From Art to Science: Why old tasks get easier, but ... · PDF file1 From Art to Science: Why...
Transcript of From Art to Science: Why old tasks get easier, but ... · PDF file1 From Art to Science: Why...
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From Art to Science: Why old tasks get easier, but
everything gets more complex
Roger Bohn, [email protected] 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