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From Art to Science: Why old tasks get easier, but

everything gets more complex

Roger Bohn, Rbohn@ucsd.eduMay 2006Paper at: http://repositories.cdlib.org/postprints/808/

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What is technological progress?

What changes?New domains, new products

Better performance, cost -- but why?

How/what work is done

Craft/Art Science/ProcedureConfusing Well documented

Idiosyncratic methods Standardized

Random result PredictableControl by people Computerized

ExamplesArt-to-science examples

Aviation: Sopwith Camel to Global Hawk

Disk drives: Moore’s Law

Check clearing

Evolution of workJobs: offshoring, automation, outsourcing

Business process re-engineering

Will professional services follow? (law? teaching?)

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Agriculture: Soil chem + GPS ==> per meter recipe

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OutlineTransformation of manufacturing

One family, 1 industry, 1 company; 200 yearsFrom filing and fitting to flexible manufacturing: the evolution of process control by R. Jaikumar, 2005http://www.nowpublishers.com/getpdf.aspx?doi=0200000001&product=TOMHow work changed

What is technology: Methods + Knowledge

How technology evolves

Fractal complexity; other recurrent patterns

Modularity + separation of K in complex societies

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*Firearms 1715-1985

Images from Diderot’s

Encyclopedia

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Six epochs of manufacturingThe Craft System (circa 1500)

1. Invention of machine tools and the English System of Manufacture (circa 1800)

2. Special purpose machine tools and interchangeability of components in the American System of Manufacture (circa 1830)

3. Scientific Management and the engineering of work in the Taylor System (circa 1900)

4. Statistical process control (SPC) in an increasingly dynamic manufacturing environment (circa 1950)

5. Information processing and the era of Numerical Control (NC, circa 1965)

6. Flexible and Computer-Integrated Manufacturing (CIM/FMS, circa

0

3.75

7.50

11.25

15.00

Engl

ish

Amer

ican

Tayl

orSP

C

Num. C

ontrol

CIM/F

MS

Line workers per machineRework/Total work

Source: Jaikumar 2005

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Epochal changes:Intellectual watershed in how people thought about manufacturing and its key activities

Key technological change in each case: Solving new process control problem

In all cases, this problem revolved around controlling variation.

Entailed introduction of a new system of manufacture;Machines, the nature of work, and the organization all had to change in concert Approx 10 years to assimilate the changes

All originated outside Beretta

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English System

American System

Taylor System

Statistical Process Control

Numerical Control (CNC)

CIM/ FMS

Year 1810 1860 1928 (1900) 1950 1976 1987 # of People 40 150 300 300 100 30 Productivity Increase

4:1 3:1 3:1 3:2 3:1 3:1

Number of Products ∞ 3 10 15 100 ∞ Worker Skills Required

Mechanical craft Repetitive Repetitive Diagnostic

Experime

ntal

Learn/ generalize/

abstract

Na t

u re

of W

o rk

Control of Work

Inspecti

on of work

Tight supervision of work

Loose of work/ tight contingenc

ies

Loose of contingenc

ies

No supervision of work

No supervision

of work

Process Focus

Accurac

y

Precision: Repeatabili

ty (machines)

Precision: Reproducibility (processes)

Precision: Stability

(over time)

Adaptabil

ity

Versatility

Focus of Control

Product functionality

Product conforman

ce

Process conforman

ce

Process capability

Product/ process integration

Process intelligence

Tec

hno l

ogy

Key

s

Instrument of Control

Micrometer

Go/No-Go gauges

Stop watch

Control chart

Electronic gauges

Professional workstation

*Changing nature of work

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Technological Knowledge: What is it?

Dichotomies eg: tacit vs. explicitIndividual vs. collectively held

Know-how vs. know-why

Learning- Curve models: Production Improvement

Production Learning Knowledge Changed Methods Better Outcomes

Examining knowledgeVincenti: What do engineers know & how do they know it?

Bailey and Gainsburg (2004)Literature on org. knowledge and technical work “underestimates importance of formal, often technical, knowledge in ...tasks.”

Jaikumar and Bohn: Stages of knowledge (1994)

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Controlling variationResponding to disturbances

Dynamic world: Deliberate changes

Tolerances etc.

Precise control dominates higher speed (quantity)

Speed matters: economically important

Limit to speed is control of variation

Manufacturing: Key problem = controlling the

physical process

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Key Knowledge in the English System

Specifying/measuring the intended outcome

Dimensions (new concept)

Micrometer (new metrology tool)

Isometric drawing (new mathematical method)

Allows crude feedback loop: keep going until goal

General purpose machine tool

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Bayesian networks for causality

Directed acyclic graphJudea Pearl: causal reasoning

Arrow = flow of causality (conditional probability; function)

Axiom: Core of technological knowledge is knowledge about causality (in human- created systems)

Both variables (nodes) & relationships (arcs) are knowledge

X4 = f(X2, X3)

Systems of structural equations in economics

As more learned, known causal network grows

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Level of Knowledge

1

2

3

4

5

6

Level of Importance

y Normalization

X1 Actual Pwr

X111 Eo X1111 SlurryMaterial

X11 Ri

X1112 Tc

X1113 ElementPlacement

X1114 Th

X1115 Power Number

X1116

Voltmeter

X1121 Shunt

X112 LoadedVoltage

X1122 Lag time

X112112 Al Resistivity

X111515 Pinch Off

X112115 Puck Ejection

X112116 Cutting Speed

X112114 Module Thickness

X1121136 Molybdenum

X11121 Water Temp

X11141SurfaceFinish

X11141 ACPower

X11151SeebeckCoefficient

X11211 Current

X111311 Grit Size

X111513 Dopant Weight

X111512 Raw Material Weight

X111511 N Pump Time

X111514 Raw Material Purity

X111516 Mold

X111517 Aging Time

X111518 Aging Temp

X111519 Mixing Rate

X11151A MixingTemperature

X1112111 ResistivityX11211362AtomizingAirX11211363

GunDistance

X11211364CoolingAir

X11211365 Deposition rate

X11211361 Voltage

X112113651 Surface Speed

X112113652 Wire Size

X112113653 Current

X11121111 Time @ vacuum

X1121125 Gun VoltageX1121124 Atomizing AirX1121123 Gun Distance

X11121112 N Powder Storage TimeX11121113 Puck Removal TemperatureX11121114 Cold VacuumX11121115 Powder Size

X11121116 Hot VacuumX11121117 Time at Load

X11121118 Pressing Load

X11121119

Backfill

X1121121 Deposition RateX1121122Humidity

X1121126 Cooling AirX1121127 Dust ControlX1121131 FeedrateX1121132 Belt Speed

X1121134 Surface CleanX1121135 Element Finish

X11211211 Gun Current

X11211212 Wire Size

X11211213 Surface Speed

X1121133 Sand Blast

X112113 Interface Resistance

0.0 to 0.10.2 to 0.50.6 to 1.0

1.1 to 2.0

2.1 to 9.9

≥10.0

The importance of avariable to the finalnode is a product ofthe importance ofdownstreamrelationships. It isrepresented by thearea of the node.

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*Machining as causal system

Causality propagated by material flows, information flows, + much more.

Each variable expands as more known

Causal subsystem: more variables, relationships learned (fractal)

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OutlineTransformation of manufacturing

One family, 1 industry, 1 company; 200 yearsFrom filing and fitting to flexible manufacturing: the evolution of process control by R. Jaikumar, 2005http://www.nowpublishers.com/getpdf.aspx?doi=0200000001&product=TOMHow work changed

What is technology: Methods + Knowledge

Examples of new knowledge: FW Taylor discoveries

How technology evolves

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Taylor 1: ReductionismElaborate each subsystem in minute detail

Sharpening a tool is independent of using it

Concept of optimal method

Examples:

Tool maintenance

Storeroom

Tool material

Power transmission

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“Power transmission” = entire subsystem

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Taylor Innovation #2: Knowledge as systems of math equations

A. What are the key variables? (The nodes in the causal graph.)

Determinants of optimal cutting speed: 10 (sic) variables

B. What are their relationships? (The arcs)

C. Given A and B, what is optimal way to machine metal?

1. Quality of the metal to be cut, e.g. hardness 100 2. T he depth of cut 1.36 3. T he feed per revolution of the workpiece 3.5 4. T he elasticity of the work or tool 1.15 5. Shape /contour of the cutting edge of the tool,

together with its clearance and rake angles 6

6. T ool material: chemical composition and heat treatment

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7. U se of a coolant such as water 1.4 8. T ool life before it needs to be reground 1.2 9. T he lip and clearance angles of the tool 1.023 10. The force exerted on the tool by the cut 11. The diameter of the workpiece

12. The maximum power, torque, and tool feeding force available on the lathe.

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System of equations (maps to causal network)

V20 = cutting speed that leads to a 20 minute tool life, feet per minute

r = tool nose radius, inches

f = feed per revolution, inches

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Express knowledge in operationally useful way

Analog computer to

calculate simultaneous

equation system

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Each epoch specialized K

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OutlineTransformation of manufacturing

One family, 1 industry, 1 company; 200 yearsFrom filing and fitting to flexible manufacturing: the evolution of process control by R. Jaikumar, 2005http://www.nowpublishers.com/getpdf.aspx?doi=0200000001&product=TOMHow work changed

What is technology: Methods + Knowledge

How technology evolves

Fractal complexity; other recurrent patterns

Modularity + separation of K in complex societies

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*Patterns across the epochs

Properties and evolution of K graphseg backwards

Classes of solutions recurSpecial role of measurementHow is complex society possible?Punctuated equilibrium?

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Evolution of Know. graphsIncreasing complexity

More variables, relationships among known variables

Increasing depth

Some kinds of knowledge often critical: solution classes, measurement

Knowledge grows backwards: from effects, to causes

Rising stages of Knowledge for variables, relationships

Fractal complexity: closer you look, more complex the causal relationships

Networks are not hierarchical trees:Variables have multiple descendants

Modularity

1

Level of Knowledge

1

2

3

4

5

6

Level of Importance

y Normalization

X1 Actual Pwr

X111 Eo X1111 SlurryMaterial

X11 Ri

X1112 Tc

X1113 ElementPlacement

X1114 Th

X1115 Power Number

X1116

Voltmeter

X1121 Shunt

X112 LoadedVoltage

X1122 Lag time

X112112 Al Resistivity

X111515 Pinch Off

X112115 Puck Ejection

X112116 Cutting Speed

X112114 Module Thickness

X1121136 Molybdenum

X11121 Water Temp

X11141SurfaceFinish

X11141 ACPower

X11151SeebeckCoefficient

X11211 Current

X111311 Grit Size

X111513 Dopant Weight

X111512 Raw Material Weight

X111511 N Pump Time

X111514 Raw Material Purity

X111516 Mold

X111517 Aging Time

X111518 Aging Temp

X111519 Mixing Rate

X11151A MixingTemperature

X1112111 ResistivityX11211362AtomizingAirX11211363

GunDistance

X11211364CoolingAir

X11211365 Deposition rate

X11211361 Voltage

X112113651 Surface Speed

X112113652 Wire Size

X112113653 Current

X11121111 Time @ vacuum

X1121125 Gun VoltageX1121124 Atomizing AirX1121123 Gun Distance

X11121112 N Powder Storage TimeX11121113 Puck Removal TemperatureX11121114 Cold VacuumX11121115 Powder Size

X11121116 Hot VacuumX11121117 Time at Load

X11121118 Pressing Load

X11121119

Backfill

X1121121 Deposition RateX1121122Humidity

X1121126 Cooling AirX1121127 Dust ControlX1121131 FeedrateX1121132 Belt Speed

X1121134 Surface CleanX1121135 Element Finish

X11211211 Gun Current

X11211212 Wire Size

X11211213 Surface Speed

X1121133 Sand Blast

X112113 Interface Resistance

0.0 to 0.10.2 to 0.50.6 to 1.0

1.1 to 2.0

2.1 to 9.9

≥10.0

The importance of avariable to the finalnode is a product ofthe importance ofdownstreamrelationships. It isrepresented by thearea of the node.

Process & machine

variables ⬇

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*Some types of solutions appear again

New mathematical methods (creation and articulation of knowledge)Basic variation-reduction strategiesFeedback-based controlImproved metrology: precision and speed

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*Special role of measurement

Measurement limits precision

English: micrometer

American: go/no-go gauges

NC: coordinate measuring machine

Measurement speed determines feedback speed

Laboratory, to off-line, to on-line, to real-time

Measurements = “production” processes

Own causal networks, technology choices, economics, etc

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Causal knowledge fractal and cyclic

How can modern industrial systems be built?

Infinite regress of causality + control:

Product behavior ⬅ product construction ⬅ process control ⬅⬅ Machine + RM construction ⬅ Product behavior again

To build a modern disk drive requires a modern semicon process

Back to first tool: hand axe?

Soln: Modular knowledge enables complex society

Modular causal graph ⇒ local knowledge suffices

“Unit controller” box: moves 30 micronsEmbeds: sensor + actuator + PID controller

Handbooks, catalogs, and specificationscommunicate relevant knowledge

Only outcomes matter to users

Generalization of machine tool concept • Ultra-Fast Response • Ultra-Precise Trajectory Control • Digital Controllers with Fast

FiberLink Interface Available • ID-Chip for AutoCalibrate • Direct-Metrology Capacitive

Sensors for Highest Precision • 0.1 nm Resolution • PICMA® High-Performance Drives

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Punctuated equilibrium

Knowledge grows incrementally

Adding refinements to existing graph

Better control of same key variables

Discontinuity

New requirement created elsewhere in network (e.g. CMP; MR heads <== EMI)

Jump from one tech trajectory to another

Must quickly fill in new area in K graph

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Technology discontinuity: new physical processFundamentally new physical mechanisms. Example:

Electrical discharge machiningAbrasive water jet machiningElectrical chemical machining

What happens to causal knowledge network?

1. Replace subsystem: tool cut “blast” removal

2. Learn new set of key variables,

3. Learn causal network in detail

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Subsystem Key determinants of ECM performance

Sample values

Current 50 - 50,000 Current areal density 10 – 500

A/cm2

Voltage 5 - 30

Power system

Pulse shape (on time, rise rate, etc.)

Aqueous or nonaqueous specific molecules Organic or

inorganic Alkalinity Mixtures

Electrolyte Composition

Passivating or nonpassivating

Flow rate Pressure Max 5MPa Temperature

Fluid circulating system

Concentration Contour gradient Radii Flow path Flow cross section

Tool design; tool/workpiece geometry

Tool feed rate

Key control variables in ECM

> 80% of variables

completely new

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*Electro-chemical machiningDifferent workpiece characteristics affect behavior

Unaffected by the strength and hardness (opposite of conventional)

Affected by electrical parameters (opposite of conventional)

Unaffected by thermal characteristics (different than conventional)

So good for pieces hard to machine conventionally (eg low rigidity parts)

Poor accuracy: electrical current flow influenced by many factors, hard to predict

So, Tool shape must be modified by trial and error

Accuracy 10 to 300 microns even then

Translation: causal graph incompletely known even after production feasible

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ECM more of an “art”Knowledge less complete

Variables, relationshipsLower stage of relationships (eg empirical curve fit )Less detail in ancestors of key variables

Outcomes harder to predictControl methods less automaticUse trial and error to refine mold shape

What knowledge is not obsolete?

Some subsystems change littleCNC machine toolsOff-line measurement methods

Hidden variables: unknown properties

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*Art and scienceBetter knowledge of relationships: stages of knowledge

Ignorance; awareness; direction (+ or -), slope, numerical equation, theory-based equation

Denser and darker networksNew areas: start as art

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Non-manufacturing?Mass information services: IT-based

Product design: manufacturing system = very elaborate product

Farming: Green Revolution; micro-fertilizers

Professional services: incomplete knowledge

BUT are people deterministic?

We view the task of causal modeling as an induction game that scientists play against Nature. Nature possesses stable causal mechanisms that, on a detailed level of descriptions, are deterministic functional relationships between variables, some of which are unobservable. {Pearl, 2000 #1223, p 43}

The true causal network is complete and deterministic: in principle it exactly predicts all causal relationships, at all scales from AUs and years to nanometers and nanoseconds.

Does this apply to biological systems? To people?

Open issues Technological determinism? (No)How much of technological knowledge does theory cover?Implications of feedback loops in causal networks?Implications for early-stage processes (Art) ?How to learn

Where take this research?

Evolution of a professional field eg medicine (surgery; human health)Early-stage fields eg politics, lawFields w more tangible knowledge eg softwareIntegrate with book on how societies learn (“Sterile Mice and Flight Simulators”)

Thank you

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*Characteristics of problems needing control

Key problems = tolerances/variation + operating speeds

Control of more and smaller disturbances

Increasing number of side effects

Growing list of requirements from downstream

Problems, once solved, recur

Multiple solutions: old ones refined + new ones added

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*Implications?Many patterns described before

Causal knowledge graph provides formal model of underlying reasons

Tractable for detailed research? (eg inventory)

Tacit knowledge (art) dominated by detailed formal knowledge (“science”)

Sensorimotor skills: machining, piloting, surgery.

Expertise’ role: novelty, learning, design, but not mass execution

Practical applications:

Debugging, problem solving

Quantitative version: variance propagation eqn.

Determining maturity + holes in tech