To address subjectivity and indicator weighting controversy in traditional print quality evaluation, the work aims to establish an objective and quantifiable multi-dimensional evaluation system, providing a scientific basis and decision-making support for the precise adjustment and optimization of printing process parameters. Based on the data from 36 print samples (including key indicators such as solid density, relative contrast, print gloss, dot gain, trapping rate, etc.), the quality was classified with a five-level scale. Ordinal Logistic Regression (OLR, α=0.05, max iterations=500) was adopted to classify quality grades and quantify parameter impacts (β, OR, 95% CI). The Random Forest (RF, trees=500, max depth=10) was employed to assess the feature importance (Gini decrease). A Bayesian Hierarchical Model (BHM, MCMC samples=3 000, warmup=1 000, chains=4) was constructed to capture non-linear interactions and heterogeneity. The OLR-RF-BHM model was established and validated via accuracy, AUC, and comprehensive quality ranking (CQS). OLR identified solid density (β=0.82, OR=2.27, P<0.001), relative contrast (β=0.93, OR=2.53, P<0.001), and dot gain (β=0.57, OR=1.77, P<0.001) as core positive predictors. RF importance analysis indicated that solid density (importance weight of 0.31) had the most significant impact on print quality, followed by relative contrast (0.25) and dot gain (0.22). The cumulative contribution of these three factors reached 78%. BHM confirmed main effects of solid density (post. mean 0.80, 95% HDI [0.65, 0.95]) and relative contrast (post. mean 0.91, 95% HDI [0.73, 1.09]) and their significant interaction (β=0.42). The synergistic model achieved 84.7% accuracy, a 6.4% improvement over OLR alone (78.3%), with superior discrimination for excellent grade (AUC=0.88). Comprehensive ranking correlated significantly with CQS (r≈0.82) but emphasized parameter synergy. The BHM-OLR-RF synergistic framework integrates model strengths, significantly enhancing objectivity, accuracy, and interpretability of print quality evaluation. It precisely quantifies core parameter impacts and importance, resolving subjectivity and weighting issues in traditional methods and providing a scientific basis and decision support for multi-parameter process optimization and mechanical improvement.
Key words
print quality evaluation /
Bayesian Hierarchical Model (BHM) /
Ordinal Logistic Regression (OLR) /
random forest
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