Statistical Process Control for Technical Cleanliness in Automotive Die-Casting: A Data-Driven Framework for Particle Contamination Monitoring and Reduction

This study proposes and validates a data-driven statistical process control (SPC) framework for monitoring and reducing particle contamination in automotive aluminium die-casting, addressing a documented gap in the structured application of inferential statistics to technical cleanliness management in automotive SMEs. An action research strategy based on DMAIC and CRISP-DM was conducted over eleven months in an automotive die-casting SME. The study encompassed a VDA 19.1/19.2-aligned audit, descriptive analysis of 64 pre-intervention samples, negative binomial regression with Wald hypothesis testing, Individual Moving Range (I-MR) control charts for before-and-after comparison, and microscopic characterisation of out-of-specification particles using Microsoft Excel, Minitab, and RStudio. The negative binomial model consistently outperformed Poisson regression across all granulometric classes (ΔAIC up to 2958.77). Mould cavity, injection machine condition, and injection operator were the dominant contamination predictors. Statistically significant variability reductions were achieved for total particles in the 150–400 µm range and for metallic particles in the 150–200 µm and 200–400 µm classes; the metallic 200–400 µm class also showed a significant mean reduction. Particles exceeding 400 µm remained dominated by sporadic special-cause events. The study is restricted to one component and site; the post-intervention sample (n = 17) limits statistical power, and extension to additional components, processes, and larger datasets is recommended. The reported findings should accordingly be read as case-study evidence rather than as generalisable, definitive conclusions. The framework offers a replicable, cost-effective approach to data-driven quality management for automotive SMEs, aligned with Industry 5.0 human-centric manufacturing principles. This paper bridges a literature gap by integrating negative binomial regression, Wald testing, and I-MR control charts into a unified SPC framework for technical cleanliness in automotive die-casting, connecting quality intelligence with the transversalities of artificial intelligence, innovation, and sustainability. The methodology itself relies on classical inferential statistics and SPC rather than on artificial intelligence algorithms; its Industry 5.0 relevance lies in providing the structured, human-centric, data-driven decision-making foundation on which future AI-assisted particle classification and real-time monitoring can be built.