different domains. Beam-based lattices are also very useful for air-based exchangers and heatsink designs.

These structures enable engineers to develop compact and multifunctional components with high strength, low weight, and excellent heat dissipation properties when coupled with additive manufacturing methods.

Generative design is often the only way to optimize these highly complex geometries. An iterative, simulation-driven workflow powered by automated geometry generation is needed to evaluate the performance of these advanced structures and help the engineer select the best geometry that fulfills all design requirements for a specific application.

Key Benefits

  • Maximize surface area
  • Minimize weight and volume
  • Minimize pressure drop

Relevant Industries

  • Automotive
  • Aerospace
  • Consumer Products

Customer-Specific Products

Generative design introduces the capability to take in large files or data inputs to create customized solutions. With generative design, this process can also be turned into a repeatable automated workflow, making customization of 3D geometry easier and faster than it has ever been before.

Generative design software can automatically transform any data into geometry, whether that is a mesh cloud from a 3D scan, a pressure map, or other information related to the anatomy of a patient or end-user or simulations results that can influence the design.

CAD History

This capability is more useful than ever in an era where personalization is critical in many industries:

  • Patient-specific medical devices such as orthopedics implants, orthoses or prostheses, are proven to lead to better patient outcomes and faster rehabilitation.
  • Personalized footwear and protective sports equipment lead to improved comfort, increased protection, and better athlete performance.
  • For high-end automotive or other luxury items, apart from improved ergonomics, personalization increases sentimental value.

Key Benefits

  • Automate design workflows
  • Rapidly customize designs
  • Reduce engineering time

Relevant Industries

  • Medical
  • Consumer Products
  • Automotive

Industrial Design

Generative Design is typically used to improve the technical characteristics, but it can also enhance a product’s aesthetics and ergonomics. Using computational and simulation-driven techniques, you can approach old design problems in a new way.

CAD History

Organic-looking products with unique aesthetics are desired by customers and can even enhance product function. These features can be implemented using various generative design tools such as 3D textures, perforated or embossed patterns, and conformal lattice structures.

Generative design can also be used to create and iterate through multiple prototype designs to gain inspiration about the form of an object. Starting with a block of material and design constraints can be an efficient way to get started on a project, no matter the size or importance.

Key Benefits

  • Generate organic textures
  • Rapidly iterate through designs
  • Enhance product ergonomics

Relevant Industries

  • Consumer Product
  • Automotive
  • Medical

Part 4

Generative Design Software

Now that you know the basic techniques and applications of generative design, it is time to choose software to put the theory into practice.

In this section, we give you an overview of the available options.

GenDes Guide - Software

Generative Design Software Categories

The basis of generative design involves three main components: geometry generation, design analysis, and automation. The ideal program effortlessly combines all three of these.

There are three main types of generative design software:

  • Simulation-driven design packages in CAD software
  • Geometry generation packages in CAE software
  • Optimization-focused engineering design platforms
+ Accesible through a single design environment + Proven design analysis tools with multiphysics simulation capabilities + Specialized generative tools for designing advanced geometry
+ Offer an easy-to-use interface + Design automation options through 3rd party software + Powerful design automation capabilities that augment existing CAD and CAE software
– Limited simulation capabilities for only a small scope of problems – Offer very basic geometry generation capabilities – Requires learning a new software
– Lacking geometry generation tools for complex modeling operations – Interface that is not suitable for non-expert users – Their advanced capabilities may not be necessary for simpler problems

Generative design solutions evolved from CAD generally offer an easy-to-use interface for beginners and intermediate users but lack specialized design analysis and geometry generation tools.

CAE-based solutions typically provide a robust platform for design analysis and simulation but offer limited tools for geometry generation and an interface that is not suitable for non-expert users.

Optimization software fills in the gap between CAD and CAE. Typically, they are built from the ground up onto new technologies that augment the capabilities of CAD and CAE with powerful geometry generation and automation tools.

How to Pick the Right Software For You

The needs of every engineering team and project are different, so apart from the type of software, there are also other considerations you should take into account before selecting a generative design solution.

Cloud vs. Local Computing

When first introduced, generative design capabilities were closely associated with cloud-based CAD software only. However, this technology is now also available with desktop offerings.

Cloud platforms offer access to elastic computing resources. Additional cores and storage space can be allocated as required to address the high computational demands of generative design. A significant drawback of cloud computing is that data protection can be an issue, especially when working on sensitive intellectual property.

An alternative to such cloud-based solutions can be found in high-performance computing on local networks. Some generative design platforms can also run on desktop computers as well, which is convenient for quick iterations and lean teams.

The choice between a cloud or local solution can be a decisive factor in the software decision-making process, especially when sensitive intellectual property is involved in your project.

Open Vs. Closed Workflows

The results of a generative design algorithm are ultimately evaluated by its users, the engineers who decide whether a solution meets all design requirements. Building confidence in the results is a critical step in adopting generative design, and the openness of the software used is a deciding factor in this.

Using a “closed” generative design solution is typically straightforward and fast. However, the users can only select from a limited set of options during the problem definition phase and have little control over the optimization process.

On the other hand, using an open solution can significantly improve the trustworthiness of the results. It allows users to specify additional design constraints to define the problem better or modify the logic of the optimization workflow to achieve more recognizable or understandable results.

Essentially, when the engineers can take control of the whole process, there is a much better chance that the output will be production-ready without further work and comply with all design requirements.

Implicit Modeling Vs. Traditional Modeling

There are limited modeling technologies that can handle the geometric complexity that is the result of a generative design process; with implicit modeling being the most promising.

The traditional modeling techniques that CAD and CAE software are built upon — boundary representations (B-REPs) and the meshes — are reaching their limits when it comes to handling complex lattice structures and organic shapes. Even when the modeling operation doesn’t break the software, the resulting file size is too large to handle.

In implicit modeling, every shape and modeling operation can be expressed as a simple and lightweight math equation. The robust nature of implicit modeling guarantees that operations like booleans, offsets, rounds, and drafts never fail, making it an ideal fit for generative design workflows.

However, there is only a handful of computer software that enables you to use implicit modeling in engineering design.

nTopology as a Generative Design Toolbox

nTopology is an engineering design platform that was built from the ground up to solve complex engineering problems.

nTopology pioneered the use of implicit modeling in engineering design and offers a slew of geometry generation and design automation tools to power your generative design workflows.

CAD History

Here are the three key drivers that set nTopology apart from other computer software solutions are:

  • Unbreakable Geometry: Generate parts at lightning speeds no matter how complex, bypassing the bottlenecks of traditional modeling technologies.
  • Field-Driven Design: Control your designs at every point in space using simulations, test data, and engineering formulas to generate optimum results.
  • Reusable Workflows: Create configurable, re-executable, and shareable processes that package engineering knowledge and automate design tasks.

In the engineering design process, nTopology falls between CAD and CAE and augments the capabilities of both.

The typical design workflow receives concept design data from CAD, applies advanced geometry generation modeling operations, and exports the results to CAE for analysis.

CAD History

Would you like to see how you can apply a generative design approach to your engineering design application?

Request a demo of nTopology to see the software in action.

Get a Demo

Download the Guide

Download nTopology’s Engineering Guide to Generative Design. In this 20+ page document, we explain how you can use nTopology as a generative design platform to develop high-performance products.

Download Guide

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Image demonstrating generative design