Aanæs, Henrik

Eiríksson, Eyþór Rúnar

Pedersen, David Bue

In this paper we address the colorimetric performance of a multicolor additive manufacturing process. A method on how to measure and characterize color performance of said process is presented. Furthermore, a method on predicting the color output is demonstrated, allowing for previsualization of parts prior to print. Results show that color prediction can be achieved with an average color difference error of ΔE*00 = 1.5 andstd.dev σ= 0.75, with similar order of magnitude as the literature defined threshold for „Just NoticeableDifference” (JND)

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Downloaded from orbit.dtu.dk on: Dec 21, 2017Predicting Color Output of Additive Manufactured PartsEiríksson, Eyþór Rúnar; Pedersen, David Bue; Aanæs, HenrikPublished in:Proceedings of Achieving Precision Tolerances in Additive ManufacturingPublication date:2015Document VersionPublisher's PDF, also known as Version of recordLink back to DTU OrbitCitation (APA):Eiríksson, E. R., Pedersen, D. B., & Aanæs, H. (2015). Predicting Color Output of Additive Manufactured Parts.In Proceedings of Achieving Precision Tolerances in Additive Manufacturing: ASPE Spring Topical Meeting (Vol.60, pp. 95-99). ASPE – The American Society for Precision Engineering.AMERICAN SOCIETY FOR PRECISION ENGINEERING 95PREDICTING COLOR OUTPUT OF ADDITIVE MANUFACTUREDPARTSEythor R. Eiriksson1, David B. Pedersen2 and Henrik Aanaes11Department of Applied Mathematics and Computer Science2Department of Mechanical EngineeringTechnical University of DenmarkKgs. Lyngby, DenmarkABSTRACTIn this paper we address the colorimetric perfor-mance of a multicolor additive manufacturing pro-cess. A method on how to measure and charac-terize color performance of said process is pre-sented. Furthermore, a method on predicting thecolor output is demonstrated, allowing for pre-visualization of parts prior to print. Results showthat color prediction can be achieved with an av-erage color difference error of ∆E∗00 = 1.5 andstd.dev σ = 0.75, with similar order of magnitudeas the literature defined threshold for „Just Notice-able Difference” (JND).INTRODUCTIONAdditive manufacturing has been an acceptedmeans of production for the past decade and is arapidly growing market. Today, additive manufac-turing technologies are offering multi-color printedparts in an assortment of different materials[1].Availability of print per order parts, where cus-tomers can supply their own high resolution colortextures and geometries, has become a real-ity1234. Therefore, geometry and color quality as-surance is required. So far, research focus hasbeen placed on geometrical verification, givingpromising results, whereas less effort has beenplaced on color verification[2][3]. Here we ad-dress the colorimetric performance aspect.When converting 3D color models to the physi-cal domain, through additive manufacturing, theenormous range of color available during model-ing is not reproducible by the printer. The com-plete range of producible colors is known as theprinters gamut. Any input colors outside the print-able gamut will simply be constrained to a gamutboundary color, as illustrated in Figure 2. Thisis a known problem in the paper printing indus-try and is normally solved through printer profil-1shapeways.com2twinkind.com3i.materialize.com4figureprints.coming. Printer manufacturers sometimes offer theseprinter profiles which describe the printers colorproduction capabilities according to a standarddefined by the International Color Consortium(ICC).In order to fully profile a printers color gamut, onewould ideally define the input 3D model in termsof the printers native color space (CMYK ). Un-fortunately this is not possible, as color 3D meshrepresentations are derived from the RGB space.This is to be expected, as these data formats aremostly intended for use in RGB based devicessuch as a computer monitor. It is clear that thegrowing market of color 3D printing calls for anew or updated mesh format. To the authors bestknowledge, no color 3D printing manufacturer of-fers color profiles or color management features.That being said and due to most printers pro-prietary nature, any color correction needs to bemade at the 3D modeling level.MATERIALS AND METHODIn order to characterize the gamut of a ZCorpZPrinter 650, a patched color plate was printedas seen in Figure 1.FIGURE 1. 3D model of the color calibrationplate, providing 729 unique color patches.96 201 5  SPRING TOPICAL MEETING VOLUME 60FIGURE 2. Comparison of input sRGB values to the actual measured values after print. The printers colorgeneration limitations are apparent. Any colors outside of this printer gamut are not printable.3D Model GenerationThe color plate was generated in the Wavefront.OBJ format where each square in the plate wasdefined with a unique face color. This methodwas selected over applying a color image textureonto a blank plate. Thus, eliminating all uncer-tainties of texture handling and mapping in theprinter software. The 729 unique color patcheswere sampled evenly from the entire three dimen-sional sRGB space, as visualized by Figure 2.This 9 × 9 × 9 sample was selected due to phys-ical size constraints set by the color measure-ment system used. The final plate dimensionsare therefore 64.25 × 64.25 × 3 mm with individ-ual patch size of 2.15× 2.15 mm.Print ProcedureIn order to capture the variability between prints,a set of 15 plates were printed in a stacked config-uration, centered in the build volume. Layer thick-ness was set to 89 µm, bleed compensation en-abled and the ZP150A powder was used. Specialcare was taken in thoroughly removing residualpowder as to minimize color variations betweenprints whose effect can be seen in Figure 3. As itis known, infiltrating substances have significanteffect on color vibrancy, and therefore the printswere not infiltrated in order to minimize additionalvariability. Modeling of this effect, dependent oninfiltration type is a subject for future work.FIGURE 3. Two color plates, before brushing(left) and after brushing (right).MeasurementEach color patch was measured using the multi-spectral imaging system VideometerLab2.5 TheVideometerLab2 allows for rapid color capture ofan entire plate, contrary to other point samplebased color measurement devices such as a col-orimeter. Measurements were made in the de-vice independent CIELAB color space under theD50 illuminant. Example output from the instru-ment can be seen in Figure 4. An automatedcolor patch extraction algorithm was implemented5www.videometer.comAMERICAN SOCIETY FOR PRECISION ENGINEERING 97and for each colored patch an average color valuewas computed, ideally simulating the perceptualintegration as some color values are printed indithered like patterns.FIGURE 4. Automated patch extraction algo-rithm collects patches and computes their aver-age color value.RESULTSAn average plate was computed from all of the15 measured plates. The color difference metric∆E∗00 from the average color was computed foreach color in the CIELAB space[4]. The color dif-ference distribution from the mean for each plateis shown in Figure 5.00.511.522.533.544.551 2 3 4 5 6 7 8 9 10 11 12 13 14 15Plate Number∆ E* 00FIGURE 5. Boxplots illustrating color difference∆E∗00 from the group mean. Median value isshown as a line in each box; Box edges are the25th and 75th percentiles; Whiskers extend to themost extreme data points not considered outliers;Outliers are plotted individually.It is apparent from the figure that the first andlast plates in the stack differ significantly and aretherefore omitted from the model. Further study isneeded in order to evaluate whether this is a reoc-curring trend. Figure 2 shows the transformationfrom the input sRGB values and the measuredvalues. It is clear that the printer is not capable ofprinting a large majority of the input colors. Fig-ure 6 clearly shows the printers inability to pro-duce darker colors and the average color error is∆E∗00 = 21.4, std.dev σ = 4.15. Several studieshave tried to estimate the Just Noticeable colorDifference threshold (JND) of the ∆E∗00 metric,however many are in disagreement. DocumentedJND threshold values range from 1 to 5.9[5]. Theaverage systematic error was estimated by re-peatedly measuring the same plate several timesand an identical analysis performed. The mea-sured systematic error difference was ∆E∗00 =0.11 and std.dev σ = 0.07, orders of magnitudelower than the variations between prints.  ∆ E* 0051015202530354045FIGURE 6. Color difference between the idealplate and an average of the measured plates.From the average color plate measurements, a3D Look-Up Table (3D LUT) was constructed re-lating the input sRGB space to the measuredCIELAB space. The input LUT is essentially a3D lattice structure where each lattice corner con-tains CIELAB measurement data. From there,any intermediate points within a single latticecube can be estimated using trilinear interpola-tion. This 3D LUT model can thus be used to pre-dict resulting color measurements of a printed ob-ject given an input color. 3D LUTs are commonlyused for this color conversion purpose and can beefficiently implemented on modern hardware.98 201 5  SPRING TOPICAL MEETING VOLUME 60MODEL EVALUATIONA week after the initial prints were made, a colorplate as seen in Figure 7 was generated with ran-dom color values. A print prediction was per-formed using the 3D LUT model and the platewas printed using identical procedures as before.Post print, the plate was measured and comparedto the predicted output. The color difference fromthe prediction obtained from the 3D LUT modeland the measured plate is illustrated in Figure 8.The computed error mean was ∆E∗00 = 1.5 withstd.dev σ = 0.75.FIGURE 7. Plate with randomly generated colors,used for model verification.  ∆ E* 000.511.522.533.544.5FIGURE 8. Color difference between predictionand actual measurements of the random plate.DISCUSSION AND SUMMARYThe method described in this paper has been ap-plied on an inkjet based 3D printer. Its variabilityand color limitations were visualized. When look-ing at the group statistics of the plates we observethe bottom and top plates to be statistically differ-ent from the rest. For the plate located in the bot-tom, we have two hypotheses. Firstly, the pow-der in the build chamber might not be compactenough in the bottom prior to print, thus yield-ing different surface textures. Secondly, the printhead might not have reached a steady state ofoperation so early in the print. For the top plate,we believe it might be a factor of powder compres-sion and drying as only a few extra layers of pow-der are deposited over the last plate. The remain-ing plates were measured using a multi-spectralimaging system with determined average system-atic color measurement error of ∆E∗00 = 0.11 andstd.dev σ = 0.07. Upon comparing the input datato an average of the measured data it was ap-parent that a large part of the input colors wasnot printed to specification. In fact, the averagecolor error was ∆E∗00 = 21.4, std.dev σ = 4.15and maximum color error greater than 45. Signifi-cantly exceeding the literature defined noticeablecolor difference threshold which ranges from 1-5.9. The largest error contribution was from thedarker colors, particularly in the absence of redand green. These error regions are expected tosomewhat heal by infiltration, where darker re-gions are known to benefit greatly. However, thismight come at the price of the more lighter colors,as white will appear more grayish. A 3D Look-Up Table was constructed relating the input colorvalues to the empirically measured color values.This allowed for a simple and efficient way to per-form a color prediction given an input color value.If an input color value was not present in thetable, trilinear interpolation was performed fromthe known values, yielding a color estimate. Toevaluate the prediction model, a new color platewas generated consisting of randomly generatedcolor patches. It was printed on the same printerand finally compared to the generated predictionmodel. The model captured the color conver-sion well with reasonable accuracy and an av-erage color difference error of ∆E∗00 = 1.5 withstd.dev σ = 0.75. This result lies within the no-ticeable color difference threshold range, makingthis method a promising candidate for color cor-rection. Some error contribution is due to interpo-lation of color values, to which extent is unknown.The effect could be minimized by an even greaterAMERICAN SOCIETY FOR PRECISION ENGINEERING 99sampling of the input color space. An importantaspect of this study lies in the fact that the modeland model verification was performed at a weeksinterval, whilst the printer underwent normal use.It is therefore, interesting to see the stability of theprint process and that the model holds, makingthis a candidate for longer term production runsutilizing the current setup.FUTURE WORKReverse modeling methods will be attempted, re-lating the CIELAB output space to the sRGB inputspace. Color correction could thus be applied tothe 3D modeled part prior to print. Furthermore,a further statistical analysis on the error compo-nents will be conducted.ACKNOWLEDGEMENTThe work of this paper was supported and madepossible by the Manufacturing Academy of Den-mark (MADE).66www.made.dkREFERENCES[1] Wohlers T, Caffrey T. Additive manufacturingand 3D printing state of the industry annualworldwide progress report. 2014. WohlersAssociates. 2013;.[2] Hansen HN, Nielsen JS, Rasmussen J, Ped-ersen DB. Performance Verification of 3DPrinters. In: Proc. ASPE Spring TopicalMeeting on Dimensional Accuracy and Sur-face Finish in Additive Manufacturing (13-16April 2014, Berkeley, CA, USA) pp; 2014. p.104–107.[3] Pedersen DB, Hansen HN. Comparabilityof the performance of in-line computer vi-sion for geometrical verification of parts, pro-duced by additive manufacturing. In: Proc.ASPE Spring Topical Meeting on Dimen-sional Accuracy and Surface Finish in Addi-tive Manufacturing (13-16 April 2014, Berke-ley, CA, USA) pp; 2014. p. 179–183.[4] Sharma G, Wu W, Dalal EN. TheCIEDE2000 color-difference formula: Imple-mentation notes, supplementary test data,and mathematical observations. Color Re-search & Application. 2005;30(1):21–30.[5] Larraín R, Schaefer D, Reed J. Use ofdigital images to estimate CIE color coordi-nates of beef. Food Research International.2008;41(4):380–385.

Predicting Color Output of Additive Manufactured Parts

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Predicting Color Output of Additive Manufactured Parts

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