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.1

Adaptive Make for Cyber-Physical Systems (Vehicles)

2

A worrisome trend

3

Existence proof

Daily engineer output(Trans/day)

Develop-ment time (mo)

IP block performance Inter IP communication performance models

incr

easi

ng a

bstr

actio

n

Cluster

AbstractCluster

AbstractRTL RTL

clusters

Abstract

Cluster SWmodels

IP blocks

Transistor model Capacity load

Gate level model Capacity load

System-on-chip Design Framework Wire 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

Design tools (META)

• Models are fully composable• Simulation trace sampling to verify

correctness probability• Application of probabilistic model

checking under investigation• 10^2 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 designs

Static Trade Space Exploration Qualitative Reasoning

• Qualitative abstraction of dynamics• Computationally inexpensive• Quickly eliminate undesirable designs• State space reachability analysis• 10^4 10^3 designs

Relational AbstractionLinear 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 iFAB

• Generate structured &unstructured grids

• Provide constraints and input data to PDE solvers

• Couple to existing FEA, CFD,EMI, & blast codes

• 10 1 design

CAD & Partial Differential Equation Models

Embedded Software Synthesis• Auto code generation• Generation of hardware-

specific timing models• Monte Carlo simulation

sampling to co-verify• Hybrid model checking

under investigation

Inner Loop Model

, ,,p,q,r

, ,

6

Inner Loop State

5

Eul er Angles

4

Angular Rates

3

Velocity

2

Position

1

Body vel

y

xyz

x

veloci ty

pqr

atti tude

thrust1

thrust2

thrust3

thrust4

motor1

motor2

motor3

motor4

TTActuatorNetwork

Actual Sensed

Saturation

Thrust Command 1-4

Body v el

Posit ion

Velocity

Angular Rates

Euler Angles

Non-Li near Dynamics

Outer Loop Request

Attitude State

z St ate

Thrust Command

Inner Loop State

Inner Loop Controller

Demux

Demux

eulreq

Atti tude Request

1

Outer Loop Request x,y ,z

x,y ,z

p,q,r

xdot,y dot,zdot

Physi

cal

Softw

are

Compu

ting A

B

5

Foundry-style manufacturing tools (iFAB)

Manufacturing Process Model Library

Constraintsfrom Selected Configuration

META Design

Static Process Mapping SequencingFoundry Trade Space

Exploration

Kinematic Machine Mapping

Topological Decomposition

Kinematic Assembly Mapping

SchedulingCNC Instructions

Human Instructions

*Manufacturing ConstraintFeedback to META Design

Rock Island Arsenal Bldg 299 Final Assembly

*

*

6

Foundry-style manufacturing processes (Open Mfr’ing)

Manufacturing Technology Development

5-7 YearsDesign

3-5 YearsTest and

Evaluation/Qualification/Certification7-10 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

7

• Accelerate development of innovative additive manufacturing processes to reduce risk for first adopters

• Exemplar: Demonstration of Micro-Induction Sintering for additive manufacturing of metal matrix composites

• Probabilistic computational tools (process-microstructure-property models) to predict process and part performance

• Exemplar: Integrated Computational Materials Engineering (ICME) Tools for Direct Metal Laser Sintering (DMLS) of Inconel 718

• Simulate thermal history of the laser sintered powder, residual stress of the sintered material, gamma prime phase particle size distribution, and material performance

Foundry-style manufacturing processes (Open Mfr’ing)

ProcessModels

μ-structuralModels

PropertyModels

Flux ConcentratorPowder bed

Consolidated

metal matrix

composite

8

Open innovation (VehicleFORGE)

9

Adaptive Make for Synthetic Biology

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1 10 100 1,000 10,000 100,0001.00E+03

1.00E+04

1.00E+05

1.00E+06

1.00E+07

1.00E+08

1.00E+09

1.00E+10

1.00E+11

JBEI/AmyrisArtemisinin2009

DuPont, 20021,3 propanediol

1 10 100 1,000 10,000 100,000 Complexity (# genes inserted/modified)

1010

1011

109

108

107

106

105

104

103

Effor

t (t

otal

$ *

yrs

to

deve

lop)

[$*

yr] yeastminimal

bacterium

DARPA annual budget

Living Foundriesgenome rewritecomplex genetic

circuits

metabolic engineering

LF: after 6 mos

A worrisome trend

SOA Goal

Design 1-3 months <1 week

DNA Synth.$0.45-$0.75 2wks-2mos20 kb

$0.0042 daysMb’s

Test/Debug weeks <1 day

Complexity <10s genesroutine: <10

103-104

genes

Total Time 7 yrs <1 yr

11

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 TrackingData Management

Design Build Test

Activity

LearnNew molecules/new functions

12

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.

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Unfolded (unstable)

Folded (stable)

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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, 2011

Adaptive Make for Robotics

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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 models• Statistically 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

Exp. Sim.

0

10

20

30

40

50

60

70

80

90

100

Position 1

Perce

ntage

(%)

Exp. Sim.

0

10

20

30

40

50

60

70

80

90

100

Position 2

Perce

ntage

(%)

Exp. Sim.

0

10

20

30

40

50

60

70

80

90

100

Position 3

Perce

ntage

(%)

Exp. Sim.

0

10

20

30

40

50

60

70

80

90

100

Position 4

Perce

ntage

(%)

Exp. Sim.

0

10

20

30

40

50

60

70

80

90

100

Position 5

Perce

ntage

(%)

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Fabrication (M3)

Serial Processes Printing Processes Self Assembly)()( 3/13/2 NONO ))(ln()( 3/1 NONO )(NO

Manual AssemblyPresent Rapid Prototyping

NatureTissue Engineering

(e.g. insect muscles)

Ron Fearing, UCBNeal Gershenfeld, MIT

(DSO Prog. Matter)

Ward, Pratt, et. al (1992)

Roll-Roll PrintingPlate Printing

101

102

103

104

105

106

100

101

102

103

104

105

106

N

Tim

e

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Open innovation (DARPA Robotics Challenge)

18

www.darpa.mil

19

Backup/Reference Charts

20

Status quo approach for managing complexity

CostOptimization

Power Data & Control Thermal MgmtSWaP

Optimization

SWaPOptimization

System FunctionalSpecification

. . .

. . .

SubsystemDesign

ComponentDesign

SystemLayout

Verification & Validation

ComponentTesting

SubsystemTesting

SWaP = Size, Weight, and PowerUndesirable interactions (thermal, vibrations, EMI)Desirable interactions (data, power, forces & torques)

V&V = Verification & Validation

System decomposed based on arbitrary cleavage lines . . .

Conventional V&V techniques do not scale to highly complex or adaptable systems–with large or infinite numbers of possible states/configurations

SWaP used as a proxy metric for cost, and dis-incentivizes abstraction in design

Unmodeled and undesired interactions lead to emergent behaviors during integration

. . . and detailed design occurs within these functional stovepipes

MIL-STD-499A (1969) systems engineering process: as employed today

Re-Design

Resulting architecturesare fragile point designs

21

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)

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Complexity is the root cause of cost growth

23

AVM integrated toolchain with major releasesDe

sign

Upd

ate

Feed

back

Cons

trai

nts f

rom

Hi

gher

Lev

els o

f Ab

stra

ction

Man

ufac

tura

bilit

yCo

nstr

aint

s

Com

pone

nt

Mod

el L

ibra

rySemantic Integratio

n

Design Trade Space Visualization Dynamic Visualization

Structural & Entropy-Based Complexity Metrics Calculation

Design Space Construction(St

atic 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 SimCo

ntex

t Mod

elLib

rary

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

RequirementsVerification

Process Mapping

Ass’y SelectionMachine Selection

Machine/Ass’y Mod Lib

CNC Generator

QA/QC

Visualization

Metrics

Legend:

FANG1

FANG2

FANG2’

FANG3

Foundry ResourceScheduler

24

Low-fidelity dynamics

Caterpillar C9 Diesel Engine : AVM Component

High-Fidelity Modelica Dynamics Model

Rotational Power Port Signal Port

Low-Fidelity Modelica Dynamics Model

Rotational Power Port Signal Port

Bond Graph Dynamics Model

Rotational Power Port Signal Port

Detailed Geometry Model (CAD)

Structural Interface

Structural Interface

FEA-Ready CAD Model

Structural Interface

Structural Interface

ThrottleSignal Port

map

Power OutRotational Power Port

map

MountStructural Interface

map

Bell HousingStructural Interface

map

Weight680 kg

Length1245 mm

Number of Cylinders6

Maximum Power330 kW

Height1070 mm

Width894.08 mm

Maximum RPM2300 rpm

Minimum RPM600 rpm

Structuralinterfaces

Powerinterfaces

Detailed geometry

Signalinterfaces

Structuralinterfaces

Parameter/propertyinterfaces

FEA geometry

25

AVM component model

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

EquationsModelica-XML

FMU-MES-functionFMU-CS

High LevelArchitectureInterface (HLA)

Composition• Continuous Time• Discrete Time• Discrete Event

• Energy flows• Signal flows• Geometric

Hybrid Bond Graph

ModelicaFunctional Mock-up

Unit

Embedded Software Modeling

TrueTimeSimulink/Stateflow

Stochastic Co-Simulation

• Open Modelica• Delta Theta• Dymola

26