Integrating Transmission-Based Color Measurement with Clustering for Objective Grading of Translucent Plastics

Accurate and objective color grading is essential for quality control in the plastics industry. However, conventional visual inspection is subjective, while commercial color measurement instruments may be limited by measurement geometry, sample dimensions, or insufficient sensitivity to subtle color variations. This study proposes a low-cost transmission-based color measurement device integrating with unsupervised clustering for detecting subtle color differences in large-size and translucent general-purpose polystyrene samples. The system measures transmitted light through the samples and converts the sensor outputs into CIE L*a*b* color coordinates and derived color characteristics. A dataset comprising 128 GP samples classified into three quality grades by visual inspection is subsequently analyzed using Welch’s ANOVA and the Games–Howell post hoc test. To establish more objective grading criteria, K-Means, spectral clustering, Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH), and Gaussian mixture models, which allow the number of clusters to be specified, are employed to determine quantitative color difference thresholds for the different grades. Jenks natural breaks, a simple one-dimensional classification method, is also used for comparison. Experimental results demonstrate that the proposed device has good measurement reproducibility and greater separation of subtle color variations than a commercial reflectance colorimeter. For the same set of GP samples, the color difference values measured using the proposed device range from 0.57 ± 0.08 to 3.06 ± 0.31, compared with 0.25 ± 0.04 to 0.70 ± 0.08 using the commercial colorimeter. BIRCH achieves the highest adjusted Rand index (0.50) and normalized mutual information (0.35), whereas K-Means produced the highest silhouette score (0.57). Overall, the color difference cutoff thresholds derived from BIRCH clustering show the closest agreement with the manufacturer’s practical quality control requirements. These results demonstrate that combining transmission-based color measurement with unsupervised machine learning provides an effective approach to objective and quantitative color grading of translucent plastic products. The proposed system provides a practical foundation for AI-assisted quality assurance and smart manufacturing in the plastics industry.