THE BUSINESS CASE FOR IMPLEMENTING MACHINE VISION

THE BUSINESS CASE FOR IMPLEMENTING MACHINE

VISION

Vision Systems International

Established in 1984 Consultancy concentrating on machine vision Services include:

Training Application related:

Application engineering Specification writing Vendor identification/evaluation

Market related Market research

strategic development and planning partnering activities market analysis/competitive analysis due diligence

Technology transfer

Introduction

Electronic Imaging Where is Machine Vision Used Why Machine Vision Now Machine Vision Industry/Market Compared to Human Vision Why Consider Machine Vision Applications Systematic Deployment What is Machine Vision

Electronic Imaging vs.. Machine Vision Computers generating images

CAD Animation Scientific Visualization GIS

Computers operating on acquired images - Computer vision Security/surveillance Security/baggage handling Retail security Biometric/access control ATMs/OCR/security ITA/IVHS Biomedical/scientific/microscope Radiology - CAT/MRI/PET Automotive - autonomous vehicles Automotive aftermarket 2D symbology/bar code Document/form reading/OCR Machine Vision

Where is Machine Vision Being Used

Machine Vision is in use in virtually all manufacturing industries

In some industries one can no longer produce without machine vision

CHART 2 NORTH AMERICAN MERCHANT MACHINE VISION MARKET UNITS

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

CHART 7 NORTH AMERICAN MACHINE VISION MARKET DISTRIBUTION BY MAJOR END USER INDUSTRIES - UNITS

SEMICON ELECTRONIC CONTAINER FOOD WOOD TRANSPORT FAB METAL PLASTIC PRINTING PHARM + MED

Why Machine Vision Now

Technology Readiness

Underlying technology for machine vision has evolved Components developed with features required to

succeed in machine vision applications Lighting - LED - stable, long life Cameras - solid state, progressive scan, asynchronous

scan, exposure control, color, high resolution Optics - telecentric, computer controlled zoom Compute power - PCs, DSPs, etc. Software - GUI - Windows - Standard PCI Interface, IEEE 1394

Technology Pull

Quality emphasis (ISO 9000, 6 sigma, etc.)

Productivity gains sought/downsizing - eliminates eyes/requires substitute sensing

Government regulations

Machine Vision Industry/Market

Not homogenous Segmented

supply side GPMV/IPBS ASMV VAR

demand side by industry

process end package end

applications that cut across industries e.g. web scanners

GPMV Application Specific Modules PRINTING: INSPECTION, REGISTRATION CONTROL, COLOR CONTROL PHARMACEUTICAL: BLISTER PACK, VIAL/AMPULE, SOLID DOSAGES, OCR/OCV WELDING WEB PRODUCT OFF-LINE GAUGING MECH ASSY VERIFY CONSUMER PKG INSP FILLED CONTAINER: METAL, PLASTIC, GLASS, CLOSURES 2D LOCATION ANALYSIS ELEC. PKG. INSP: INSPECTION, QUALITY OF MARKINGS, CO-PLANARITY, BALL GRID ARRAY,

OCR/OCV ELECTRICAL/ELECTRONIC CONN OCR/OCV 1D BAR CODES/2D BAR CODES/SYMBOLOGY EMPTY CAVITY INSPECTION COMPACT DISC APPLICATIONS CRT ELECTRONIC DISPLAYS DATA STORAGE

Container Market

Glass glassware manufacturer filler

Can Plastic Closure For glass and can in late majority phase; for plastic in

early adopter phase; for closure in early majority phase

Pharmaceutical Market

Process end vials, filled/unfilled solid dosages

Packaging end label issues

In process end in early adopter phase; in packaging in early/late majority phase

Compared to Human Vision

Machine vision does not compare well! We use 1011 neurons to perform about 1015

operations per second 2 billion years of evolutionary

programming

So Why Machine Vision?

Humans only 70 - 85% effective!

People

Attention span/distractions Eye response Relative gauging Availability (breaks, vacations, sick, etc.) Consistency

individual between individuals from day-to-day

People

Overload Boring Detect anomalies Adapt/make adjustments Interpret true nature of condition

Machine Vision vs. People

Speed Accuracy Repeatability

Production Errors

System Random

Machine Vision vs. Human Vision

Machine vision: best for quantitative measurement of structured scene

Human vision: best for qualitative interpretation of complex unstructured scene

Why machine vision works

Because variables can be controlled parts can be presented consistently scene can be constrained

MACHINE VISION

Technology to improve quality reduce scrap/rework reduce cost improve productivity improve product reliability increase customer satisfaction increase market share

Why Consider Machine Vision

Technology to lower inventories avoid equipment breakdowns eliminate adding value to scrap avoid inspection bottlenecks yield

consistent and predictable quality

Machine Vision Applications

Throughout a manufacturing facility incoming receivingforming operationsassembly operationstestpackaging operationswarehousingetc.

Generic Applications

Inspection 2D, 3D Metrology surface flaw/cosmetic analysis mechanical/electronic assembly verification

location analysis visual servoing (2D and 3D) robot guidance

pattern recognition character recognition part recognition 2D symbol reading

Systematic Deployment

Success Requires

Senior management must foster atmosphere to encourage change support change agents demonstrate buy-in to change encourage plant and line to take

ownership establish realistic schedule for changes

Success is more likely

People assigned are interested in new techniques and welcome change

begin with easy, non-critical application define the parameters of the project and avoid

creeping expectations select applications not critical to labor issues be supportive during learning process plan for replications

Success is more likely

Obtain people involvement Avoid technology leap that is too far Make certain project is part of an overall

Implementation Process

Assemble task force and study production process

task force should develop understanding of what machine vision is

define need and evaluate alternatives investigate - select specific applications assess technical feasibility and cost feasibility write comprehensive specification

Install and run-in. Conduct acceptance test Provide shop floor support Evaluate system’s performance against

goals Look for another machine vision

opportunity

Solicit 4 - 6 vendors with appropriate expertise Visit vendors to review proposals, policies,

expertise, QC procedures Systematically select vendor Purchase Acceptance test at vendor Train all personnel Involved

What is Machine Vision?

As defined by the AIA:

A system capable of acquiring one or more images, using an optical non-contact sensing device, capable of processing, analyzing and measuring various characteristics so decisions can be made.

Relevance of Pixels

Pixels

512 X 512 1/4M

1300 X 1200 1.4M

AP Wire Photo 2.5M

35 mm color film 20.0M

Steps to Take When Buying a Machine Vision System

Identifying Machine Vision Opportunities Assess Application Feasibility Understand the Application Understand the Vendors Responsive Proposals Systematic Buy-off Procedure Mistakes in Buying Automation

Project Justification

Identifying Machine Vision Opportunities

Quality concerns Productivity/mechanization Process control Rework Inventory build-up - inspection bottleneck Equipment jams Warranty issues - field returns Employee turnover

Identifying Machine Vision Opportunities

Lowest value added Expensive fixturing Lengthy set up times 100% inspection required to sort bad parts Hazardous environment Contaminants Capital expansion Operator limitations

Profile of Good Machine Vision Opportunity

Perceived value Cost justifiable Recurring concern Can do something about it Straight forward Technically feasible

Profile of Good Machine Vision Opportunity

User friendly potential Dedicated line Long line life Operation champion Management commitment

Global Competition Requires

Higher manufacturing productivity Increased demand Higher product quality Better customer service Flexible manufacturing Greater return on manufacturing assets Changing standards of manufacturing

performance

Computer Aided Inspection

Provides traceability - records Statistical data base - isolate production

problems Real time machine correction/adaptive

control Automatic QC data collection and analysis Remove drudgery of humans

Hidden Costs Machine Vision Can Help

Lost business because product not produced on time

Shipment of wrong products Excess inventory Idle labor because parts are not available Doing a job over Loss of valuable information

Machine Vision and Factory Automation

Data driven automation Machine vision = data !

Statistics

Measurements Parts recognized Classification Types of defects Trend analysis Performance assessment Record keeping Process Control

Successful Application Requires

Comprehensive understanding of needs Proper application process Good equipment and performance

specifications Comprehensive understanding of machine

vision system capability

Steps to Take When Buying a Machine Vision

Machine Vision is in Widespread Use Best Justification is Process Control Infrastructure Resources: AIA and MVA

How To Select Machine Vision Equipment

Understand the technology Assess application feasibility Understand the application Understand the vendors Responsive proposals Systematic buy-off procedure Applications in pharmaceuticals

Understand the technology

Become Informed Conferences Books Bibliography

Assess Application Feasibility

Assess Feasibility Basis rests with size of a pixel/FOV

MVA slide rule Typical system handles 500 pixels Function of generic application:

verification gauging part location flaw detection OCR/OCV/pattern recognition

Verification

Function of contrast - real or artificial high contrast - feature should cover

3 X 3 pixel area low contrast - feature should cover more

pixels

Gauging

500 marks on a ruler = resolution subpixel interpolation - factor of 4 to 10 requirements driven by tolerance rules of thumb:

repeatability: 1/10th of tolerance accuracy: 1/10th to 1/20th of tolerance sum of accuracy + repeatability = 1/3

tolerance

Gauging

Discrimination - smallest change in dimension detectable with measuring instrument

Discrimination = sub-pixel resolution Repeatability = +/- Discrimination Accuracy - determined by measurement of

calibration standard = Discrimination

Gauging - Example

2” part and 2” FOV tolerance: +/- 0.005”, total range 0.010” repeatability: 1/10 X 0.010” or 0.001” discrimination/accuracy: 1/20 X 0.010” or

0.0005” with 500 X 500 pixel camera, resolution = 0.004” with sub-pixel resolution 1/10, discrimination =

0.0004” = accuracy, so repeatability is = 0.0008”

Part Location

Analogous to gauging Can expect to achieve sub-pixel

resolution: repeatability and accuracy

Flaw Detection

Contrast! Contrast! Contrast! Detection Vs. Classification Detection: High Contrast, normalized

background (no pattern), can detect a flaw that covers 3 X 3 pixels

Classification: flaw should cover 25 X 25 pixels

OCR/OCV

Stroke width - 3 pixels wide Character should cover 25 X 25 pixels Spacing between characters - 2 pixels Single font style - bold Result - 99.9% read rate effectiveness

Linear Array Image Capture

2000 - 8000 pixels Scanning rates up to 2 - 20 KHz Speed should be well regulated Resolution in direction of travel function of

speed and sampling rate of camera

Understand the Application

General

Defect prevention is better than the cure! Study application site personally! Consider vision to enhance people! Expect productivity to decline!

application issues: generic application variables: part, presentation, etc. material handling operator interface machine interfaces environmental issues system reliability/availability miscellaneous: documentation, warranty, training,

software, spares, service acceptance test/buy off procedure responsibilities

Job descriptions Present specifications Part drawings Floor space drawings Samples Photos/videos Personnel

Write functional specification Use “Machine Vision Requirements

Checklist” - available from MVA - forces examination of: production process justification issues application issues

System Spec

Defines “what” system is and “how” system will work

involves examination of implementation details programming standards style control methods

System Specification

The spec is not what the customer wants! Creeping expectations! Variables - Gotchas!

System Specification

Adhere to factory standards Adhere to engineering standards Use conventional jargon for part

descriptions and to describe the process Use existing frames of reference to

develop acceptance test

Before RFP

Prepare preliminary conceptual design Develop schedule - be realistic Assess cost Determine technical and cost feasibility

Developing Functional Requirements

What does the system do? What specific function do you want the MV

value adder to do? What goals do you expect to achieve with

MV? Will the MV system be for a retrofit or next

generation product?

Defines “what” system is and “how” system will work

involves examination of implementation details programming standards style control methods

Does the application involve: One object at a time Multiple objects

How many different objects What are the part numbers?

Is it a batch operation or continuous dedicated process?

What are the changeover times and frequency of changeovers?

What are the skill levels involved in changeover?

How is function currently being performed?

Can new variations to the part be expected? What might they be?

Where do parts come from? What is material handling surrounding MV?

Can rejected parts be repaired? Where do pass and fail objects go? When does the project have to be

completed? How many shifts is the equipment used? If machine vision fails, what is the option?

How many MV systems will be required annually?

What are the consequences of a failed MV sequence?

What are the consequences of a false reject?

Describe the application Generically, does the application involve

Gauging Assembly verification Flaw inspection Pattern recognition

If Gauging What are the tightest tolerances? What is the accuracy design goal? What is the repeatability design goal? Are there reference features? What are calibration requirements?

If assembly verification Dimensions of assembly Is it presence/absence Orientation verification What is the smallest piece to be verified and

dimensions of that piece? Is part correctness also required?

If flaw inspection Describe flaw types What is the smallest size flaw? Does the flaw affect surface geometry? Does the flaw affect surface reflectance? Is it more of a stain? Is classification of flaws required?

If location analysis What is the design goal for accuracy?

For repeatability? What is the area over which the “find” is

required? Will angular as well as translation correction

be required? Will scale change? Describe calibration requirements

If pattern recognition What is the size of the pattern? Describe difference between patterns? Is there a background pattern? Does pattern involve color? Geometry? Number of different patterns? Is objective to identify? To sort?

If specifically OCR/OCV Fixed font? Variable font? What is font? What is the height of the characters? What is the stroke width? What is spacing between and around characters? How many characters in string? How many lines?

Color of print? Describe background – color, “busyness”

Object dealing with What is material? What is finish (texture) like? Dull, glossy,

specular? Is surface finish the same on all surfaces?

For all part numbers? Production runs? Any platings, coatings, films, paints? Markings?

Object dealing with – Shapes – flat, curved, gently curved, other?

Irregular, grooved, sharp radii, mixed geometric properties?

Part orientation variation? Part sizes? Part colors? (hue, saturation, brightness) Part temperature?

Object dealing with – Possibility of warping, shrinking, bending,

etc? Any change in appearance over time? Any markings? General appearance variables? Sensitivity to light?

Material handling Present handling or being considered? Production rates? Currently? Future? Parts static? Moving continuously? Speed? If indexed

How long stationary? Total in-dwell-out time? Settling time? Acceleration?

Material handling Maximum positional variations – translation,

rotation? More than one stable state? Volume envelope for MV? Any restrictions or obstructions? What triggers action? What is result of MV?

Operator interface Operators themselves (education, familiarity

with machinery, electronics, computers, etc.) Operator interface requirements? Personnel access requirements? Enclosure requirements? Object display requirements? Image condition storage requirements?

Operator interface Fail-safe operation? Program storage requirements? Data storage requirements? Power failure requirements? Reporting requirements? False reject and escape rates?

Machine interfaces Alarms desired? Other machine integration? What event triggers MV action? How

detected? How communicated to MV? Machine interfaces: part in position, sensor

type, PLC, Ethernet, etc. Hierarchical interfaces anticipated?

Environmental issues Factory – clean room? Air quality? Corrosive? Ambient lighting? Part conditions? Wash-down? Temperature? Humidity? Radiation? Shock &

Vibration? Utilities available: power, air, water, vacuum?

System availability/reliability Number of hours per week? Hours available

for maintenance? Calibration procedures? Challenge procedures? MTBF? MTTR?

Other issues Special paint? Installation? Warranty? Spare parts? Documentation? Training? Software ownership?

Questions?

Good RFP

Describes project in detail Describes operation’s business Reviews why the project is being solicited Reviews schedule

RFP Should Request

Schedule Training Service Warranty Software ownership Documentation Installation support

Identifying Vendors AIA - Directory MVA - Directory Opto*Sense database

Vendor type: image processing board general purpose machine vision system application specific machine vision system system integrator

Understand the Vendors

Machine Vision Industry

Image Processing Board Suppliers General Purpose Machine Vision suppliers Machine Vision Software Suppliers Smart Cameras Suppliers Application Specific Machine Vision Suppliers System Integrators OEM

System Integrator

Look for application competency industry competency technological competency professional competency technology independence schedule/cost

System Integrator

Questions to ask: Have you done anything like this before? What do other clients think of you? Do you understand my requirements? Are your skills consistent with my

requirements?

Need a Consultant?

Time an issue and corporate resources are lean

Consultant can: write specifications write bid package identify vendors evaluate proposals prepare acceptance test plans

Need a Consultant?

Consultants conserve resources bring technology knowledge bring vendor knowledge bring objective counsel bring negotiating prowess

Evaluate vendors systematically Use Decision Matrix technique to assess proposals Visit the 2 - 3 “best” vendors to assess:

application engineering skills quality control procedures software practices training materials documentation policies references

Responsive Proposals

Proposals Should Include

Review of implications of variables: staging image processing image analysis

Implication of organization/lack of organization of parts

Time budget to demonstrate confidence throughput can be met

Position/temperature error budgets Interfacing issues:

people machine/line

Miscellaneous issues: enclosures start up/changeover battery back up maintenance diagnostics calibration reports

Exceptions to the spec Responsibilities:

installation specifically what is required for system to be

successful Acceptance testing

validation procedure challenge set

Policy review training installation warranty field service spares software upgrades documentation

Schedule Cost

Proposals Should Reflect

Familiarity with processes Grasp of problem Completeness and thoroughness Responsiveness Evidence of good organization and

management practices Qualifications of personnel

Proposals Should Reflect

Experience in similar or related field or application

Record of past performance Project planning Technical data and documentation Geographic location

Proposer Evaluations

Assess staying power Technical resources Design philosophy Capital/human resources Physical facilities Documentation Policies

Proposer Evaluations

Schedules References Quality control practices Vendor skills:

optics TV Mechanical engineering Quality engineering

Sizing up Vendors - Differentiators

How long before a service call is made or phone support is obtained

Are software upgrades included in the price? Is upgrade notification automatic?

What is the company’s annual sales revenue in the specific product/application

Finalize Evaluations

Make sure vendor understands Visit most responsive vendors Visit up and running installations

Vendor Decision

Previous work Quality of work Reputation Ability to meet schedule Understanding of your business and

application

Reference Checks

Quality of work Ability to meet schedule Policies Support Would they do it over !!!

Systems

No system should be more complicated than it need to be!

Good application engineering is critical! Contrast, Contrast, Contrast! Staging is important, if not more important

than image processing algorithms!

Customer

Software and hardware should be transparent! Tinkering should be discouraged! Should not specify equipment, rather function! Samples furnished should be representative of

all variables expected! Training is critical! A little knowledge is dangerous!

Vision Company

Room lighting is a No - No! Vision company should have all disciplines

required! Beware of “Piece of cake!” Look for relevant experience! Verify quality practices! Verify policies: training, documentation,

Systematic Buy-off Procedure

Application Engineering

Material handling Must avoid jamming regardless of deformities! Murphy’s Law - If it can go wrong, it will!

Lighting Lighting is not a constant! Never use software to compensate for poor

lighting! Shrouds are cheaper than software fixes!

Application Engineering

Optics There are limits to resolution! Nothing exceeds the speed of light!

Image resolution Nyquist’s theorem does apply! More resolution means more compute power! A pixel is not a fixed size! - Magnification

issues

Write an acceptance test plan/buy-off procedure

Different for: attribute inspection system - based on Thorndyke

Chart to arrive at sample size - to test for both escapes and false rejects

gauging/location analysis - repeatability/accuracy performance at upper limit, nominal and lower limit of tolerance

Acceptance Testing

Includes evaluation of operator interface basic operation calibration accuracy & repeatability throughput sensitivity maintainability availability

Acceptance Testing

Test at system level Test at other than nominal Test failure modes Test everything in system spec Don’t put anything into spec that can not

be tested!!!

Buy-off at Supplier

Simulate external equipment Generate reports Run through all screen functions Simulate alarms and failure modes Power up/down system and components

Using the Thorndyke chart e.g.. for 0 defects 95% confidence 400 PPM (reliability)

from chart np - 3.0 n = 3/400 x 10 -6

n = 7,500 for every factor: color, finish, size, etc.

Create a challenge to verify performance

Working With The System In The Factory

Should not deteriorate production speed! Ideally, avoid having to re-engineer the

manufacturing process to accommodate machine vision!

System should have the capacity to be reconfigured!

Training

Basic principles of operation Normal operating procedures

screen functions power up/down reports

Alarm conditions and recovery procedures

Training

Back-up procedures Normal and emergency maintenance Calibration

Mistakes in Buying Automation

1. No equipment specification

2. Requesting quotes before visiting prospective suppliers

3. Incorrect cost estimate

4. Insufficient in-house machine support

5. No input from production people

Mistakes in Buying Automation

6. Poor communication with vendor

7. Acceptance of inadequate equipment

8. Failure to supply latest drawings and parts with specifications

9. Failure to design for automation

10. Using the wrong technologyper E. Martin, Lanco/NuTec, Assembly March 96

Reasons Why Automation Fails

Per Automation Research Corp. Study Unclear or false expectations regarding

what is to take place and the results that are to be achieved

Lack of commitment by user management Over dependence on technical solutions

Reasons Why Automation Fails

Lack of acceptance by the user organization

Poor project management Not properly taking into account the

human resources issues

SI Difficulties With Users

Inadequate specifications Lack of technical knowledge No management commitment Internal policies Separating needs from wants Inability to take over system Changes in midstream

SI Difficulties With Users

No one person in charge Tight project constraints Lack of communication Price constraints Inability to take risks Manpower shortages Rigid specifications

Benefits of Machine Vision

Scrap reduction Scrap disposal costs Rework Inventory reduction associated with rework Avoiding value added Improving machine uptime - capital productivity Avoiding return and warranty costs Improving customer satisfaction

Tangible benefits: increase productivity reduce scrap reduce rework time/inventory avoid adding value to scrap avoid product returns - warranty issues avoid liability issues avoid field service

Tangible benefits: avoid freight costs on returns avoid equipment breakdowns/improve

machine uptime improve product fabrication cycle and impact

on inventory save indirect labor cost save floor space to store rework inventory

Tangible benefits: training/labor/turnover/recruiting costs out of cycle costs due to schedule upsets waste disposal costs costs of overruns to compensate for yield personnel/payroll costs per employee:

average worker’s compensation average educational grant per employee

tooling/fixturing savings

Intangible benefits improve quality - consistency of quality predictability of quality information automation flexibility people effectiveness/limitations sample inspection only monitors system

errors, not random errors

Intangible benefits: process control environment consumer/government pressure “eyes” for automation expansion needs seasonality

Because some things appear to be intangible does not mean they have zero value !!!

In final analysis, justification of technology is a management issue - not an accounting issue !!!!

Data required: How many pieces are produced per month

per line? How many production lines make the piece? What is the current inspection time per piece?

(minutes/piece) What is the inspection labor rate? ($/hr

including benefits)

Data required: How many rejects per month (%)? What is the value of a reject - $ -? What is the value of the raw material in the

piece - $ -? What percent of the rejects are reworked per

month? What is the average rework time/piece

(minutes/piece)?

Data required: What is the monthly warranty cost - $? -

includes costs of field service, field returns, repairs, shipments to and from plant, paperwork, etc.

Product liability costs per month - $? - includes liability claims, lawyer fees, insurance, paperwork, etc.

Data required: What percent of the rejects are scrapped per

month? - the difference between the number of rejects per month and the number of rejects reworked per month and returned to inventory

What are the monthly waste disposal costs due to the scrapped pieces?

Data required: What are the scrap and rework inventory

costs per month? - eg. Calculate based on average number of units scrapped and in inventory per month multiplied by the value (cost) of the piece divided by 10 (factor that assumes any such unit will only be in inventory an average of two days)

How many shifts does the line operate?

Data required: Total hours operating per shift? Hours worked per month/shift/person? - paid

hours Number of units sold per month? Average

selling price of the piece? - not cost Indirect (supervisory) labor rate ($/hr with benefits)?

What is the profit per piece produced? ($)

Data required: Current cost of money? Prime rate + 1%? If sample inspection, hours per month for

specific piece?

Calculated values: annual direct cost of inspection per piece =

inspection labor rate X hours worked per month/shift X number of shifts line operates X 12

annual indirect cost of inspection per piece = indirect labor rate X hours worked per month/shift/person X number of shifts X 12

Calculated values: cost of rejects scrapped = percent rejects/

month X value of a reject X % pf rejects scrapped/ month X number of pieces produced/month X 12

cost of rework = percent of pieces reworked/month X number of pieces produced per month X rework time X rework labor rate X 12

Calculated values: warranty costs = monthly warranty costs X 12 liability costs = monthly product liability costs

X 12 scrap disposal costs = monthly cost X 12 scrap and rework inventory costs = monthly X

12 training costs - based on turnover experience

Assigning values: value of reliable data = sum of annual direct

and indirect labor costs X 0.05 value of improved customer satisfaction =

average selling price of the piece X number of units sold per month X 12 X 0.001

Assigning values: percent uptime line improvement anticipated -

an estimated value value due to gain in line uptime = cost of

machine vision system X number of systems required X 0.05

Costs cost of machine vision system (or systems) launch costs (training, etc.) - estimate 10% of

machine vision system costs annual service contract - estimate 10% of

machine vision system costs

Costs: opportunity cost - function of the cost of

money = cost of the machine vision system X number of systems + launch costs + annual service contract X number of systems X current cost of money

Costs: total equipment costs = cost of machine vision

systems X number of systems + launch costs + annual service contracts X number of systems + opportunity cost

average annual cost over four years = total equipment costs/4

Return on investment = (average annual savings/total equipment costs) X 100

Payback (years) = total equipment costs/(average annual savings + average annual costs with machine vision)

Average Payback Period by Company SizePer Automation Research Corp.

2.6 2.8 3 3.2 3.4

medium

Average Payback Period in Years by IndustryPer Automation Research Corp.

2.42.8

2.32.8

3.6 3.7

THE BUSINESS CASE FOR IMPLEMENTING MACHINE VISION

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