Systematic monitoring of defects in castings produced by high-pressure die casting (HPDC) is essential to ensure the production of high-quality components, particularly given their growing prevalence in safety-critical automotive applications. However, conventional analysis techniques may not fully capture the complex dynamics of the process, resulting in limited effectiveness and an inability to continuous improvement. This study therefore proposes an innovative methodology that autonomously performs pre-processing by transforming tabular data into images using the REFINED (Representation of Features as Images with Neighbourhood Dependencies) approach. This transformation enables convolutional neural networks (CNNs) to recognise hidden patterns and dependencies in the data. Additionally, Explainable Artificial Intelligence (XAI) principles were employed to elucidate the influence of process parameters on porosity formation, by the Local Interpretable Model-Agnostic Explanations (LIME) method application. Integrating these techniques improves autonomous decision-making processes for quality control in line with Industry 4.0 and Quality 4.0 principles, establishing a new paradigm for analysing HPDC process data.