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THE BUSINESS CASE FOR IMPLEMENTING MACHINE VISION

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1 THE BUSINESS CASE FOR IMPLEMENTING MACHINE VISION

2 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

3 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

4 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

5 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

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8 Why Machine Vision Now

9 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

10 Technology Pull Quality emphasis (ISO 9000, 6 sigma, etc.)
Productivity gains sought/downsizing - eliminates eyes/requires substitute sensing Government regulations

11 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

12 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

13 Semiconductor Processing Market
Artwork Reticle/photomask inspection Unpatterned wafers - defects Patterned wafers: critical dimensions, overlay registration, defects In early majority phase but due to changes in industry also in innovator and early adopter phases

14 Semiconductor Packaging Market
Alignment OCR/OCV Lead straightness Co-planarity Package markings Package cosmetics In late majority phase

15 Electronic Market Bareboard inspection Populated PCB inspection
solder paste pre-solder post solder (optical and X-ray) In the case of bareboard systems - late majority In the case of populated PCB systems - early majority

16 Other Market Properties that Affect Adoption
Geography Segment of market: consumer electronics, telecommunications, computers, military, etc. Captive manufacturer or merchant/contract builder

17 Automotive Market 3D sheet metal assembly
spot welder fixture alignment wheel alignment paint inspection 2D - miscellaneous aftermarket head light aiming body alignment Major companies are in late majority phase; component suppliers in early majority phase

18 Wood Market Yield optimizer Grade optimizer
Late majority phase for yield optimizer; innovator phase for grade optimizer

19 Printing Market Color registration Print registration Print inspection
For first two in late majority phase; for third in early adopter phase

20 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

21 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

22 Three Dimensional Machine Vision
Leading adopters: semiconductors - co-planarity electronic - solder paste, co-planarity transportation - gap/flushness wood - scanner/optimizers misc: OCMM, CAD input, welding, etc. Total No. Am. market - $248.8M, increased 9.1%

23 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

24 Humans only 70 - 85% effective!
So Why Machine Vision? Humans only % effective!

25 People Attention span/distractions Eye response Relative gauging
Availability (breaks, vacations, sick, etc.) Consistency individual between individuals from day-to-day

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

27 Machine Vision vs. People
Speed Accuracy Repeatability

28 Production Errors System Random

29 Machine Vision vs. Human Vision
Machine vision: best for quantitative measurement of structured scene Human vision: best for qualitative interpretation of complex unstructured scene

30 Why machine vision works
Because variables can be controlled parts can be presented consistently scene can be constrained

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

32 Why Consider Machine Vision
Technology to lower inventories avoid equipment breakdowns eliminate adding value to scrap avoid inspection bottlenecks yield consistent and predictable quality

33 Machine Vision Applications
Throughout a manufacturing facility incoming receiving forming operations assembly operations test packaging operations warehousing etc.

34 Machine Vision Functions
Location inspection gauging identification recognition counting motion tracking

35 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

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58 Systematic Deployment

59 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

60 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

61 Success is more likely Obtain people involvement
Avoid technology leap that is too far Make certain project is part of an overall plan

62 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

63 Implementation Process
Install and run-in. Conduct acceptance test Provide shop floor support Evaluate system’s performance against goals Look for another machine vision opportunity

64 Implementation Process
Solicit 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

65 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.

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70 Relevance of Pixels Pixels 512 X 512 1/4M 1300 X 1200 1.4M
AP Wire Photo M 35 mm color film M

71 Steps to Take When Buying a Machine Vision System

72 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

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

74 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

75 Profile of Good Machine Vision Opportunity
Perceived value Cost justifiable Recurring concern Can do something about it Straight forward Technically feasible

76 Profile of Good Machine Vision Opportunity
User friendly potential Dedicated line Long line life Operation champion Management commitment

77 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

78 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

79 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

80 Machine Vision and Factory Automation
Data driven automation Machine vision = data !

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

82 Successful Application Requires
Comprehensive understanding of needs Proper application process Good equipment and performance specifications Comprehensive understanding of machine vision system capability

83 Steps to Take When Buying a Machine Vision
Machine Vision is in Widespread Use Best Justification is Process Control Infrastructure Resources: AIA and MVA

84 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

85 Understand the technology

86 Steps to Take When Buying a Machine Vision System
Become Informed Conferences Books Bibliography

87 Assess Application Feasibility

88 Steps to Take When Buying a Machine Vision System
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

89 Verification Function of contrast - real or artificial
high contrast - feature should cover 3 X 3 pixel area low contrast - feature should cover more pixels

90 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

91 Gauging Discrimination - smallest change in dimension detectable with measuring instrument Discrimination = sub-pixel resolution Repeatability = +/- Discrimination Accuracy - determined by measurement of calibration standard = Discrimination

92 Gauging - Example 2” part and 2” FOV
tolerance: +/ ”, total range 0.010” repeatability: 1/10 X 0.010” or 0.001” discrimination/accuracy: 1/20 X 0.010” or ” with 500 X 500 pixel camera, resolution = 0.004” with sub-pixel resolution 1/10, discrimination = ” = accuracy, so repeatability is = ”

93 Part Location Analogous to gauging
Can expect to achieve sub-pixel resolution: repeatability and accuracy

94 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

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

96 Linear Array Image Capture
pixels Scanning rates up to KHz Speed should be well regulated Resolution in direction of travel function of speed and sampling rate of camera

97 Understand the Application

98 Questions to Ask Before Proceeding From: Wayne Chaneski, Taking Steps to Improve Your Process, Modern Machine Shop, February, 2000. Can we eliminate the process? Does each step in the process add value? Is the step a duplication? Is the step a built in correction now unnecessary?

99 Questions to Ask Before Proceeding From: Wayne Chaneski, Taking Steps to Improve Your Process, Modern Machine Shop, February, 2000. Can we change the process? How can the operation be changed? Can different equipment make the operation better? Can we use less costly options? Can the operation be done faster? Can the operation frequency be reduced?

100 Questions to Ask Before Proceeding From: Wayne Chaneski, Taking Steps to Improve Your Process, Modern Machine Shop, February, 2000. Can we rearrange the process? Is it now the most efficient? Would changing the layout eliminate handling, transport, etc.? Is the operation sequenced properly? Can operations be performed in parallel?

101 Questions to Ask Before Proceeding From: Wayne Chaneski, Taking Steps to Improve Your Process, Modern Machine Shop, February, 2000. Can we combine steps in the process? Can suppliers perform some operations more effectively? Can customers perform some operations more effectively?

102 Questions to Ask Before Proceeding From: Wayne Chaneski, Taking Steps to Improve Your Process, Modern Machine Shop, February, 2000. Can we simplify the process? Are we taking full advantage of tooling/fixturing/material handling to assist operators? Are instructions easy to understand? Has all the information been made available, including “helpful hints” and “pitfalls” inherent in the process?

103 Questions to Ask Before Proceeding From: Wayne Chaneski, Taking Steps to Improve Your Process, Modern Machine Shop, February, 2000. Can we imagine the perfect process? What would it be? How much time would it take? Would we get a better yield rate? Could we ship products to our customers faster? How different is the perfect process? Can perfect process be documented - flow chart, etc.?

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

105 Steps to Take When Buying a Machine Vision System
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

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

107 Steps to Take When Buying a Machine Vision System
Write functional specification Use “Machine Vision Requirements Checklist” - available from MVA - forces examination of: production process justification issues application issues

108 System Spec Defines “what” system is and “how” system will work
involves examination of implementation details programming standards style control methods

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

110 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

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

112 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?

113 Developing Functional Requirements
Defines “what” system is and “how” system will work involves examination of implementation details programming standards style control methods

114 Developing Functional Requirements
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?

115 Developing Functional Requirements
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?

116 Developing Functional Requirements
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?

117 Developing Functional Requirements
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?

118 Developing Functional Requirements
Describe the application Generically, does the application involve Gauging Assembly verification Flaw inspection Pattern recognition

119 Developing Functional Requirements
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?

120 Developing Functional 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?

121 Developing Functional Requirements
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?

122 Developing Functional Requirements
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

123 Developing Functional 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?

124 Developing Functional Requirements
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”

125 Developing Functional Requirements
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?

126 Developing Functional Requirements
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?

127 Developing Functional Requirements
Object dealing with – Possibility of warping, shrinking, bending, etc? Any change in appearance over time? Any markings? General appearance variables? Sensitivity to light?

128 Developing Functional Requirements
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?

129 Developing Functional Requirements
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?

130 Developing Functional Requirements
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?

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

132 Developing Functional Requirements
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?

133 Developing Functional Requirements
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?

134 Developing Functional Requirements
System availability/reliability Number of hours per week? Hours available for maintenance? Calibration procedures? Challenge procedures? MTBF? MTTR?

135 Developing Functional Requirements
Other issues Special paint? Installation? Warranty? Spare parts? Documentation? Training? Software ownership?

136 Questions?

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

138 RFP Should Request Schedule Training Service Warranty
Software ownership Documentation Installation support

139 Steps to Take When Buying a Machine Vision System
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

140 Understand the Vendors

141 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

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

144 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?

145 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

146 Need a Consultant? Consultants conserve resources
bring technology knowledge bring vendor knowledge bring objective counsel bring negotiating prowess

147 Steps to Take When Buying a Machine Vision System
Evaluate vendors systematically Use Decision Matrix technique to assess proposals Visit the “best” vendors to assess: application engineering skills quality control procedures software practices training materials documentation policies references

148 Responsive Proposals

149 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

150 Proposals Should Include
Position/temperature error budgets Interfacing issues: people machine/line Miscellaneous issues: enclosures start up/changeover battery back up maintenance diagnostics calibration reports

151 Proposals Should Include
Exceptions to the spec Responsibilities: installation specifically what is required for system to be successful Acceptance testing validation procedure challenge set

152 Proposals Should Include
Policy review training installation warranty field service spares software upgrades documentation

153 Proposals Should Include
Schedule Cost

154 Proposals Should Reflect
Familiarity with processes Grasp of problem Completeness and thoroughness Responsiveness Evidence of good organization and management practices Qualifications of personnel

155 Proposals Should Reflect
Experience in similar or related field or application Record of past performance Project planning Technical data and documentation Geographic location

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

157 Proposer Evaluations Schedules References Quality control practices
Vendor skills: optics TV Mechanical engineering Quality engineering

158 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

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

160 Vendor Decision Previous work Quality of work Reputation
Ability to meet schedule Understanding of your business and application

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

162 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!

163 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!

164 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, etc.!

165 Systematic Buy-off Procedure

166 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!

167 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

168 Steps to Take When Buying a Machine Vision System
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

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

170 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!!!

171 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

172 Steps to Take When Buying a Machine Vision System
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.

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176 Steps to Take When Buying a Machine Vision System
Create a challenge to verify performance

177 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!

178 Training Basic principles of operation Normal operating procedures
screen functions power up/down reports Alarm conditions and recovery procedures

179 Training Back-up procedures Normal and emergency maintenance
Calibration

180 Mistakes in Buying Automation

181 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

182 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 technology per E. Martin, Lanco/NuTec, Assembly March 96

183 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

184 Reasons Why Automation Fails
Lack of acceptance by the user organization Poor project management Not properly taking into account the human resources issues

185 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

186 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

187 Project Justification

188 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

189 Project Justification
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

190 Project Justification
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

191 Project Justification
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

192 Project Justification
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

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

194 Project Justification
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 !!!!

195 Project Justification
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)

196 Project Justification
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)?

197 Project Justification
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.

198 Project Justification
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?

199 Project Justification
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?

200 Project Justification
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 ($/hr with benefits)? What is the profit per piece produced? ($)

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

202 Project Justification
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

203 Project Justification
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

204 Project Justification
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

205 Project Justification
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

206 Project Justification
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

207 Project Justification
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

208 Project Justification
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

209 Project Justification
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

210 Project Justification
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)

211 Average Payback Period by Company Size Per Automation Research Corp.

212 Average Payback Period in Years by Industry Per Automation Research Corp.

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