Robust Design Engineering Excellence Hardware Fundamentals Workshop Prepared By: The Engineering...

Robust Design

Engineering ExcellenceHardware Fundamentals Workshop

Prepared By:

The Engineering Excellence Institute

Xerox Corporation, Webster, New York 14580

Copyright 1996 Xerox Corporation. All rights reserved.

July 1996

Revision #002

2

EEFW - Robust Design Revision #002

At the end of this module you will be able to:

• Describe the basic concepts of Robust Design

• Describe the basic elements of Robust Design

• State how Robust Design impacts the entire design process to

improve TTM

• Relate Robust Design to the Design Quality Process

• List the process steps to achieving Robust Design• Recognize the Robust Design Quality Enablers and inspect for

this robustness

Objectives

3


Technology Readiness

3.2

DefineProduct &DeliverTechnology

DemonstrateProduct

3.4

DeliverProduct

3.5

DelightCustomers

3.63.3

Design Product

Market &

Product

Strategy

Vision

3.1

DefineProductPlatform &Technology

Product Launch ReadinessTechnology/Design Validation

As we progress towards Robust Design crossover occurs sooner and the length of the Design and Demonstration Phases decreases

A larger S/N ratio results in a robust design with the latitude to accommodate ‘Noises’

DesignLatitude Variance

Major Connections To Time To Market

The “Bottle Model”.........

4


Major Connections To Time To Market

• Technology Readiness

– Technology Readiness is confirmation that the hardware/software configuration supports the program’s overall objectives from a technical perspective

• Reusability

– Ability to handle a variety of signals and new or changing customer requirements

• Reproducibility

– Making technology insensitive to all sources of variation

– Selecting good tuning factors for adjustment

– Assuring that best conditions in labs are best for downstream customers.

The following considerations require Robustness of Function which can be achieved by developing actual functions close to ideal, i.e. to close the gap between actual function and ideal function by applying robust design process.

5


• Develop low cost product designs, mfg process designs, measurement system designs with consistent functional performance under a wide range of usage conditions through systems intended life.

• Improve product quality within constraints of cost and time, through more efficient, customer focused, R&D activities.

• Achieve desired customer performance through optimization of designs, rapid commercialization of technologies, and reduced quality loss after shipment

Purpose of Robust Design

Product Quality Model: Quality achieved when VOC is met, and the enablers are process capable.

BAB 6/6/96

Voice of the Customer (VOC)

1

1a

22a

House 1System

PerformanceSpec Devlp. Production and

Quality Control Planning Matrix

Multiple Houses

Pre planning matrix1 VOC1a Customer

Importance Rating

2 CustomerCompetitive Evaluation

2a XC Strategy and Quality Plan

House 2Module Level

CriticalParameter

Devlp.House 3

Subsystem LevelCritical

ParameterDevlp.

House 4Component Level

Critical Specifications

Devlp.

House 5Manufacturing

Process ParameterDevlp.

Integrated System Design

3.1 3.3 Design 3.4 Demonstrate 3.5 Deliver3.2 Define

Manufacturing Quality • Key Quality Indicators• Manufacturing Quality Plan• Design/Manufacturing Readiness

• Product Verification Test (PVT) Implementation• Supplier Process Certification• Manufacturing Process Robustness

• Planning / Customizing

• Status vs Plan • Verification, Plan customer satisfaction monitoringDesign Quality

Software Process Management and Software Capability Maturity

Reusability Technology, Modules, Manufacturing Process and Parts

Technology Readiness Selection Ready Validation

Process Qualification of Critical Specifications and enablers

Robust Design

• Data and DemonstrationCritical Parameter Management Implementation • Critical Parameter

Process Capability Verification

Optimum Nominal Value and Tolerance of Critical Parameters/Critical Specs DeterminedParameter Design Phase

Trade-offs - (Exceptions) Performance vs CostTolerance Design Phase

Optimum Concept Selected - Best of Breed Will Meet VOC at Lowest CostConcept Design

MPSV 3.6

Quality Function Deployment (QFD)

Customer Feedback

Market Analysis

Focus Interviews

Development

Quality:

• Key Quality Indicators • Quality Initial Design Emphasis• Problem Discovery Process

• Customer Survey

• Customer Survey• Supplier Process Qualification• Factory and Plan Implementation

• Plans/Strategy

On Line Quality Control

7


Robustness:

The ability of a product or process to function close to ideal customer satisfaction under actual conditions of use. A product or process is said to be robust when it is insensitive to the effects of sources of variability, even though the sources themselves have not been eliminated.

Robust Design:

A systematic engineering based methodology (which is part of a quality engineering process) that develops and manufactures high reliability products at low cost with reduced delivery cycle. The goal of Robust Design is to reduce cost and quality loss. Concept Design, Parameter Design, tolerance design and on-line QC are the 4 successive stages. Parameter design, most widely practiced,uses a two step optimization process --Maximizing S/N ratio and then Tuning to Target.

Ideal Function:

A mathematical relationship between input and output, which can be used for function or energy transfer optimization. Ideal function of resistor is Ohm’s law, to convert current to voltage.

Definitions

8


Control Factor:Factors affecting the function whose levels can be specified freely by engineer / designer. Their settings are used to amplify sensitivity to signal, dampen sensitivity to noise plus tuning to target .

Noise Factor:Factors affecting the function which cannot be controlled by the engineer/designer. Factors whose settings are difficult or expensive to control are also called noise factors. There are three noise classifications:

1) Internal 2) External 3) between product noises

Response factor:Response factors are often called the response variables, measurands, quality characteristics, outputs. These are also selected by engineers to pick up information about control, noise, and signal factor effects. Some desirable properties for responses include:- fundamental (related to basic energy transfer mechanism of input/output relationship) - continuous, quantitative - monotonic with changes in control factor levels , unambiguous.- should be complete (cover important dimensions of the function)

- valid, independent of imposed specifications - economical, timely

Signal Factor: Factor whose levels carry the information to easily change output response, usually related to input power.

It is based on the physics or engineering of the system.

Definitions

9


Each customer expects that every product will deliver the target performance each time the product is used, under all intended operating conditions, and throughout its intended life, with no harmful side effects.

“When a product’s performance deviates from the target performance, its quality is considered inferior. Such deviations in performance cause losses to the user of the product, and in varying degrees, to the rest of society.”

G. Taguchi (1992)

Zero Defects CPk S/N Ratio

Achieve Target Values

A B C

Ideal Quality

10


Traditional Quality vs. Robust Quality

Traditional QualityGood/No Good

No GoodLoss $

Functional Limits

GoodNo Loss

No GoodLoss $

Paradigm ShiftContinuous Quality loss

away from target

Functional Limits

No Good No Good

Poor

Good

Fair

Poor

Good

Fair

Best

target

target

11


Summary / The Robustness Paradigm

• Robustness = Problem Prevention

• Any deviation from target incurs a quality loss

• Don’t need to always control / eliminate the root cause to improve design

• High performance does not always require high cost

• Robust Design process is to improve efficiency of R&D

• Engineering focus using mathematical and statistical concepts

12


Proactive Quality Indicators of Robustness

Quality Indicator Desired StateS / N RatioReliable Measures of FunctionFunctional Limits / LatitudeImportant Noise FactorsRange of OperationPower ConsumptionBenchmark Comparison Test GapDesign ComplexityTuning FactorsCritical Parameter Nominal ValuesCostQuality LossSystem Integration ProblemsUnderstanding of DesignData Supporting Decisions

Gain Over TimeIdentifiedExpanded / IncreasedIdentifiedExpandedReducedReducedReducedIdentifiedIdentifiedNo IncreaseImprovedFewerImprovedUnambiguous

13


Signal To Noise Ratio:

Power of signal to create functional output

Power of noise to create dysfunctional output

Useful Part of input power

What you don’t want

S / N =

S / N =

• All engineering functions are transformation of energy from one form to another, one place to another, one time to another

• Variations in energy transfer cause functional variation

• Therefore, Maximize Signal to Noise:

} Improve the functionwhile simultaneouslyreducing the dysfunction

Definitions

In Laymans’ terms

S / N =What you want

Harmful Part of Input Power

14


Input

Output

DesignConcept

DesignConcept Manufacturing

Process Concept

ManufacturingProcess Concept

Robust ProductDesign

Robust ProductDesign

Robust Mfg. ProcessDesign

Robust Mfg. ProcessDesign

Robust Product Process Capable

ProcessSteps System Verification

Test Process

System VerificationTest Process

Tolerance DesignProcess

Tolerance DesignProcess

Production / FieldReadiness Test Process

Production / FieldReadiness Test Process On-Line Quality

Control Process

On-Line QualityControl Process

Manufacturing ToleranceDesign Process

Manufacturing ToleranceDesign Process

Mfg. Process VerificationTest Process

Mfg. Process VerificationTest Process

Parameter DesignOptimization Process

Parameter DesignOptimization Process Mfg. Process Parameter

Optimization Process

Mfg. Process ParameterOptimization Process

Robust Design as Part of Concurrent Engineering

15


In this innovation stage of the design process, the system and subsystem engineer examines a variety of architectures and technologies for achieving a desired function for a planned product and selects the most suitable one(s).

Once the concept is selected, the design engineer determines the best nominal values and tolerances for each of the critical parameters of the system / subsystem that will produce consistent output using low cost components and tolerances.

If at the end of the parameter design stage the output is not at or above benchmark , a third stage is introduced to improve quality by selectively adding cost by upgrading components and/or tightening tolerances, maintaining cost and quality balance.

Cost effective maintenance of quality

ConceptDesign

ToleranceDesign

ParameterDesign

On-Line QCDesign

Process Stages of Robust Design

ParameterDesign

16


System Design

Debug/PIT/System/Characterization Tests

Release DesignFor Production

CostReduction

No

Redesign/Fix

Prototype Hardware

Meets ProductSpecifications

YesRelease

Design for Production

SubsystemTechnology

Development

Parameter DesignExperimental Hardware/Modeling

Robust?

Yes

SVT

YN

No

ToleranceDesign

Design Intent Hardware

Subsystem Verification Tests

System Design Concept

Build, Test, Iterate

Problem Solving

VerifyProductSpecs?

From Problem Solving to Problem Prevention

Problem Prevention

17


ConceptDesign

ToleranceDesign

On-Line QCDesign

Tools Output MeasuresQFD House I Voice of CustomerBenchmarking Benchmark Defined Signal to Noise RatioPugh/Combinex Selection Matrix Concept Chosen FMEA, DFA/DFM Failure modes Reliability EstimatesVA/VE FAST diagram (projected CP’s) Cost per functionModeling Math. Model Validity of Model

Critical Parameter Nom. Signal to Noise Ratio Taguchi Methods and Ranges Defined, Sensitivity

Technology Verified

Taguchi Methods Cost & Performance Cost & Quality Loss Trade-offs Complete

SPC / Production Control Cpk, Taguchi Methods Quality Loss

ParameterDesign

Process Stages: Tools, Outputs, and Measures

18


Robust Design as a Part of Critical Parameter Management

}

Design Hardwareand OptimizationExperiments

Critical Parameter Development Activity 5.2:Conduct Experiments /

Analysis to VerifyPerformance at CPNominal

Conduct Experiments to Define CP Range( tolerance )

ToleranceDesign /Analysis

SystemRobustness

Demo Reset&

VerifyNominal Set Points

Conduct /Analyze

Experiments

ParameterDesignPlans

Fixture &

Measurement

System

Development

1

2

345

6

On-Line Quality Control

1.0 Concept Design

}

}}

Conduct Experimentsto Optimize CPNominals and Verify all CP’s Identified

Critical ParameterDevelopment Activity 2.1:

Critical ParameterDevelopmentActivity 5.2:

Critical ParameterDevelopmentActivity 6.1:

Refer to slide 34 for detailed inspection list for each step

19


SIGNAL FACTORSInputs Main

Function

CONTROL FACTORSDesign aspects you control

PrimaryResponses

functional outputs

Secondary Responses

dysfunctional outputs

Signal/Noise Ratio &

Sensitivity

Expected Quality Loss

Benchmarking

Failure Mode

Failure Mode

Counter Measure

NOISE FACTORSDesign aspects you can't control

Steps 1 & 2: Parameter Design Methodology

Step 1: Fixture & Measurement System Development

Step 2: Parameter Design Plans

345

345

6

20


Signal Factors

Replenisher Dispensing

PrimaryResponse Trickle

Charging

PrimaryResponse

Signal Factors

Control Factors Noise FactorsR

espo

nse

Signal

S / N ~ Beta 2 / Sigma2

Signal Factors

PrimaryResponse

DeveloperMixing

Signal Factors

PrimaryResponseMagnetic Roll

Loading

PrimaryInput

rgf-4/96

PrimaryInput

Signal Factors

PrimaryResponse

Signal Factors

PrimaryResponse Cloud Generation

(Jumping)

Signal Factors

PrimaryResponsePhotoreceptor

Development

Donor RollLoading

Steps 1 & 2: Design Decomposition & Flowchart:Dispensing Replenisher and Development Subsystem



* Develop Ideal Function for each subfunction

345

345

6

21


Control Factors Noise Factors

Signal FactorAuger Speed

Dispense Rate (Gm/Min)

Function: Replenisher Dispensing

Ideal Function: Convertauger speed to dispense rate

(BO

TT

LE

)

Dis

pens

e G

PM

Auger RPM

S / N ~ Beta2 / Sigma2

Spiral Pitch(1)

Spiral Depth(2)

diameter (6)

(CA

P) # of fins (3)

fin type(4)

fin length (10)

Bottle speed ((9)

pitch(5)

pitch(5)

diameter (6)

(A

UG

ER

S)

LO

WE

R-U

PP

ER

toner flow (1)4 -26 HF#

tilt angle (2)+ \ - 2deg.

dispenser (3)tolerances#1 vs.#2

Inputs Primary Response

P/C calls for toner

* Replenisher dispense rate for 6%A/C at 40PPM to 25%A/C at 65PPM with a 3:1 Ratio of toner to carrier by weight.


Signal FactorCarrier added

Sump Mass & Charge

Main Function: TrickleCharging of Developer

Ideal Function: Trickleout equals carrier added

Dev. Housing tilt angle**+ / - 2 Deg. (2)

Tri

ckle

(gm

s)

Carrier Added (gms )

S/ N ~ Beta2 / Sigma2

Primary Response

overflow location (1)

overflowheight (2)

Mix Auger Type (3)

Mix Auger Speed (4 )

Developerdispense rate*2-10 GPM (1)

Toner Concentration1-4 % (3)***

Steps 1 & 2:Example for Toner Dispenser

4



345

345

6

22


Level21 3Signal Factor

Auger Speed (rpm) 12081.542.5

Signal 3Response Factor

Dispense Rate (g/m)*y1 ,y2 ,y3 ,y4

Control Factor Units

mm

mm

1

Levels

2 3

mm

mm

rpm

type

mm

#

#

scoopangle

5 6

40 50

8

12

13

large

14.5

10.5

10.5

med

9.75

perp

4

30

4

8

sm

4.82

9

# of Fins

Spiral Pitch

Spiral Depth

Auger Pitch

Auger Dia

Fin Type

Auger Opening

Bottle Speed

1

2

3

4

5

6

7

8

Noise Factor

Material Flow Rate &Angle

Units Level1 2

Dispenser #

gpm deg

#

4.0 -2

1 2

22.0 +2

Ideal Function:Convert auger RPM to dispense rate

Steps 1 & 2: Process Chart (P-Chart)



345

345

6

Signal 2Signal 1

y5 ,y6 ,y7 ,y8

y9 ,y10,y11 ,y12

* 3 signal levels * 4 noise levels =12 data per test

23


Dispense Rate vs. Auger Speed:

Auger Speed (rpm)

12042.5

g/m

Dis

pen

sed

40 Data Points @4 Noise Levels

Beta = Grams / Revolution = Mechanical Efficiency

Two Step Optimization

Maximize S/N ratio Tune to target beta

81.5

Step 3: Optimization Plots

45

6

Step 3 : Conduct / Analyze Experiments

2

1

24


Step 3: Control Factor Classification

Affects Signal / Noise

A

A

B

C

Yes NoN

oY

es

Aff

ec

ts

Re

sp

on

se

45

6


2

1

25


S/N

Rat

ioSe

nsit

ivit

yStep 3: Factorial Effects Plot

456


21

AugerPitch

1 2 3

1 2 3

TuningFactor

(Type B)

AugerDiameter

1 2 3

1 2 3

(Type A)

AugerOpening

1 2 3

1 2 3

(Type A)

1 2 3

1 2 3

BottleSpeed

(Type A)

FinType

1 2 3

1 2 3

(Type C)

26


Predicted Actual

S/N Ratio

Sensitivity

Sigma

-21.21

0.14

1.611.48

0.126

-21.40

Steps 4 & 5: Subsystem & System Verification

6 2

1

3

Step 4: Reset & Verify

Nominal Set Points

Step 5: System Latitude /Robustness Demo

System Verification :

• After verification with both prototype and production intent subsystem hardware

• Toner Dispenser integrated with total developer system for verification of developer requirements

• Total Developer system integrated in Systems Verification Test for analysis of Image Quality Responses

PreviousCondition

-25.36

0.13

2.41

Gain=4.15 dB

27


45 3

2

1Step 6: Tolerance Design/Analysis

Tolerance Design

• Trade-offs are made between reduction in quality loss due to performance variation and increase in manufacturing cost (selective reduction of tolerances, selective specification of higher grade material/components)

• Performed only after signal to noise is maximized in parameter design

• Sensitivity analysis and economic considerations are used to select the correct tolerances for drawings

Step 6: Tolerance Design / Analysis

28


• The Robustness of Function ( Dynamic Signal to Noise ) is used to achieve Technology Readiness.

• Dynamic Signal to Noise will enable low cost Reusable and Reproducible Technologies for both design and manufacturing.

• Ideal Quality equals Target Performance even in the presence of noise, throughout design life.

• Robust Design is the process of developing and improving the design latitude.

• Parameter Design minimizes the sensitivity to noises without eliminating the causes of the noise.

• Signal to Noise is a proactive improvement metric.

• Robust Design enables the prevention of problems. Problem solving minimized.

Key Messages

29


Putting It All Together

1. Start with better performance

2. Steepen the slope of the growth curve (Problem Prevention)

• Parameter Design w/Verification• Dynamic S/N• Target Performance

PerformanceGrowth Curve Impact

TimeTo

Market

Goal

Current

Key QualityIndicators

LaunchQuality

TTMBench-mark

TTMGoal

TTMCurrent

Engineering Excellence Practices and Methods, in support of the Time-To-MarketProcess, enable establishing new benchmarks in schedule and/or quality.

30


Robust Design – Selected Bibliography

Books

Camp, Robert. Benchmarking: The Search for Industry Best Practices that Lead to Superior Performance. Milwaukee, Wis.: ASQC Quality Press, 1987

Clausing, D. Total Quality Development, ASME, 1993

Phadke, Madhar. Quality Engineering Using Robust Design. Englewood Cliffs, NJ: Prentiss Hall, 1989

Mori, Teruo. Taguchi Techniques for Image and Pattern Developing Technology

Mori, Teruo. The New Experimental Design, Taguchi’s approach to Quality Engineering

Stein, P. Measurement System Engineering

Suh, N. Principles of Design

Taguchi, Genichi. System of Experimental Design: Engineering Methods to Optimize Quality and Minimize Costs. Dearborn, Mi: Unipub Kraus International Publications, American Suppliers Institute, 1987.

Taguchi, Genichi. Quality Engineering Series Volumes 1 - 7, Japanese Standards Association (JSA), 1994.

Taguchi. Robust Technology Development, ASME Press, 1993.

Taguchi, Elsayed, Hsiang. Quality Engineering in Production Systems, 1989.

Articles

ASI Symposium Proceedings, ITT Symposium Proceedings, Xerox Symposium Proceedings

Taguchi Center at Xerox Corporation: Taguchi Quality Engineering System for Robust Design. (MIT Videotape Series)

Engineering Excellence Institute (EEI)

31


Reference Slides

Robust Design / Reference

32



Flexible Designs with Adjustment Lowest Cost Tolerances Applied Measurement System Capability Decompose into Subsystems Determine benchmark system


Identify Main Function Identify Side Effects and Failure Modes (FMEA) Identify Noise Factors for Testing Identify the Response(s) and Function that is to be Optimized List Control Factors and Levels Identify Appropriate S / N Ratio and Ideal Function Run Current Case & Determine Benchmark Gap Estimate Experiment Time and Cost

Noise conditions repeated or stresses increased to validate previous results Critical parameter nominal values/latitudes verified Tuning factors verified for system integration test/model verification Early Detection of Potential Downstream Problems

Comparison to benchmark completed System latitude demonstrated and/or shortfalls identified System integration tradeoffs identified System power reduction captured

Cost/Quality Analysis to close gaps in identified shortfalls conducted

Confirmation experiment conducted Process optimization confirmed Cpk / latitude ratio

tracking formalized On-line Q.C. feedback/forward process elements

identified

Step 3 : Conduct / Analyze Experiments Conduct peer reviews during experiments Have checkpoints and backups ready Identify response tuning factors and model sensitivities for simulation Summarize optimum parameter levels and tradeoffs where necessary Identify S/N ratio improvement Document lessons learned

Step 4: Reset & Verify Nominal Set Points

Step 5: System Latitude / Robustness Demo

Step 6: Tolerance Design/Analysis

Parameter Design Checklist (refer to slide 19)

Control Factor Best Nominal Values Noise Analysis


Signal FactorAuger Speed

Dispense Rate (Gm/Min)

Function: Replenisher Dispensing

Ideal Function: Dispensetoner consistent and predictable rate.

C/F Best Nom. Values Noise Analysis


Signal FactorCarrier added

Sump Mass & Charge

Main Function: TrickleCharging of Developer

Ideal Function: Trickleout equals carrier added

Additional Analysis

Additional Analysis

AA

BCR

espo

nse

No

Y

es

Signal/ NoiseYes No

(BO

TT

LE

)

HJD Robustness LRB level @ April ‘96Sheet #1 Updates 4/96 J. Lioy

Dis

pens

e G

PM

Auger RPM

S / N ~ Beta2/ Sigma2

Tri

ckle

(gm

s)

Carrier Added (gms )

Qualification Method Used

Spiral Pitch(1)

Spiral Depth(2)

diameter (6)

(CA

P) # of fins (3)

fin type(4)

fin length (10)

Bottle speed ((9)

pitch(5)

pitch(5)

diameter (6)

(A

UG

ER

S)

LO

WE

R-U

PP

ER

toner flow (1)4 -26 HF#

tilt angle (2)+ \ - 2deg. Dev. Housing

tilt angle**+ / - 2 Deg. (2)

dispenser (3)tolerances#1 vs.#2

C/F# LEVEL TYPE C/F# LEVEL TYPE1) 40MM (A) 6) 9MM (A)2) 4 MM (A) 7) 12MM (A)3) 4 (A) 8) 9MM (A)4) Straight (C) 9) 5RPM (A)5) 10.5MM (B) 10) 40MM (A)

#1- 18GPM Max.#2 & #3 < 1GPM

Bottle seal - Vacuumrequired added valve

Primary input Primary Response

P/C calls for toner

Primary Response

overflow location (1)

overflowheight (2)

Mix Auger Type (3)

Mix Auger Speed (4 )

Developerdispense rate*2-10 GPM (1)

Toner Concentration1-4 % (3)***

* Replenished dispense rate for 6%A/C at 40PPM to 25%A/C at 65PPM with a 3:1 Ratio of toner to carrier by weight.

C/F# LEVEL TYPE.1) outboard (A)2) 14 MM (B) 3)A 18 MM Psed. (A)4) 400 RPM (A)** Aug to wall< 2.5MM

** Added Auger to wall dist. (C/F )*** Toner flow replaced T.C. noise3)A Change from 12H to 18P mixing rgf-4/96-hjdnid2

S / N ~ Beta2/ Sigma2

Control Factor Best Nominal Values

Noise Analysis


Signal FactorToner Gms. Added

Mass @ T.C.% & Q/M

Main Function: Developer Mixing (Add and Cross)

Ideal Function: Grams added Mixed and Charged

Noise Analysis


Signal FactorTrim Bar Gap

Dev. Mass on roll

Main Function: Mag RollLoading

Ideal Function: Mass on Roll uniform &controlled

Additional Analysis

Additional Analysis

AA

BCR

espo

nse

No

Y

es

Signal/NoiseYes No

Type (1)

Cutout (2)

Ton

er in

dev

Toner gms. added

S / N ~Beta2 Sigma2

Mas

s on

Rol

l

Trim Bar Gap


Primary ResponsePrimary Input

Sump Mass level

Primary Response

Speed (7)

Type-Hel. 12

Toner Entry Pt. (8)

Toner Sensing Type (9)

C/F# LEVEL TYPE C/F# LEVEL TYPE1) 18MM -Ps. (A) 6) 500rpm (A)2) with (A) 7) 400 rpm (A)3) 900 gms (A) 8) Front * (A)4)A 50 IPS (A) 9)B Long Snout (A)5) 0.075” (A)

AU

GE

RS

Rel

oad

Mix

ing

Speed (6)

Sump Mass (3)

Trim Gap (5)

Mag Roll Spd. (4)

Dev. Tilt Angle+ / - 2 Deg. (1)

Dev. Asub T<60 to >100 (4)

Toner add rate 3 -12 GPM (2)

Toner flow rate 4 -12 HF# (3)

Tilt angle #1***Toner flow #2Toner Disp. Rate #3

* #8 to Normal 9)B Removed tc sensor*** Auger to wall4)A F1@ 40, F2 @56 IPSTuning for power &PQ

Mag/Donor spacing (2)

Sump Mass (3)

TBG (5)

Donor Roll Spd. (6)

Mag roll spd. (7)

Auger spd (1)

Mag angle (4)

Mag field strn.(8)

Dev. Tilt Angle+ / - 2 Deg. (1)

Toner flow rate 4 -12 HF# (2)

Toner Conc. % 1-4 % (3)

Position on Rollin./ out. / ctr (4)2 reads per position


C/F# LEVEL TYPE C/F# LEVEL TYPE1) 500rpm (C) 5) 0.075” (B)2)A 0.045” (C) 6)D 15 IPS (A)3)B 1000 gms (B) 7)F 50 IPS (A)4)c +10 deg. (A) 8) Norm Lvl. (C)

rgf-4/96- hjdnid2

Position #1noiseOthers minimal

2)A - 0.05” Rollback3)B - 900 gms Mixing 4)c -0 deg. Auger Mks6)D- F1@ 13.6, [email protected] IPS for PQ tune.7) -F1@ 40, F2 @56 IPSTune Power& PQ

HJD Robustness LRB level @ April ‘96Sheet #2 Update 4/96 J. Lioy S / N ~Beta2 Sigma2

Noise Analysis


Signal FactorsVdm D.C. Bias

Main Function: Donor Roll Loading

Ideal Function: ConstantMass, Charge, and Size

Noise Analysis


Signal FactorsVjump A.C. - P-P

Main Function: Cloud Generation

Ideal Function: Cloud of uniform mass and charge

Additional Analysis

Additional Analysis

AA

BCR

espo

nse

No

Y

es

Signal/NoiseYes No

AC P-P (5)

Dev

. Mas

s

Vdm Bias (volts)

S / N ~Beta2 / Sigma2

Clo

ud

Den

sity

V jump A.C.


Primary Response Primary Response

Mag Roll Mass Donor roll tonerMass, Charge ,Size

Primary Input

Toner Cloud in Gap

AC freq. (6)

AC P-P (7)

AC freq. (8)

Toner properties

Donor Roll Props.

Don

orto

P/R

Don

or t

o M

ag.

Bia

s V

olta

ges

Don

or t

o M

ag.

AC P-P (1)

AC freq. (2)

Donor RollTime cons.(3)

Mag ang. (4)

Donor spd (5)

Mag. field (6)

Donor /mag spacing (7)

Mass on roll (8)

Cycle number1st - 10th - (1)

Dev. Conduct108 - 1011 - (2)

Toner Flow4-12 HF # (3)

Control Factor Best Nominal Values C/F# LEVEL TYPE C/F# LEVEL TYPE1) 200V (C) 5)c 14 IPS (A)2)A 500Hz (A) 6) Normal (A)3 B < 2000uS ( C) 7) 0.04”-0.06” (B)4) 0 - 5 deg (B) 8) 20+ (A)

Cycle number -Large

2)A- 3.25 KHz same as main P/S frequency3 B-Remains undefinedfor function as well asmeasurement changes.5)c- F1@ 13.6, [email protected] IPS for PQ tune


C/F# LEVEL TYPE C/F# LEVEL TYPE5) 3KV (A)6) 3Khz (A) 7) 200 V (A)8) 9Khz (A) All @ 0.014” GAP to P/R rgf-4/96- hjdnid2

Toner properties

Cyclic State

Environmental

HJD Robustness LRB level @ April ‘96Sheet #3 Update 4/96 J. Lioy S / N ~Beta2 / Sigma2

Noise Analysis


Signal FactorsNeeds Discussion

Primary Response

Main Function: Non - Interactive Development

Ideal Function: P/R Mass not reflected back to theother color donor rolls

Noise Analysis


Signal FactorsVdev = Vpr- Vdonor

Primary Response

Main Function: Photoreceptor Development

Ideal Function: Reproduce image to match input signal

Additional Analysis

Additional Analysis

AA

BCR

espo

nse

No

Y

es

Signal/NoiseYes No

Donor Roll Spd. (3)

Donor to P/R Gap (1)

P/R

Mas

s

Signal

S / N ~ Beta2 / Sigma2

Res

pons

e

Signal

S / N ~ Beta2 / Sigma2Qualification Methods Used

Primary Input

Toner Cloud Image Metrics for Lines , solids , halftones

P/R Mass Developed

Toner properties

Donor Roll Props.

Jumping Biases

Jumping Electrodes

Cyclic State

Environmental

Toner properties

Donor to Mag Gap (2)

Mag Roll Spd. (4)

Cloud GenerationFactors 5,6,7,8

Toner Cons.& Tribo (AsubT) (1)Toner Flow 4 - 12 HT # (3)Toner CohesionNumber (4)

Toner Residence Time (5) Print Number and Area Coverage Combined

Control Factor Best Nominal ValuesControl Factor Best Nominal Values C/F# LEVEL TYPE C/F# LEVEL TYPE

C/F# LEVEL TYPE C/F# LEVEL TYPE1)A 0.014” (A) 5)E 3KV (A)2)B 0.045” (A) 6)F 3KHz (A)3)C 16 IPS ( A) 7) 200 V (A)4)D 50 IPS (A) 8)G 9 K (A)

rgf-4/96- hjdnid1

Residence time key noise

1)A- 0.012” -PQ & Power 2)B- 0.045” Rollback 3)C-F1 / F2 Speed Tuning 4)D-F1 / F2 Speed Tuning 5)E - 2.2-2.4 KV P/S arcing 6)F - 3.25 KHz PQ Tuning 8)G - 3.25 KHz P/S Cost

HJD Robustness LRB level @ April ‘96Sheet #4 Update 4/96 J. Lioy

37


Developer Housing Critical Parameter Diagram

S. Mordenga / R Faull April96

OTHER DESIGN CONTROL PARAMETERS

MAG ROLL TO DONOR ROLL SPEED RATIO - 3:1 (WITH)

DONOR ROLL TO P/R SPEED RATIO - 1.6 :1 (AGAINST)

Vdm ADJUSTABLE FOR DEVELOPMENT CONTROL (100+VLTS)

AC JUMPING FIELD < 4.5 VOLTS / MICRON AT 3.25 KHZ.

Donor Roll

Pickup Auger

Speed @ 500

Mixing Auger

Speed @ 400

P/R DIRECTION IS UPWARD OUT OF THE PAGE

Auger to wall clearance < 2.00 MM

GROUND

Photoreceptor

FLOW

Vd AC

Vm AC

Vdm

Vdb

MATERIAL

Mag Roll

Replenisher

entry point

Trickle

port

Mag field strength and

position

Donor roll

time constant

Vexp


38


Critical Parameter Drawing for F2/F2 Replenisher Dispenser

S. Mordenga/ R Faull April'96

DEVELOPER HOUSING

BOTTLE SPEED

SET @ 5 RPM

PADDLE WHEEL SECTION

BOTTLE CRADLE

FRICTIONAL FORCE

SUMP

UPPER AUGER

SPEED @120RPM

LOWER AUGER

SPEED @100RPM

AGITATOR BAR

SPEED @24 RPM

OTHER DESIGN CONTROL PARAMETERSBOTTLE FEED RATE TO UPPER AUGER - 4 GRAMS PER REVOLUTIONUPPER AUGER FEED RATE TO LOWER AUGER - 0.16 GRAMS PER REV.LOWER AUGER FEED RATE TO DEVELOPER HOUSING - .2-.23 GR. / REV.VACUUM IN BOTTLE WHILE RUNNING AT FULL RATE < TBD PSI

TONER DISPENSER OUTPUT SPEC. FOR F1/F2 IS 21.4 GRAMS PER MINUTE


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