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Figure 6.62. Experimental viscosity models WOA (ML) and WA (SB)

Figure 6 62. Experimental viscosity models WOA (ML) and WA (SB)

Related Figures (121)

Figure 2.1. Incident and reflection of light on an opaque material [5] in Figure 2.1.
Figure 2.1. Incident and reflection of light on an opaque material [5] in Figure 2.1.
Figure 2.2. Scattering, absorption, transparency, and translucency of incident light [7] 2.2 Factors of Colours (The Triad) will appear white; and if the visible light is absorbed by the pigment, then the object appears
Figure 2.2. Scattering, absorption, transparency, and translucency of incident light [7] 2.2 Factors of Colours (The Triad) will appear white; and if the visible light is absorbed by the pigment, then the object appears
Figure 2.4. Two sets of observer functions with difference in field of vision [14]
Figure 2.4. Two sets of observer functions with difference in field of vision [14]
Figure 2.5. Schematic of a digital simulation of a visual colourimeter — spectrocolourimeter [13] 7 Colour Space and Tristimulus Values
Figure 2.5. Schematic of a digital simulation of a visual colourimeter — spectrocolourimeter [13] 7 Colour Space and Tristimulus Values
Figure 2.6. Three-dimensional colour space of hue, lightness, and saturation [10] pure hue to black; and (c) tones, the range from a pure hue to any gray. We can measure a colour quantitatively by creating scales for the hue, lightness, anc
Figure 2.6. Three-dimensional colour space of hue, lightness, and saturation [10] pure hue to black; and (c) tones, the range from a pure hue to any gray. We can measure a colour quantitatively by creating scales for the hue, lightness, anc
Figure 2.7. Colour in the CIE 1976 (L*a*b*) colour space [20]
Figure 2.7. Colour in the CIE 1976 (L*a*b*) colour space [20]
Figure 3.1. (a) Architectural graph of a multilayer perception (MLP) with one hidden layer. (b) Basic concepts of the CNN-supervised learning algorithm [51]
Figure 3.1. (a) Architectural graph of a multilayer perception (MLP) with one hidden layer. (b) Basic concepts of the CNN-supervised learning algorithm [51]
Figure 4.1. Coperion Co-Rotating twin screw extruder Figure 4.2. Injection Molding Machine KM-100
Figure 4.1. Coperion Co-Rotating twin screw extruder Figure 4.2. Injection Molding Machine KM-100
Figure 4.3. (a) Haake Minilab system (Thermo Fisher Scientific), and (b) schematic of the design [115]
Figure 4.3. (a) Haake Minilab system (Thermo Fisher Scientific), and (b) schematic of the design [115]
Figure 4.4. Ares-G2 rotational rheometer: (a) photo, and (b) schematic diagram for the parallel plate (TA- Orchestrator software) [116]
Figure 4.4. Ares-G2 rotational rheometer: (a) photo, and (b) schematic diagram for the parallel plate (TA- Orchestrator software) [116]
Figure 4.5. Tinius Olsen MP600M Et. Plastometer Melt [117] Figure 4.6. X-rite-7000A-Spectrophotometer [118] according to ASTM D1238.
Figure 4.5. Tinius Olsen MP600M Et. Plastometer Melt [117] Figure 4.6. X-rite-7000A-Spectrophotometer [118] according to ASTM D1238.
Figure 4.8. (SEM), Joel 5500 LV [120] Figure 4.7. Keyence-Digital-Microscope VHX-1000 [119]
Figure 4.8. (SEM), Joel 5500 LV [120] Figure 4.7. Keyence-Digital-Microscope VHX-1000 [119]
Figure 4.10. X-ray Micro-CT-scanner-Sky Scan 1172 [122] Figure 4.9. Microtrac $3500 Particle Size Analyzer (PSA) [121]
Figure 4.10. X-ray Micro-CT-scanner-Sky Scan 1172 [122] Figure 4.9. Microtrac $3500 Particle Size Analyzer (PSA) [121]
Table 4.1. Composition of compounding material (g3)
Table 4.1. Composition of compounding material (g3)
Figure 4.11. Schematic diagrams of process methods of plastics Table 4.2. Composition of R1-R2 (polycarbonate resins) blends
Figure 4.11. Schematic diagrams of process methods of plastics Table 4.2. Composition of R1-R2 (polycarbonate resins) blends
Table 4.4. Processing conditions with speed variation Table 4.3. Processing conditions with temperature variation
Table 4.4. Processing conditions with speed variation Table 4.3. Processing conditions with temperature variation
Table 4.5. Processing condition with feed rate variation
Table 4.5. Processing condition with feed rate variation
Table 4.6. Parameters and experimental design level used The desired colour output in terms of CIE L*, a*, b* values was L*= 69.59, a*= 1.42
Table 4.6. Parameters and experimental design level used The desired colour output in terms of CIE L*, a*, b* values was L*= 69.59, a*= 1.42
Figure 4.12. Experimental design of 3 level 3 factor colour. The additional centre point was added to estimate the experimental error.
Figure 4.12. Experimental design of 3 level 3 factor colour. The additional centre point was added to estimate the experimental error.
In the equations above, E, is an actual result, Ep is a predicted result, E,, is a mear value, and N is the number of patterns. The regression coefficient multiple-R* compares t
In the equations above, E, is an actual result, Ep is a predicted result, E,, is a mear value, and N is the number of patterns. The regression coefficient multiple-R* compares t
Table 5.1. Regression model (3-level factor design)
Table 5.1. Regression model (3-level factor design)
Table 5.2. Regression model (BBD)
Table 5.2. Regression model (BBD)
Figure 6.1. Overall flow chart for colour mismatch analysis of the compounded plastic in the flow chart in Figure 6.1.
Figure 6.1. Overall flow chart for colour mismatch analysis of the compounded plastic in the flow chart in Figure 6.1.
Table 6.1. Demonstrating the percentage of adjustment of the lots run in 2009
Table 6.1. Demonstrating the percentage of adjustment of the lots run in 2009
Table 6.2. Pigments requiring the highest percentage of adjustments
Table 6.2. Pigments requiring the highest percentage of adjustments
Table 6.3. Percentage of adjustment of the lines caused by red pigments Red Pigment and the Polymer Grades. SABIC IP produces many grades of PC.
Table 6.3. Percentage of adjustment of the lines caused by red pigments Red Pigment and the Polymer Grades. SABIC IP produces many grades of PC.
The table shows the fourteen grades containing Pl that had the highest rate of Table 6.4. Percentage of adjustment of grades formulated with red pigments
The table shows the fourteen grades containing Pl that had the highest rate of Table 6.4. Percentage of adjustment of grades formulated with red pigments
Figure 6.2. The six grades with the highest rate of adjustment The table shows the fourteen grades containing Pl that had the highest rate of adjustment. Six of these grades were selected for additional statistical and experimental investigations to study the effects of the formulations on the colour shift. The sample grades are illustrated in Fisure 6.2.
Figure 6.2. The six grades with the highest rate of adjustment The table shows the fourteen grades containing Pl that had the highest rate of adjustment. Six of these grades were selected for additional statistical and experimental investigations to study the effects of the formulations on the colour shift. The sample grades are illustrated in Fisure 6.2.
Figure 6.4. Comparison of (a) RMS, (b) mean percentage error, and (c) regression of different neural network algorithms
Figure 6.4. Comparison of (a) RMS, (b) mean percentage error, and (c) regression of different neural network algorithms
Figure 6.5. Comparison of NN output and experimental output for colour tristimulus values L*, a*, and b* of training data
Figure 6.5. Comparison of NN output and experimental output for colour tristimulus values L*, a*, and b* of training data
Figure 6.6. Comparison of experimental and neural network DE values in ascending order maximum value of 4.2, as shown in Figure 6.6.
Figure 6.6. Comparison of experimental and neural network DE values in ascending order maximum value of 4.2, as shown in Figure 6.6.
Figure 6.7. Comparison of (a) regression and (b) MSE for different algorithms cycle GDM depends on two training parameters: the learning rate and momentum coefficient. terms of regression and MSE were out of the desirable range, as shown in Figure 6.7. The
Figure 6.7. Comparison of (a) regression and (b) MSE for different algorithms cycle GDM depends on two training parameters: the learning rate and momentum coefficient. terms of regression and MSE were out of the desirable range, as shown in Figure 6.7. The
Figure 6.8. Network training cycle for LM algorithm Table 6.5. R-Squared and Mean Square Error for 16 neurons in hidden layer
Figure 6.8. Network training cycle for LM algorithm Table 6.5. R-Squared and Mean Square Error for 16 neurons in hidden layer
Figure 6.9. Comparison of experimental and neural network results for L*, a*: (a) training data and (b) test data
Figure 6.9. Comparison of experimental and neural network results for L*, a*: (a) training data and (b) test data
Figure 6.10. Contours of DE with respect to contribution of (a) DL* and Da* and (b) DL* and Db* value of DE was obtained with DL* = 0.973, Da* = 0.0013, and Db* = 0.024.
Figure 6.10. Contours of DE with respect to contribution of (a) DL* and Da* and (b) DL* and Db* value of DE was obtained with DL* = 0.973, Da* = 0.0013, and Db* = 0.024.
Table 6.6. Colour formulation of the three grades mismatch. It indicates that for all the three grades, the minimum deviation from the target
Table 6.6. Colour formulation of the three grades mismatch. It indicates that for all the three grades, the minimum deviation from the target
Figure 6.11. Effect of feed rate on dE* Figure 6.12. Effect of feed rate on b* level of speed.
Figure 6.11. Effect of feed rate on dE* Figure 6.12. Effect of feed rate on b* level of speed.
Figure 6.13. Effect of screw speed on dE*
Figure 6.13. Effect of screw speed on dE*
Table 6.7. ANOVA for each response (A=Temp, B=Speed, C=FeedRate, D=Grade)
Table 6.7. ANOVA for each response (A=Temp, B=Speed, C=FeedRate, D=Grade)
Table 6.8. Possible solutions for each grade
Table 6.8. Possible solutions for each grade
Table 6.9. Compounding material for the opaque grade
Table 6.9. Compounding material for the opaque grade
Figure 6.15. Interaction and overlay plot
Figure 6.15. Interaction and overlay plot
Figure 6.16. Effect of temperature on opaque colour (dE*)
Figure 6.16. Effect of temperature on opaque colour (dE*)
Figure 6.18. Effect of speed on opaque colour (dE*) Figure 6.17. Effect of feed rate on opaque colour (dE*) at higher feed rates and at the centre level of speed.
Figure 6.18. Effect of speed on opaque colour (dE*) Figure 6.17. Effect of feed rate on opaque colour (dE*) at higher feed rates and at the centre level of speed.
Table 6.10. Analysis of variance for L*, a*, and b* The R-Squared value indicates that the components usually would explain about 78% of the variability in the responses whereas about 22% of the variability in the responses
Table 6.10. Analysis of variance for L*, a*, and b* The R-Squared value indicates that the components usually would explain about 78% of the variability in the responses whereas about 22% of the variability in the responses
Figure 6.19. Effect of temperature on colour output (dE*) 6.4 Response Surface Methods
Figure 6.19. Effect of temperature on colour output (dE*) 6.4 Response Surface Methods
interactions are presented in Figure 6.20.
interactions are presented in Figure 6.20.
Table 6.11. Comparison of experimental and predicted values
Table 6.11. Comparison of experimental and predicted values
experimental error. The model appears valid for the prediction of colour values. represents the coded unit of parameters and Y-axis represents the desirability. The plot
experimental error. The model appears valid for the prediction of colour values. represents the coded unit of parameters and Y-axis represents the desirability. The plot
Figure 6.24. Contour plot for L* along temperature and feed rate
Figure 6.24. Contour plot for L* along temperature and feed rate
Figure 6.25. 3D surface graph flagged with optimal desirability fitness of optimal solutions as the response mean values are within 95% CIs.
Figure 6.25. 3D surface graph flagged with optimal desirability fitness of optimal solutions as the response mean values are within 95% CIs.
Figure 6.26. 2D contour graph flagged with optimal desirability Graphical Optimization (Overlay plot). Solutions suggested by the numerical
Figure 6.26. 2D contour graph flagged with optimal desirability Graphical Optimization (Overlay plot). Solutions suggested by the numerical
Figure 6.27. Graphical optimization and sweet spot flagged for a desired solution desired set of process conditions that truly satisfies all the constraints.
Figure 6.27. Graphical optimization and sweet spot flagged for a desired solution desired set of process conditions that truly satisfies all the constraints.
Figure 6.28. Variation in (a) melt flow index vs. polycarbonate resin R2, wt. %, and (b) pressure difference with respect to polycarbonate resin R2, wt. % with and without additives
Figure 6.28. Variation in (a) melt flow index vs. polycarbonate resin R2, wt. %, and (b) pressure difference with respect to polycarbonate resin R2, wt. % with and without additives
Figure 6.29. Strain sweep test for blend R1-R2 (50%-50%) at 10Hz
Figure 6.29. Strain sweep test for blend R1-R2 (50%-50%) at 10Hz
Figure 6.30. Storage modulus of Blend 100%-R1, 30%, 0% at the temperature of 255°C: (a) without additives and pigments, and (b) with additives and pigments fell into a narrow band at the higher frequencies.
Figure 6.30. Storage modulus of Blend 100%-R1, 30%, 0% at the temperature of 255°C: (a) without additives and pigments, and (b) with additives and pigments fell into a narrow band at the higher frequencies.
Figure 6.31. Complex viscosity at a temperature of 255°C: (a) without additives and pigments, and (b) with additives and pigments
Figure 6.31. Complex viscosity at a temperature of 255°C: (a) without additives and pigments, and (b) with additives and pigments
Figure 6.32. Complex viscosity behaviour vs. frequency for all blends of R1-30%: without and with addition of additives at investigated temperatures along with the corresponding Carreua models
Figure 6.32. Complex viscosity behaviour vs. frequency for all blends of R1-30%: without and with addition of additives at investigated temperatures along with the corresponding Carreua models
Figure 6.33. Shear viscosity for Blend R1-30% at temperatures of 230, 255, and 280°C: (a) without additives, and (b) with additives magnitude of shear viscosity is quite comparable to that of complex viscosity. temperatures are depicted in Figure 6.33, without and with the addition of additives. The
Figure 6.33. Shear viscosity for Blend R1-30% at temperatures of 230, 255, and 280°C: (a) without additives, and (b) with additives magnitude of shear viscosity is quite comparable to that of complex viscosity. temperatures are depicted in Figure 6.33, without and with the addition of additives. The
y Rheological Behaviour of the Blend Compounded at SABIC (SB) in all of the blends. ‘iscosity coincided with the linear regime of shear viscosity. Similar behaviour was observed Initially, the composites were rheologically characterized using an Ares-G2
y Rheological Behaviour of the Blend Compounded at SABIC (SB) in all of the blends. ‘iscosity coincided with the linear regime of shear viscosity. Similar behaviour was observed Initially, the composites were rheologically characterized using an Ares-G2
Figure 6.35. Strain sweep test for grade PC1 content of 30 wt. % at 10 Hz
Figure 6.35. Strain sweep test for grade PC1 content of 30 wt. % at 10 Hz
Figure 6.36. Storage (a) and loss (b) modulus at 230, 255, and 280°C
Figure 6.36. Storage (a) and loss (b) modulus at 230, 255, and 280°C
the examined temperatures of 230, 255, and 280°C), as shown in Figure 6.37. measured at temperatures of 230, 255 and 280°C display a shear thinning effect as
the examined temperatures of 230, 255, and 280°C), as shown in Figure 6.37. measured at temperatures of 230, 255 and 280°C display a shear thinning effect as
280°C. The increase in temperature and frequency shows a proportional relation: decreasing
280°C. The increase in temperature and frequency shows a proportional relation: decreasing
Figure 6.40. Tan 6 for R1 30% blends at 255°C Figure 6.39. Complex viscosity for R1 30% blends at 255°C
Figure 6.40. Tan 6 for R1 30% blends at 255°C Figure 6.39. Complex viscosity for R1 30% blends at 255°C
Figure 6.41. Effect of temperature on colour output with respect to dE* (DE) value (dE*) was obtained at the higher temperatures namely 255°C and 280°C with dE*=0.3.
Figure 6.41. Effect of temperature on colour output with respect to dE* (DE) value (dE*) was obtained at the higher temperatures namely 255°C and 280°C with dE*=0.3.
Figure 6.42. Effect of rheological parameters at temperature 230, 255, and 280°C on colour reases the number of particles (frequency) dispersed, ultimately reducing the colour shift
Figure 6.42. Effect of rheological parameters at temperature 230, 255, and 280°C on colour reases the number of particles (frequency) dispersed, ultimately reducing the colour shift
Table 6.13. Fitting parameters of Carreau model, PC blend (ML) with pigment and additives (WA), and without pigment and additives (WOA) Table 6.12. Carreau model parameters for SB at 230, 255, and 280°C
Table 6.13. Fitting parameters of Carreau model, PC blend (ML) with pigment and additives (WA), and without pigment and additives (WOA) Table 6.12. Carreau model parameters for SB at 230, 255, and 280°C
Master Curves at Reference Temperature of 255°C
Master Curves at Reference Temperature of 255°C
Table 6.14. Carreau model parameters at reference temperature of 255°C 6 Effect of Processing Parameters on Dispersion
Table 6.14. Carreau model parameters at reference temperature of 255°C 6 Effect of Processing Parameters on Dispersion
Figure 6.44. CIELAB colour differences for the samples produced at temperatures of 230, 255, and 280°C 255°C; both had better dispersion than those processed at 230°C.
Figure 6.44. CIELAB colour differences for the samples produced at temperatures of 230, 255, and 280°C 255°C; both had better dispersion than those processed at 230°C.
Figure 6.45. Pigment size distribution in the blends produced at the examined temperatures
Figure 6.45. Pigment size distribution in the blends produced at the examined temperatures
Figure 6.46. CIELAB Colour differences at various feed rates: 20, 25, and 30 kg/hr parameter samples Effect of feed rate on Pigment size distribution
Figure 6.46. CIELAB Colour differences at various feed rates: 20, 25, and 30 kg/hr parameter samples Effect of feed rate on Pigment size distribution
Figure 6.47. Pigment size distribution at the examined feed rates
Figure 6.47. Pigment size distribution at the examined feed rates
Figure 6.48. CIELAB colour differences for the samples processed at 700, 750, and 800 rpm Effect of Speed on Pigment Size Distribution
Figure 6.48. CIELAB colour differences for the samples processed at 700, 750, and 800 rpm Effect of Speed on Pigment Size Distribution
Figure 6.49. Pigment size distribution at the examined screw speeds
Figure 6.49. Pigment size distribution at the examined screw speeds
Figure 6.50. Average particle size for four pigments using wet analysis with varying ultrasound time at 30W 6.7.2 SEM Characterization
Figure 6.50. Average particle size for four pigments using wet analysis with varying ultrasound time at 30W 6.7.2 SEM Characterization
Figure 6.51. SEM micrograph of black (a), white (b), yellow (c), and red (d) pigments
Figure 6.51. SEM micrograph of black (a), white (b), yellow (c), and red (d) pigments
of particles had a size of 0.8 microns at 280°C in comparison to 38% at 230°C.
of particles had a size of 0.8 microns at 280°C in comparison to 38% at 230°C.
Figure 6.53. (Clockwise from top left) Micrograph at 5000X of the blend R1-30% at 230°C, 20 kg/hr, 700 rpm, and 800 rpm (colour chips) temperatures, feed rates and screw speed.
Figure 6.53. (Clockwise from top left) Micrograph at 5000X of the blend R1-30% at 230°C, 20 kg/hr, 700 rpm, and 800 rpm (colour chips) temperatures, feed rates and screw speed.
Figure 6.54. DOM-Particle distribution for chip sample processed at 750rpm (left), and microtome sample processed at 280°C (right) micron chip samples, cut by a microtome.
Figure 6.54. DOM-Particle distribution for chip sample processed at 750rpm (left), and microtome sample processed at 280°C (right) micron chip samples, cut by a microtome.
Figure 6.55. DOM-agglomerations for (a) 700rpm, (b) 800 rpm, (c) 230°C, and (d) 30 kg/hr; samples magnified to 2000X, measured by 50-micron chip, and cut by microtome
Figure 6.55. DOM-agglomerations for (a) 700rpm, (b) 800 rpm, (c) 230°C, and (d) 30 kg/hr; samples magnified to 2000X, measured by 50-micron chip, and cut by microtome
Figure 6.56. SEM micrograph of polycarbonate grade compound at 230, 255, and 280°C agglomerations than that of the sample, produced at higher parameters.
Figure 6.56. SEM micrograph of polycarbonate grade compound at 230, 255, and 280°C agglomerations than that of the sample, produced at higher parameters.
Figure 6.57. SEM micrograph of polycarbonate grade compound at 20, 25, and 30 kg/hr
Figure 6.57. SEM micrograph of polycarbonate grade compound at 20, 25, and 30 kg/hr
Figure 6.58. SEM micrograph of polycarbonate grade compound at 700, 750, and 800 rpm
Figure 6.58. SEM micrograph of polycarbonate grade compound at 700, 750, and 800 rpm
Figure 6.59. Micro CT scanner micrographs of the blend processed at 750 rpm, 255°C, and 25 kg/hr: (a) agglomerations, (b) particle shape, and (c, d) pigment distribution
Figure 6.59. Micro CT scanner micrographs of the blend processed at 750 rpm, 255°C, and 25 kg/hr: (a) agglomerations, (b) particle shape, and (c, d) pigment distribution
colour variations in different compounding processes.
colour variations in different compounding processes.
Figure 6.61. Viscosity (a) and particle size distribution (b) for the blends processed in two different extruders (ML and SB) at a temperature of 255°C
Figure 6.61. Viscosity (a) and particle size distribution (b) for the blends processed in two different extruders (ML and SB) at a temperature of 255°C
constant regime of complex viscosity coincided with that of shear viscosity. mtaining 30 wt. % of resin R1 processed at temperatures of 230, 255, and 280°C. The
constant regime of complex viscosity coincided with that of shear viscosity. mtaining 30 wt. % of resin R1 processed at temperatures of 230, 255, and 280°C. The
Table 6.15. Effect of processing parameters on colour in terms of tristimulus values
Table 6.15. Effect of processing parameters on colour in terms of tristimulus values
Figure 6.64. Particle diameter distribution for (a) temperature, (b) feed rate, (c) speed, and (d) for 230°C temperature layers at thickness A (90 um), B (70 um), and C (50 um)
Figure 6.64. Particle diameter distribution for (a) temperature, (b) feed rate, (c) speed, and (d) for 230°C temperature layers at thickness A (90 um), B (70 um), and C (50 um)
Table A-1. The complete dataset used for training (70%), validation (15%), and testing (15%); R=resin, A=additives, P=pigments
Table A-1. The complete dataset used for training (70%), validation (15%), and testing (15%); R=resin, A=additives, P=pigments
Figure A—1. Comparison of NN and experimental output for colour tristimulus values L*, a*, b*: validation data
Figure A—1. Comparison of NN and experimental output for colour tristimulus values L*, a*, b*: validation data
Figure A—2. Comparison of NN and experimental output for colour tristimulus values L*, a*, b*: test data
Figure A—2. Comparison of NN and experimental output for colour tristimulus values L*, a*, b*: test data
Table A—2. Weights between the input layer and hidden layer for L*, a*, and b*
Table A—2. Weights between the input layer and hidden layer for L*, a*, and b*
Figure B—3. Mixing two colours additively leads to a lighter colour; if red, green, and blue light are mixed equally together at full power, you get white light (from WWW.MICA.EDU) yellow. If red, green, and blue light are mixed equally together at full power, you get whit
Figure B—3. Mixing two colours additively leads to a lighter colour; if red, green, and blue light are mixed equally together at full power, you get white light (from WWW.MICA.EDU) yellow. If red, green, and blue light are mixed equally together at full power, you get whit
CMC (Colour Measurement Committee of the Society of Dyes and Colourists of Great Britain): Organization that developed and published in 1988 a more logical, ellipse-based
CMC (Colour Measurement Committee of the Society of Dyes and Colourists of Great Britain): Organization that developed and published in 1988 a more logical, ellipse-based
Figure B—6. Cultural variations in the meaning of colour (http://www.swiss-miss.com/2005/08/page/2) Cultural Variations in the Meaning of Colours: See figure below D65: The CIE standard illuminant that represents a colour temperature of 6504K. This is the
Figure B—6. Cultural variations in the meaning of colour (http://www.swiss-miss.com/2005/08/page/2) Cultural Variations in the Meaning of Colours: See figure below D65: The CIE standard illuminant that represents a colour temperature of 6504K. This is the
Figure B—7. Hue changes by moving position on the colour circle (from Hyper Physics) distinguish red from green, blue from yellow, etc. Munsell defined five principal hues (red,
Figure B—7. Hue changes by moving position on the colour circle (from Hyper Physics) distinguish red from green, blue from yellow, etc. Munsell defined five principal hues (red,
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