Polymer Color Intelligence: Effect of Materials, Instruments, and Measurement Techniques - A Review

Development of a Machine Learning Model to Predict the Color of Extruded Thermoplastic Resins

The conventional method for the color-matching process involves the compounding of polymers with pigments and then preparing plaques by using injection molding before measuring the color by an offline spectrophotometer. If the color fails to meet the L*, a*, and b* standards, the color-matching process must be repeated. In this study, the aim is to develop a machine learning model that is capable of predicting offline color using data from inline color measurements, thereby significantly reducing the time that is required for the color-matching process. The inline color data were measured using an inline process spectrophotometer, while the offline color data were measured using a bench-top spectrophotometer. The results showed that the Bagging with Decision Tree Regression and Random Forest Regression can predict the offline color data with aggregated color differences (dE) of 10.87 and 10.75. Compared to other machine learning methods, Bagging with Decision Tree Regression and Random Forest Regression excel due to their robustness, ability to handle nonlinear relationships, and provision of insights into feature importance. This study offers valuable guidance for achieving Bagging with Decision Tree Regression and Random Forest Regression to correlate inline and offline color data, potentially reducing time and material waste in color matching. Furthermore, it facilitates timely corrections in the event of color discrepancies being observed via inline measurements.

Influence of Compounding Parameters on Color Space and Properties of Thermoplastics with Ultramarine Blue Pigment

The incorporation of thermoplastics with pigments imparts diverse aesthetic qualities and properties to colored thermoplastic products. The selection of pigment type and content, along with specific processing conditions, plays a pivotal role in influencing color properties and overall product performance. This study focuses on optimizing these parameters to ensure the desired color quality and product functionality. Two types of polypropylene copolymer (PPCP) with different melt flow rates (MFRs) and acrylonitrile butadiene styrene (ABS) were compounded with ultramarine blue pigment masterbatch (MB) in concentrations ranging from 1 to 5 wt.% using a twin-screw extruder. The compounding process was conducted at a constant screw speed of 200 rpm and a die temperature of 210 °C. The effects of screw speed and die temperature were investigated at a constant MB of 3 wt.%. Colored samples were fabricated by injection molding. Microscopic analysis revealed a well-dispersed pigment within the PPCP matrix when using the MB. Rheological properties, assessed through the power law index, confirmed effective pigment dispersion, facilitated by shear thinning behavior and controlled shear rate via the manipulation of screw speed and die temperature. The effects of masterbatch contents and processing conditions on color spaces were evaluated using CIELAB and CIELCH, with one-way ANOVA employed to identify statistical significance. Higher opacity in high-MFR PPCP and ABS resulted in increased lightness and color strength, surpassing low-MFR PPCP by 15-40% at equivalent MB contents. Masterbatch content emerged as a significant factor influencing the color spaces of all colored thermoplastics. Further analysis, including Fisher pairwise comparisons of one-way ANOVA, revealed that screw speed influenced the redness and hue of low-MFR PPCP, whereas die temperature affected the lightness and hue of high-MFR PPCP and ABS. Interestingly, the blueness and chroma of colored thermoplastics were minimally affected by both screw speed and die temperature. Notably, regardless of processing conditions, the flexural properties of colored thermoplastics remained comparable to the neat polymer when incorporated with ultramarine blue pigment masterbatch.