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<p>To ensure high quality castings, the ability to accurately quantify an as-cast surface’s characteristics is of vital importance to the foundry industry. In addition, recent advancements in non-contact measurement systems have provided new opportunities to quantify a casting’s surface beyond traditional roughness measurements. However, in the realm of non-contact measurement systems, there are numerous methodologies and metrics for evaluating and quantifying a surface. This paper investigates the critical surface features and approaches necessary for effective surface quality classification. More specifically, this study compares the accuracy of spatial statistical, modern digital processing techniques (e.g., convolution neural network) to classify as-cast surfaces. Through this comparison, this paper aims to develop a methodology to provide the most reliable results to industry. The findings will help guide the foundry industry in adopting the most appropriate techniques for surface quality assessment, ultimately enhancing product quality.</p>

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Integrating Spatial Statistics and Digital Processing for Enhanced Surface Quality Classification in the Foundry Industry

  • 期刊论文

  • 发表日期：

    2025年6月18日

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  • Ronit Shetty,
  • Ahmad Al Majali,
  • Lee Wells

摘要

To ensure high quality castings, the ability to accurately quantify an as-cast surface’s characteristics is of vital importance to the foundry industry. In addition, recent advancements in non-contact measurement systems have provided new opportunities to quantify a casting’s surface beyond traditional roughness measurements. However, in the realm of non-contact measurement systems, there are numerous methodologies and metrics for evaluating and quantifying a surface. This paper investigates the critical surface features and approaches necessary for effective surface quality classification. More specifically, this study compares the accuracy of spatial statistical, modern digital processing techniques (e.g., convolution neural network) to classify as-cast surfaces. Through this comparison, this paper aims to develop a methodology to provide the most reliable results to industry. The findings will help guide the foundry industry in adopting the most appropriate techniques for surface quality assessment, ultimately enhancing product quality.