This study aims to bridge the persistent lab-to-bulk reproducibility gap in polyester thread dyeing with disperse dyes by developing a systematic, closed-loop optimization approach. The research focuses on achieving right-first-time (RFT) dyeing results at the industrial scale, minimizing shade variation between laboratory and bulk production and reducing resource-intensive reprocessing cycles. The overarching goal is to enhance process accuracy, sustainability and production efficiency in synthetic fiber coloration, thereby addressing both technical and environmental challenges associated with scale-up operations in the sewing thread industry.
To achieve RFT production by aligning laboratory and bulk dyeing parameters, five experimental models were conducted. Models 1 and 2 aligned spectrophotometric recipes at lab and bulk scales, with Model 2 enhancing lab–bulk matching via bulk dyeing time adjustment (30–45 min). In Model 3, auxiliary chemical concentrations were adjusted to avoid dye migration and shade variation. In Model 4, the effect of liquor ratios (LRs) (1:20–1:50) was observed on yellow, navy and brown colors. Model 5 integrated all previous optimizations, finalizing optimal LRs (1:30 for yellow, 1:40 for navy and 1:50 for brown) and extending the study to different polyester thread counts (30/2, 50/2, 54/3 and 30/4). The dyeing performance of different experimental models was evaluated through CMC ΔE values (<1) and color fastness assessments for crocking, washing and bleaching.
The developed optimization protocol effectively minimized color variation between lab and bulk dyeing, demonstrating excellent shade reproducibility, by achieving CMC ΔE values below 1 across all tested polyester thread counts and a peak color strength of 101.54%. Fastness ratings for various tests remained at or above Grade 4, indicating strong color durability. The protocol lowered reprocessing frequency by 15%–20%, which translated to an 8%–12% reduction in production costs due to decreased dye consumption, energy use and machine time. This approach enhances RFT performance and offers a practical, cost-effective method that improves productivity and lessens the environmental impact of large-scale dyeing operations.
This research presents a closed-loop optimization framework for translating laboratory dyeing parameters to industrial processes, emphasizing sustainability. It utilizes data-driven parameter tuning to improve shade consistency and resource efficiency, offering a practical solution for the polyester thread dyeing industry to enhance performance, reduce waste and minimize environmental impact during scale-up, thereby contributing to sustainable practices in synthetic textile manufacturing.
