Surface roughness is a quality-critical attribute in industrial zinc phosphating, directly affecting sealing performance, coating uniformity, dimensional tolerances, and first-pass production yield in automotive pretreatment lines. While the chemical mechanisms of phosphate coating formation are well understood, the translation of this knowledge into statistically defensible, production-scale prioritization of bath chemistry control levers under real manufacturing constraints remains limited, particularly with respect to surface roughness stability and its environmental implications. This study investigates surface roughness control in a fully operational industrial zinc phosphating line by systematically evaluating the effects of pickling acid chemistry (H2SO4 versus H3PO4), dissolved ferrous iron (Fe2+) levels in pickling and phosphating baths, and nitrate accelerator dosage. A Taguchi L16 (24) experimental design was implemented under real manufacturing constraints. Surface roughness (Rz) was measured in accordance with ISO 4287 and analyzed using a general linear model supported by partial effect size estimation (ηp2) and bootstrap confidence intervals. This approach enables statistically robust ranking of dominant and secondary control parameters, rather than qualitative trend confirmation alone. The robustness of statistically identified trends was independently verified using paired measurements from 25 production components, while scanning electron microscopy provided qualitative mechanistic support. The results demonstrate that pickling acid chemistry and nitrate accelerator dosage are the dominant control parameters governing surface roughness stability, whereas Fe2+ concentration does not act as a primary independent driver within the defined Fe2+ concentration ranges investigated in this study, but contributes through interaction-dependent mechanisms. Phosphoric acid pickling combined with nitrate acceleration consistently yields lower and more stable roughness values. In addition, roughness-related nonconformities were translated into product carbon footprint outcomes using an ISO 14067–aligned, gate-to-gate framework with Monte Carlo uncertainty analysis, explicitly quantifying the carbon footprint penalties associated with quality-driven rework and external return logistics under industrial production conditions.
Deniz et al. (Thu,) studied this question.