Generative Design Primer
  • Welcome
  • Introduction to Generative Design
    • Computational Design
    • Generative Design
      • What is Generative Design?
      • Why should I use Generative Design?
      • What goes into a Generative Design Process?
        • Anatomy of each stage
      • Examples of Generative Design
        • MaRs Innovation District of Toronto
        • Furniture Design
        • A Further Analogy
      • Anatomy of a Good Generative Design Process
    • Visual Programming
    • Dynamo
    • Generative Design for Revit and Dynamo
  • Deeper Dive to Generative Design
    • Algorithms
      • What are Algorithms?
      • Generators
      • Evaluators
      • Solvers
    • Optioneering
    • Optimization
      • What is Optimization?
      • Objective Function
      • Constraints
      • Data
      • Defining Goals
    • Genetic Algorithms
      • What is a Genetic Algorithm?
      • Initialization phase
      • Evaluation Phase
      • Selection Phase
      • Crossover Phase
      • Mutation Phase
    • Other Techniques
    • Genetic Algorithm Q&A
  • Hello Generative Design for Revit and Dynamo!
    • Installing Generative Design
    • Setting up a Graph for Generative Design
    • Running Generative Design
    • Visualizing Results in Generative Design
    • Refinery Toolkit
      • Installing the Refinery Toolkit from the Dynamo Package Manager
      • Using the Refinery Toolkit
    • Space Analysis for Dynamo
      • Installing the Space Analysis for Dynamo package from the Dynamo Package Manager
      • Using the Space Analysis Package
    • Using Revit alongside Generative Design
      • Using Data from Revit
      • Remember Node Inputs
      • How to Test Revit Data Capture
      • Detailed Example Workflow
      • Sharing Logic and Results
      • Current Limitations
      • Accessing Generative Design Directly From Revit
  • Sample Workflows
    • Getting Started Workflows
      • Highest Point of a Surface
      • Minimum Volume and Maximum Surface
    • Architectural Workflows
      • Building Mass Generator
      • Building Positioning based on Solar Analysis
      • Office Layout
      • Grid Object Placement in a Room
      • Entourage Placement Exploration
    • MEP Workflows
      • Distributing Spotlights in an Office Space
    • Structural Workflows
    • BIM Workflows
      • Placement of views on sheets
    • Community Examples
      • Guidelines
      • List Of Examples
  • Generative Design in Your Office
    • What Generative Design Can Be Used For?
    • What Generative Design Can’t Be Used For?
    • How to Convince Senior Stakeholders of Using Generative Design?
    • The Role of a Generative Designer
    • Hiring a Generative Designer
  • Next Steps
    • Machine Learning
      • What is Machine Learning?
      • Is Generative Design Machine Learning?
      • Can Machine Learning and Generative Design Work Together?
  • Appendix
    • Glossary
    • Reference Material
    • Need Professional Help?
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  1. Introduction to Generative Design
  2. Generative Design
  3. Examples of Generative Design

Furniture Design

PreviousMaRs Innovation District of TorontoNextA Further Analogy

Last updated 5 years ago

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Looking at a simpler example, let's consider the process of designing a typical, four-legged table.

Using a standard approach, you as the designer would manually define the length, width, height and material of the table. The resulting output is a single, physical object with a fixed, immutable form. Here, you have the option to test several distinct sets of dimensions and material combinations to end up with three or four prototypes (or however many iterations you wanted).

In a generative design approach, you would instead create an algorithm that specifies:

  • a range of permissible values for each dimension;

  • a series of available materials and their properties (such as cost/m²); and

  • a set of goals that measure how successful a table design is.

Generate

Then, you would use a computer to run the algorithm and generate a series of designs that fall within the ranges you previously specified.

Some designs will be short and wide, others will be tall and thin, but each will satisfy the user-defined constraints. This is key, as many designs can be generated very quickly, much more than any human could feasibly examine.

Let's imagine the computer looked at 20 different values for each of: length, width, height, table/leg material combinations. The resulting solution space would be 20*20*20*20 = 160,000 designs, which is way too many options for a person to reasonably evaluate.

Above: Matrix showing 36 generated table designs, varying width, length, and height.

Evaluate

The next step is to define how the generated designs are evaluated. This is your opportunity to clearly express your design goals.

Above: A range of table designs (sizes), colour-coded based on evaluator function result (cost).

Let's see how different design goals could be expressed in this evaluation stage:

Design goal

Analysis method

Ranking method

lowest cost per desk, with minimum 800mm x 600mm size

desk size: at least 800mm x 600mm in size = yes/no and desk cost: area * material cost/m² = currency $ value)

lowest cost first and only options that satisfy area requirements

most profitable (largest desk area with lowest material cost)

desk area = outputs m² and unit cost (area * material cost/m²) = currency $ value

largest area and lowest cost

The matrix above exemplifies how you can use this stage in the generative design process to design for wildly different scenarios.

In the first scenario, lowest overall cost is the driving goal, so we can expect small desk sizes and cheap materials while still satisfying the size requirement. This scenario would be relatively simple for humans to replicate, so generative design would only come in handy when the variation or complexity of material costs is high.

For the second scenario, we're aiming to maximize return on investment (ROI) for each desk. This means that we can expect larger, more expensive desks than the first scenario, but that still have the best overall ROI. It wouldn't be unexpected for this process to identify a desk with cheap legs and more expensive tabletop materials as a viable option.

This second scenario is a good illustration of using generative design to work towards multiple and competing goals, which is very hard for humans to replicate.

Above: Visualizing evaluator function results as a color range.

As you can see, both of these examples follow the same fairly generic process, which is why there are so many possible applications of generative design in areas as diverse as aviation, automotive and building design, manufacturing, and product design.