Evolution of Print Measurement: A Historical Perspective on Quality Verification

1. ColorMetrixUser Group2004 Keynote presentation by Howard Nelson Ed.D ColorMetrix 4th Annual User Group Meeting August 8-10, 2004 • Las Vegas, Nevada

2. Print Measurement as Historical Eras • Decades in which projects were initiated (and perhaps continue to evolve) • Provides us with an overview of where we came from, so we might be able to predict where technology will take us next

3. 50 Years of Print Quality Verification • Measurement of printing through print characteristics • Print consistency control • Print quality control

4. LTF • Began researching lithographic technology during WW II for the US War Department • Began to research and consult for private industry after the War • Became GATF in the 1960’s

5. Historically Speaking • The 1950’s were the research era • Introduction of the scientific approach to problem-solving • LTF began to publish their findings in the “popular trade-press” • First densitometers became available

6. Historically… • 1960’s were the developmental era • Web offset printing grabbed the market-share for most impressions • Densitometers became widely used • GATF pioneered print characteristic identification and calculation

7. Historically • 1970’s were the standardization era • SNAP - 1970 - 1972 • SWOP - 1976 - 1980 • Color proofing systems introduced • Print measurement investigates ink/paper/chemistry relationships

8. Historically • 1980’s were a consolidation era • CEPS systems improve halftone control • Color proofing systems improved • First Spectrophotometers available • Print measurement data feeds back to improve prepress accuracy

9. Historically • 1990’s were the era of verification • No-proof editorial • Spectrophotometers become generally available • ColorMetrix Technology LLC • Press “Fingerprinting” for process control

10. Press Fingerprinting • Five rules of Press Fingerprinting • Simulate production • Choose a test form • Run the form • Measure the sheets and collect data • Feedback to prepress • A step beyond??

11. Print Control Measurements • Solid Ink Density • Dot Area Gain (at the 50% dot value) • Print Contrast • % Trap (for Wet Ink Trap) • 3/C Neutral Gray Balance • Hue Error and Grayness Error

12. Solid Ink Density • Makeready aimpoint for color approval • Print consistency target • Basis reading for other calculations • One of the main image contrast indicators

13. Dot Area Gain • Image contrast indicator

14. Print Contrast • How well the press/ink/paper combination is able to render shadow detail by differentiating between shadow area tone values

15. Wet Ink Trap • Control of secondary (RGB) colors

16. Verification Fingerprinting • Monitoring “Sheet Contrast” • At a given Solid Ink Coverage, print contrast characteristics shouldn’t vary • C=1.30, M=1.40, Y=1.05, K=1.60 • DG=20%, PC=40%, Trap • Fingerprinting to verify press components are functional

17. Press Component Life • Like aircraft component parts, press parts are rated for useful life • Plates & blankets by # of impressions • Rollers by # of operating hours • Even cylinder bearers are rated TBOoR components

18. Verification Fingerprinting • Run test form under “Press New” conditions • Discover and monitor TBOoR for press parts • Re-run test form to verify need for replacement • Watch sheet contrast for clues

19. Scanable Press Test Form • The scanable press test form

20. Scanable Form Components • Two-tier standard color bar • Scanable color bar

21. Scanable Form Components • Scanable ICC color profile

22. Scanable Form Components • Scanable Tone Ramps

23. Scanable Form Components • Scanable Gray Balance Ramps

24. Scanable Form Components • Scanable Total Ink Coverage Ramps

25. ColorMetrix • Collects and displays graphic data • Displays Run with VOC tolerances • Process Trending • Color hexagon • Press fingerprinting • Data sharing with other programs

26. Historically Speaking • 2000 - 2010 may be the era of SPC • Use of statistics to identify problems • Use of statistics to monitor runs • Using statistics to predict outcomes

27. Six Sigma Data Analysis • Specifies the amount of variation experienced compared to the specs • Greater process predictability Lowers costs by minimizing waste and rework • Isolates special cause variation from common cause variation

28. Descriptive Statistics • Mean • The average of the data as collected • Standard Deviation • The value of one sigma

29. Run Chart • Note the value of each individual point • Observe trends during the run • Runs up and down vs expected runs • Observe the P-Value • P-Value for Clustering, P < .05 = Special Cause • P-Value for Mixtures, P > .95 = Special Cause

30. I-MR Chart • Individual Value of each data point of the run • Mean, UNPL, LNPL • Moving Range of the differences in value during the run • Average range, UNPL, LNPL

31. Process Capability Analysis • The Cp index • Ratio of the spec limits to the width of the process • Cp = 2 means the process is stable • Cp ≤ 1 means the process is unstable • The Cpk index • Ratio of the process width to the spec width including centering of the spec on the process • Cpk > 1 means the process is capable of meeting spec • Cpk = 1 or less means the process is incapable of meeting spec

32. Differences in Process Capability

33. Some Words of Thanks • Colormetrix Technologies LLC • Jim Raffel, Mike Litcher, Mike Woods • E. I. DuPont de Nemours • GATF / PIA and Gretag-MacBeth • Flint Ink Corp. • Jeff Gilbert, Craig Stone