ABSTRACT
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.
ABSTRACT
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.
ABSTRACT
Commercial filaments of poly(lactic acid) (PLA) composites with particulate filler, carbon fiber, and copper powder with different contents were fabricated by FDM 3D printing in XZ-direction at bed temperatures of 45 °C and 60 °C. The effects of additives and bed temperatures on layer adhesion, fracture behavior, and mechanical performance of the PLA composites 3D printing were evaluated. Rheological properties informed viscous nature of all filaments and interface bonding in the PLA composites, which improved printability and dimensional stability of the 3D printing. Crystallinity of the PLA composites 3D printing increased with increasing bed temperature resulting in an improvement of storage modulus, tensile, and flexural properties. On the contrary, the ductility of the 3D printing was raised when printed at low bed temperature. Dynamic mechanical properties, the degree of entanglement, the adhesion factor, the effectiveness coefficient, the reinforcing efficiency factor, and the Cole-Cole analysis were used to understand the layer adhesion, and the interfacial interaction of the composites as compared to the compression molded sheets. SEM images revealed good adhesion between the additives and the PLA matrix. However, the additives induced faster solidification and showed larger voids in the 3D printing, which indicated lower layer adhesion as compared to neat PLA. It can be noted that the combination of the additives and the optimized 3D printing conditions would be obtain superior mechanical performance even layer adhesion has been restricted.
ABSTRACT
Poly(lactic acid) (PLA) filaments have been the most used in fused deposition modeling (FDM) 3D printing. The filaments, based on PLA, are continuing to be developed to overcome brittleness, low heat resistance, and obtain superior mechanical performance in 3D printing. From our previous study, the binary blend composites from PLA and poly(butylene adipate-co-terephthalate) (PBAT) with nano talc (PLA/PBAT/nano talc) at 70/30/10 showed an improvement in toughness and printability in FDM 3D printing. Nevertheless, interlayer adhesion, anisotropic characteristics, and heat resistance have been promoted for further application in FDM 3D printing. In this study, binary and ternary blend composites from PLA/PBAT and poly(butylene succinate) (PBS) with nano talc were prepared at a ratio of PLA 70 wt. % and blending with PBAT or PBS at 30 wt. % and nano talc at 10 wt. %. The materials were compounded via a twin-screw extruder and applied to the filament using a capillary rheometer. PLA/PBAT/PBS/nano talc blend composites were printed using FDM 3D printing. Thermal analysis, viscosity, interlayer adhesion, mechanical properties, and dimensional accuracy of binary and ternary blend composite 3D prints were investigated. The incorporation of of PBS-enhanced crystallinity of the blend composite 3D prints resulted in an improvement to mechanical properties, heat resistance, and anisotropic characteristics. Flexibility of the blend composites was obtained by presentation of PBAT. It should be noted that the core-shell morphology of the ternary blend influenced the reduction of volume shrinkage, which obtained good surface roughness and dimensional accuracy in the ternary blend composite 3D printing.
