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Adaptive Make: Adaptive Make:

Adaptive Make: - PowerPoint Presentation

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Adaptive Make: - PPT Presentation

DARPA Manufacturing Portfolio Overview Paul Eremenko Briefing prepared for the MITOSTP Science of Digital Fabrication Workshop March 7 2013 The views expressed are those of the author and do not reflect the official policy or position of the Department of Defense or the US Governmen ID: 567223

manufacturing design process models design manufacturing models process tools model space open foundry complexity synthesis component adaptive structural assembly

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Slide1

Adaptive Make: DARPA Manufacturing Portfolio Overview

Paul Eremenko

Briefing prepared for the MIT/OSTP Science of Digital Fabrication Workshop

March 7, 2013

The views expressed are those of the author and do not reflect the official policy or position of the Department of Defense or the U.S. Government.

1Slide2

Adaptive Make for Cyber-Physical Systems (Vehicles)

2Slide3

A worrisome trend

3Slide4

Existence proof

Daily engineer output

(Trans/day)

Develop-

ment

time (

mo

)

IP block performance

I

nter IP communication

performance models

increasing abstraction

Cluster

Abstract

Cluster

Abstract

RTL

RTL

clusters

Abstract

Cluster

SW

models

IP blocks

Transistor model

C

apacity load

Gate level model

C

apacity load

System-on-chip Design Framework

W

ire load

4

Transistors per chip

Speed (Hz)

Feature Size (µm)

Sources:

Singh R.,

Trends in VLSI Design

: Methodologies

and CAD Tools

,

CEERI,

Intel

,

The Evolution of a Revolution

, and

Sangiovanni-Vinventelli

, A.,

Managing Complexity in IC Design

, 2009 Slide5

Design tools (META)

Models are fully

composable

Simulation trace sampling to verifycorrectness probability

Application of probabilistic modelchecking under investigation10^2  10

designs

Component Models

Modelica

State Flow

Bond

Graphs

AADL

Geometry

Semantic

Integration

Static constraint application

Manufacturability constraints

Structural complexity metrics

Info entropy complexity metrics

Identify Pareto-dominant designs

10

^10

 10

^4 designsStatic Trade Space ExplorationQualitative Reasoning

Qualitative abstraction of dynamicsComputationally inexpensiveQuickly eliminate undesirable designsState space reachability analysis

10^4  10^3 designs

Relational Abstraction

Linear Differential Equation Models

Relational abstraction of dynamics

Discretization of continuous state space

Enables formal model checking

State-space reachability analysis

10

^3  10^2 designs

Generate composed CAD geometry for iFABGenerate structured &unstructured gridsProvide constraints and input data to PDE solversCouple to existing FEA, CFD,

EMI, & blast codes10  1 design

CAD & Partial Differential Equation Models

Embedded Software SynthesisAuto code generation

Generation of hardware-specific timing modelsMonte Carlo simulationsampling to co-verify

Hybrid model checkingunder investigation

Physical

Software

Computing

A

B

5Slide6

Foundry-style manufacturing tools (iFAB)

Manufacturing Process Model Library

Constraints

from Selected Configuration

META Design

Static Process Mapping

Sequencing

Foundry Trade Space

Exploration

Kinematic Machine Mapping

Topological Decomposition

Kinematic Assembly Mapping

Scheduling

CNC Instructions

Human Instructions

*Manufacturing Constraint

Feedback to META Design

Rock Island Arsenal

Bldg

299 Final Assembly

*

*

6Slide7

Foundry-style manufacturing processes (Open

Mfr’ing)

Manufacturing Technology Development5-7 Years

Design

3-5 Years

Test and Evaluation/Qualification/Certification

7-10 Years

Manufacturing variability is not captured until the sub-component/ component level testing

Iterations result from uninformed manufacturing variation

Stochastic manufacturing process variation and non-uniform manufacturing process scaling drives cost and schedule uncertainty, and leads to major barriers to manufacturing technology innovation

Open Manufacturing captures factory-floor variability and integrates probabilistic computational tools, informatics systems, and rapid qualification approaches to build confidence in

the

process

Product Development Cycle

7Slide8

Accelerate development of innovative additive manufacturing processes to reduce risk for first adoptersExemplar: Demonstration of Micro-Induction Sintering for additive manufacturing of metal matrix compositesProbabilistic computational tools (process-microstructure-property models) to predict process and part performanceExemplar: Integrated Computational Materials Engineering (ICME) Tools

for Direct Metal Laser Sintering (DMLS) of Inconel 718Simulate thermal history of the laser sintered powder, residual stress of the sintered material, gamma prime phase particle

size distribution, and material performanceFoundry-style manufacturing processes (Open Mfr’ing)

Process

Models

μ

-structural

Models

Property

Models

Flux

Concentrator

Powder bed

Consolidated

metal matrix composite

8Slide9

Open innovation (VehicleFORGE)

9Slide10

Adaptive Make for Synthetic Biology

10Slide11

1 10 100 1,000 10,000 100,000

Complexity (# genes inserted/modified)

10

10

1011

10

9

10

8

10

7

10

6

10

5

10

4

10

3

Effort

(total $ * yrs to develop) [$*yr]yeast

minimal bacterium

DARPA annual budget

Living Foundries

genome rewrite

complex genetic

circuits

metabolic engineering

LF: after 6

mos

A worrisome trend

SOA

Goal

Design

1-3 months

<1

week

DNA

S

ynth.

$0.45-$0.75

2wks-2mos

20 kb

$0.004

2 days

Mb’s

Test/Debug

weeks

<1 day

Complexity

<10

s

genes

routine

: <10

10

3

-10

4

genes

Total Time

7

yrs

<1

yr

11Slide12

12

Design tools (Living Foundries)

High-Throughput Screening:

Sequencing, RNA-

seq

, Mass spec, Multiplex PCR, LC-MS, GC-MS

Transcript Levels

Protein Levels

Sequencing

Synthesis/Assembly/Strain Creation

:

Molecular Biology

,

Microfluidics and Liquid Handling

Computer Aided Design

JIRA Bug Tracking

Data Management

Design

Build

Test

A

ctivity

Learn

New molecules/new functions

12Slide13

Foundry-style manufacturing (Blue Angel)

Biology provides the

design rules and

models

Vaccine

implementation:

Only the relevant genetic sequence of bug required, not entire virus.

The tobacco plant is the ‘protein foundry.’

Vaccine

implementation:

Redirection of tobacco plant protein production results in

candidate protein synthesis.

DARPA Blue Angel program enabled…

A 4 site manufacturing platform in the USA capable of meeting phase 1 appropriate FDA requirements for vaccine production.

3 Investigational New Drug Applications with the FDA

3 Phase 1 clinical trials

Texas A&M University (TAMU)-Caliber

example

:

Growth room is approximately the size of

half

a football field at four stories tall

(

150 feet x 100 feet x 50 feet high)

Total number of plants: 2.2 million

The result today…

Rapid,

adaptive platform.

Tobacco plant production

may result

in more rapid production cycles (< 30 days) and

less facility expenditures

to increase capacity once an FDA approved product is available.

13Slide14

Unfolded (unstable)

F

olded (stable)

14

Open innovation (

FoldIt

)

Sources: Fold it,

Katib

et al,

Crystal

structure of a monomeric retroviral protease solved by protein folding game players

., Nature Structural and Molecular Biology 18, 1175–1177, 2011Slide15

Adaptive Make for Robotics

15Slide16

Design tools (M3)Analogy: Hierarchical Electronic Design Automation (EDA) has catalyzed circuit design, enabling exploitation of Moore’s law

Robot Design, presently ad-hoc, desperately needs analogous tools, even though the problem is harder:Hierarchical “simulator in the loop”, near-real-time design tools, allowing bi-directional interaction with designers

Designer-guided interactive optimization + design space exploration (e.g. GA)Statistically valid, hierarchical environment and contact modelsStatistically valid, hierarchical human operator + adversary models

We can significantly amplify DARPA’s investment in robotics design tools through

open source partnering

with researchers and enthusiasts worldwide

Our adversaries largely don’t need robots

- improvements in robotics catalyzed by DARPA will

largely benefit the US even if improvements are shared globally

16Slide17

Fabrication (M3)

Serial Processes

Printing ProcessesSelf Assembly

Manual Assembly

Present Rapid Prototyping

Nature

Tissue Engineering

(e.g. insect muscles)

Ron Fearing, UCB

Neal

Gershenfeld

, MIT

(DSO

Prog

. Matter)

Ward, Pratt, et. al (1992)

Roll-Roll Printing

Plate Printing

17Slide18

Open innovation (DARPA Robotics Challenge)

18Slide19

19Slide20

Backup/Reference Charts

20Slide21

Status quo approach for managing complexity

21Slide22

Little change in the systems engineering process

Giffin

M., de Weck

O., et al., Change

Propagation Analysis in Complex Technical Systems, J. Mech.

Design,

131 (

8

), Aug. 2009.

Engineering

Change Requests (ECRs) per Month of Program Life

From Project Inception through Midcourse Maneuver

, vol. 1 of

Mariner Mars 1964 Project Report: Mission and Spacecraft Development

, Technical Report No. 32-740, 1 March 1965, JPLA 8-28, p. 32, fig. 20.

Mariner Spacecraft (1960s)

Modern Cyber-Electromechanical

System (2000s)

22Slide23

Complexity is the root cause of cost growth

23Slide24

AVM integrated toolchain with major releases

Design Update

Feedback

Constraints from Higher

Levels

of Abstraction

Manufacturability

Constraints

Component Model Library

Semantic Integration

Design Trade Space Visualization

Dynamic Visualization

Structural & Entropy-Based Complexity Metrics Calculation

Design Space Construction(Static Models)

Qualitative/ Relational

Models

Linear Differential

Equation

Models

Nonlinear Differential Equation (PDE)

Models

Reachability Analysis

Controller/

FDIR Synthesis

CAD Geometry/ Grid Synthesis

Probabilistic Model Checker

Monte Carlo Dynamic

Sim

Context

Model

Library

FEA

CFD

PLM

User

Req’t

Synthesis

Probabilistic Certificate of Correctness

Foundry Trade Space Construct.

Instruction Sets

BOM

Process Model Library

. . .

Domain-

Specific

Modeling

Languages

Multi-

Attribute

Preference

Surfaces

Static Constraint Solver

Requirements

Verification

Process Mapping

Ass’y

Selection

Machine Selection

Machine/

Ass’y

Mod Lib

CNC Generator

QA/QC

Visualization

Metrics

Legend:

FANG1

FANG2

FANG2’

FANG3

Foundry Resource

Scheduler

24Slide25

Low-fidelity dynamics

Structural

interfaces

25

Power

interfaces

Detailed geometry

Signal

interfaces

Structural

interfaces

Parameter/property

interfaces

FEA geometry

25

AVM component modelSlide26

Integration of formal semantics across multiple domains

META Semantic Integration

Formal Verification

Qualitative reasoning

Relational abstraction

Model checking

Bounded model checking

Distributed Simulation

NS3

OMNET

Delta-3D

CPN

Equations

Modelica

-XML

FMU-ME

S-function

FMU-CS

High Level

Architecture

Interface (HLA)

Composition

Continuous Time

Discrete Time

Discrete Event

Energy flows

Signal flows

Geometric

Hybrid Bond Graph

Modelica

Functional Mock-up

Unit

Embedded Software Modeling

TrueTime

Simulink/

Stateflow

Stochastic Co-Simulation

Open

Modelica

Delta Theta

Dymola

26