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Process Capability Assessment and Surface Quality Monitoring in Cathodic Electrodeposition of S235JRC+N Electric-Charging Station.

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Title: Process Capability Assessment and Surface Quality Monitoring in Cathodic Electrodeposition of S235JRC+N Electric-Charging Station.
Authors: Piroh, Martin1 (AUTHOR), Peti, Damián1,2 (AUTHOR) damian.peti@tuke.sk, Fejko, Patrik1 (AUTHOR), Gombár, Miroslav2 (AUTHOR), Hatala, Michal1 (AUTHOR)
Source: Materials (1996-1944). Jan2026, Vol. 19 Issue 2, p330. 25p.
Subjects: Process capability, Thickness measurement, Statistics, Electroplating, Machine learning, Electric vehicle charging stations, Surface finishing, Quality assurance
Abstract: This study presents a statistically robust quality-engineering evaluation of an industrial cathodic electrodeposition (CED) process applied to large electric-charging station components. In contrast to predominantly laboratory-scale studies, the analysis is based on 1250 thickness measurements, enabling reliable assessment of process uniformity, positional effects, and long-term stability under real production conditions. The mean coating thickness was specified at 21.84 µm with a standard deviation of 3.14 µm, fully within the specified tolerance window of 15–30 µm. One-way ANOVA revealed statistically significant but technologically small inter-station differences (F(49, 1200) = 3.49, p < 0.001), with an effect size of η2 ≈ 12.5%, indicating that most variability originates from inherent within-station common causes. Shewhart X¯–R–S control charts confirmed process stability, with all subgroup means and dispersions well inside the control limits and no evidence of special-cause variation. Distribution tests (χ2, Kolmogorov–Smirnov, Shapiro–Wilk, Anderson–Darling) detected deviations from perfect normality, primarily in the tails, attributable to the superposition of slightly heterogeneous station-specific distributions rather than fundamental non-Gaussian behaviour. Capability and performance indices were evaluated using Statistica and PalstatCAQ according to ISO 22514; the results (Cp = 0.878, Cpk = 0.808, Pp = 0.797, Ppk = 0.726) classify the process as conditionally capable, with improvement potential mainly linked to reducing positional effects and centering the mean closer to the target thickness. To complement the statistical findings, an AIAG–VDA FMEA was conducted across the entire value stream. The highest-risk failure modes—surface contamination, incorrect bath chemistry, and improper hanging—corresponded to the same mechanisms identified by SPC and ANOVA as contributors to thickness variability. Proposed corrective actions reduced RPN values by 50–62.5%, demonstrating strong potential for capability improvement. A predictive machine-learning model was implemented to estimate layer thickness and successfully reproduced the global trend while filtering process-related noise, offering a practical tool for future predictive quality control. [ABSTRACT FROM AUTHOR]
Copyright of Materials (1996-1944) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Process Capability Assessment and Surface Quality Monitoring in Cathodic Electrodeposition of S235JRC+N Electric-Charging Station.
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  Data: &lt;searchLink fieldCode=&quot;JN&quot; term=&quot;%22Materials+%281996-1944%29%22&quot;&gt;Materials (1996-1944)&lt;/searchLink&gt;. Jan2026, Vol. 19 Issue 2, p330. 25p.
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– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This study presents a statistically robust quality-engineering evaluation of an industrial cathodic electrodeposition (CED) process applied to large electric-charging station components. In contrast to predominantly laboratory-scale studies, the analysis is based on 1250 thickness measurements, enabling reliable assessment of process uniformity, positional effects, and long-term stability under real production conditions. The mean coating thickness was specified at 21.84 &#181;m with a standard deviation of 3.14 &#181;m, fully within the specified tolerance window of 15–30 &#181;m. One-way ANOVA revealed statistically significant but technologically small inter-station differences (F(49, 1200) = 3.49, p &lt; 0.001), with an effect size of η2 ≈ 12.5%, indicating that most variability originates from inherent within-station common causes. Shewhart X&#175;–R–S control charts confirmed process stability, with all subgroup means and dispersions well inside the control limits and no evidence of special-cause variation. Distribution tests (χ2, Kolmogorov–Smirnov, Shapiro–Wilk, Anderson–Darling) detected deviations from perfect normality, primarily in the tails, attributable to the superposition of slightly heterogeneous station-specific distributions rather than fundamental non-Gaussian behaviour. Capability and performance indices were evaluated using Statistica and PalstatCAQ according to ISO 22514; the results (Cp = 0.878, Cpk = 0.808, Pp = 0.797, Ppk = 0.726) classify the process as conditionally capable, with improvement potential mainly linked to reducing positional effects and centering the mean closer to the target thickness. To complement the statistical findings, an AIAG–VDA FMEA was conducted across the entire value stream. The highest-risk failure modes—surface contamination, incorrect bath chemistry, and improper hanging—corresponded to the same mechanisms identified by SPC and ANOVA as contributors to thickness variability. Proposed corrective actions reduced RPN values by 50–62.5%, demonstrating strong potential for capability improvement. A predictive machine-learning model was implemented to estimate layer thickness and successfully reproduced the global trend while filtering process-related noise, offering a practical tool for future predictive quality control. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: &lt;i&gt;Copyright of Materials (1996-1944) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder&#39;s express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.&lt;/i&gt; (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.3390/ma19020330
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 25
        StartPage: 330
    Subjects:
      – SubjectFull: Process capability
        Type: general
      – SubjectFull: Thickness measurement
        Type: general
      – SubjectFull: Statistics
        Type: general
      – SubjectFull: Electroplating
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Electric vehicle charging stations
        Type: general
      – SubjectFull: Surface finishing
        Type: general
      – SubjectFull: Quality assurance
        Type: general
    Titles:
      – TitleFull: Process Capability Assessment and Surface Quality Monitoring in Cathodic Electrodeposition of S235JRC+N Electric-Charging Station.
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            NameFull: Piroh, Martin
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            NameFull: Peti, Damián
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            NameFull: Fejko, Patrik
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            NameFull: Gombár, Miroslav
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            NameFull: Hatala, Michal
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              M: 01
              Text: Jan2026
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              Y: 2026
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              Value: 19
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            – TitleFull: Materials (1996-1944)
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