PDF(Pubmed)
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.
摘要：
用于颜色匹配过程的常规方法包括将聚合物与颜料混合，然后在通过离线分光光度计测量颜色之前通过注塑成型制备板。如果颜色不符合L*，a*,和B*标准，必须重复配色过程。在这项研究中,目的是开发一种机器学习模型，该模型能够使用内联颜色测量数据预测离线颜色，从而显著减少颜色匹配过程所需的时间。使用在线过程分光光度计测量在线颜色数据，而离线颜色数据是使用台式分光光度计测量的。结果表明，带决策树回归和随机森林回归的Bagging可以预测离线颜色数据，聚合色差（dE）分别为10.87和10.75。与其他机器学习方法相比，用决策树回归和随机森林回归进行打包，由于它们的鲁棒性，处理非线性关系的能力，并提供对特征重要性的见解。这项研究为实现利用决策树回归和随机森林回归进行Bagging以关联内联和离线颜色数据提供了有价值的指导。潜在地减少颜色匹配中的时间和材料浪费。此外，它有助于在通过在线测量观察到颜色差异的情况下及时校正。
