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Project Dreamcatcher

What if a CAD system could generate thousands of design options that all meet your specified goals? It’s no longer what if: it’s Project Dreamcatcher, the next generation of CAD. Dreamcatcher is a generative design system that enables designers to craft a definition of their design problem through goals and constraints. This information is used to synthesize alternative design solutions that meet the objectives. Designers are  able to explore trade-offs between many alternative approaches and select design solutions for manufacture.

Groups

Industry Research, Computational Science Research

Overview

Recent advancements in artificial intelligence and the simulation of complex phenomena have enabled software to play an active, participatory role in the invention of form. Project Dreamcatcher is an experimental design platform with focused research probes into generative design systems.

The Dreamcatcher Workflow.

The Dreamcatcher system allows designers to input specific design objectives, including functional requirements, material type, manufacturing method, performance criteria, and cost restrictions. Loaded with design requirements, the system then searches a procedurally synthesized design space to evaluate a vast number of generated designs for satisfying the design requirements. The resulting design alternatives are then presented back to the user, along with the performance data of each solution, in the context of the entire design solution space. Designers are able to evaluate the generated solutions in real time, returning at any point to the problem definition to adjust goals and constraints to generate new results that fit the refined definition of success. Once the design space has been explored to satisfaction, the designer is able to output the design to fabrication tools or export the resulting geometry for use in other software tools.
 

Define: Tools for Defining Problems

Dreamcatcher's problem definition is a format for designers to describe design problems. Through pattern-based description, solutions become modular and accretive, thereby expanding the quality and number of alternatives that are searched in a Dreamcatcher design session. The Dreamcatcher design knowledge base, created through machine learning techniques,is a classified index of pre-existing objects that perform functions, or satisfy constraints, similar to those the user has defined in their problem definition.

Diversifying Input Modalities

Mimicking the variety of reference material in a typical design brief, in Dreamcatcher the designer explicitly and implicitly documents goals and constraints through a number of input modalities including natural language, image inference and CAD geometry. An individual or team may manipulate the problem definition through these multiple modes of input and verify or modify the inferred changes to the problem definition document. Focused efforts on modeling problem definitions and performing design synthesis on full system models rather than individual parts is an active area of investigation for the team.

Generate: Shape Synthesis

The Dreamcatcher team is developing several, purpose-built design synthesis methods that algorithmically generate designs of different types from a broad set of input criteria. Synthesis objectives include structural, thermal and fluid physical requirements. Dreamcatcher's design synthesis methods compete against each-other to solve problems most effectively through Dreamcatcher's high-performance computing servers. A focused research effort into incorporating manufacturing constraints for various methods of fabrication are incorporated into the design synthesis process itself, so that only manufacturable designs are returned to the design team. The Dreamcatcher system enables designers to truly leverage an emerging class of manufacturing tools that release designers from hundreds of years of predicating design decisions on tool based constraints. 
 

Advances in Cloud-Based Computing and Optimization

Through a purpose-built, scalable and parallelized cloud computing framework code-named Saturn, Dreamcatcher is able to generate and evaluate solution sets with complexity well beyond that of Generative Design Systems of the past. Saturn provides the high-performance computing infrastructure necessary to run the computationally intense optimization and analysis engines, including multi-physics simulations.
 

Explore: Design Space Visualization

After a number of solutions have been computationally generated from a problem definition, the Dreamcatcher design explorer presents to the user a set of possible solutions and their associated solution strategies. This user interface provides a sense of the shape of the valid design space and variable interactions. It also assists users in building a mental model of which alternatives are high performing relative to all others in the set. Once the solution has been adequately explored, the designer can modify the problem definition to iteratively generate more relevant solutions.

Traditional optimization workflows like that of the NASA ST-5 antenna are 'bottom-up' where a design space must be defined by the user and then searched by a genetic algorithm or similar optimization function. By contrast, Dreamcatcher uses a 'top-down' approach where higher level goals are specified. This is the major differentiator between design optimization tools and Dreamcatcher's exploratory design synthesis process. 

Arguments for the incorporation of AI into design often default to concerns around replacing the human designer. While many elements that are commonly modeled from scratch such as brackets, adapters and stiffeners may be created more effectively by a system such as Dreamcatcher, complex elements and aspects that are difficult to quantify will require new types of interaction to leverage human intuition and computational rigor in partnership. Dreamcatcher is pioneering new methods for interactive synthesis and optimization with industry leaders from the automotive, aerospace and manufacturing fields.

The Dreamcatcher team consists of the Computational Science and Design Research groups of Autodesk Research in the Office of the CTO with collaborators throughout the larger Autodesk Corporation, industrial partners and academic partners. The global team in San Francisco, Toronto, and London is made up of specialists from varied domains as mathematical optimization, geometry, machine learning, mechanical engineering, material science, structural mechanics, user experience research, software design and development.

To learn more about Autodesk's initiatives in generative design, please visit our generative design information page.

Publications
14 publications
DreamSketch: Early Stage 3D Design Explorations with Sketching and Generative Design

Rubaiat Habib, Tovi Grossman, Hyunmin Cheong, Ali Hashemi, George Fitzmaurice (2017)

DreamSketch: Early Stage 3D Design Explorations with Sketching and Generative Design
UIST 2017 Conference proceedings:
ACM Symposium on User Interface Software & Technology
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How smart materials will literally reshape the world around us

Massimiliano Moruzzi (2016)

How smart materials will literally reshape the world around us
TechCrunch
September 17, 2016

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Integrated Spatial-Structural Optimization in the Conceptual Design Stage of Project

Subhajit Das, Samaneh Zolfagharian, John Haymaker (2016)

Integrated Spatial-Structural Optimization in the Conceptual Design Stage of Project
eCAADe 2016 Conference proceedings:
eCAADe Education and Research in Computer Aided Architectural Design in Europe
pp. 117-126
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Details
Embedded sensors and feedback loops for iterative improvement in design synthesis for additive manufacturing

Nigel Morris, Michael Bergin, Francesco Iorio, Daniele Grandi (2016)

Embedded sensors and feedback loops for iterative improvement in design synthesis for additive manufacturing
IDETC/CIE 2016 Conference proceedings:
ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
9 pages.
Download PDF

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Automated transformation of design text ROM diagram into SysML models

Wei Wan, Hyunmin Cheong, Wei Li, Yong Zeng, Francesco Iorio (2016)

Automated transformation of design text ROM diagram into SysML models
Advanced Engineering Informatics
August 2016, 30
pp. 585-603
Details
Four-Bar Linkage Synthesis Using Non-Convex Optimization

Vincent Goulet, Wei Li, Hyunmin Cheong, Francesco Iorio, Claude-Guy Quimper (2016)

Four-Bar Linkage Synthesis Using Non-Convex Optimization
CP 2016 Conference proceedings:
The International Conference on Principles and Practice of Constraint Programming
17 pages
Details
Automated Extraction of System Structure Knowledge from Text

Hyunmin Cheong, Wei Li, Francesco Iorio (2016)

Automated Extraction of System Structure Knowledge from Text
IDETC/CIE 2016 Conference proceedings:
ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
10 pages
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Details
Convolutional Neural Networks for Steady Flow Approximation

Xiaoxiao Guo, Wei Li, Francesco Iorio (2016)

Convolutional Neural Networks for Steady Flow Approximation
KDD 2016 Conference proceedings:
ACM SIGKDD Conference on Knowledge Discovery and Data Mining
10 pages
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Automatic Extraction of Function Knowledge from Text

Hyunmin Cheong, Wei Li, Adrian Cheung, Andy Nogueira, Francesco Iorio (2015)

Automatic Extraction of Function Knowledge from Text
IDETC/CIE 2015 Conference proceedings:
ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
10 pages
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Details
A Novel Application of Gamification for Collecting High-Level Design Information

Hyunmin Cheong, Wei Li, Francesco Iorio (2015)

A Novel Application of Gamification for Collecting High-Level Design Information
IDETC/CIE 2015 Conference proceedings:
ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
10 pages
Download PDF

Details
BIM-based Parametric Building Energy Performance Multi Objective Optimization

Michael Bergin, Mohammad Rahmani Asl, Adam Menter, Wei Yan (2014)

BIM-based Parametric Building Energy Performance Multi Objective Optimization
eCAADe 2014 Conference proceedings:
eCAADe Education and Research in Computer Aided Architectural Design in Europe
9 pages
Download PDF

Details
Use of Controlled Natural Language to Input Problem Definition for Computer-Aided Design

Hyunmin Cheong, Wei Li, Li Shu, Erin Bradner, Francesco Iorio (2014)

Use of Controlled Natural Language to Input Problem Definition for Computer-Aided Design
ICIDM 2014 Conference proceedings:
International Conference on Innovative Design and Manufacturing
6 pages
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Parameters Tell the Design Story: Ideation and  Abstraction in Design Optimization

Erin Bradner, Francesco Iorio, Mark Davis (2014)

Parameters Tell the Design Story: Ideation and Abstraction in Design Optimization
SimAUD 2014 Conference proceedings:
Symposium on Simulation for Architecture and Urban Design
8 pages
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Details
Natural Language Problem Definition for Computer-Aided Mechanical Design

Hyunmin Cheong, Wei Li, Li Shu, Alex Tessier, Erin Bradner, Francesco Iorio (2014)

Natural Language Problem Definition for Computer-Aided Mechanical Design
Workshop ACM SIGCHI Workshop
April 26, 2014
4 pages
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Details
Project Members
  • Pavel Babikov
  • Kosala Bandara
  • Erin Bradner
  • Adrian Butscher
  • Chin-Yi Cheng
  • Hyunmin Cheong
  • Mark Davis
  • Mehran Ebrahimi
  • Marco Fiocco
  • Dianne Gault
  • Ali Hashemi
  • Clayton Hotson
  • Francesco Iorio
  • Gareth Jones
  • Massimiliano Meneghin
  • Daniel Mercier
  • Nigel Morris
  • Massimiliano (Max) Moruzzi
  • Andy Nogueira
  • Mehdi Nourbakhsh
  • Anthony Ruto
  • Hesam Salehipour
  • John Schmier
  • Hooman Shayani
  • Pavel Volnyakov
  • Jenmy Zhang
Alumni Members
  • Christophe Audouze
  • Michael Bergin
  • Srikanth Bethi
  • Jimmy Cao
  • Adrian Cheung
  • Amirsaman Farrokhpanah
  • Vincent Goulet
  • Niels Grafen
  • Xiaoxiao Guo
  • Mohit Hingorani
  • Haibin Huang
  • Ken Hung
  • Zach Jagoda
  • Daniel Jin
  • Matija Kecman
  • Menaka Kiriwattuduwa
  • Wei Li
  • Cory Mogk
  • Sharmila Phadnis
  • Shashwat Sharma
  • Kyle St. Leger-Barter
  • Michael Tao
  • Wei Wan
  • Huaijun Wu
  • Bill (Rui) Zhang
  • Danlan Zhou
Partnerships

Hong Kong University of Science and Technology

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Concordia University

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Université Laval

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University of Toronto

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