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<p>In the foundry industry, the quality of a cast part is often assessed by its surface roughness. This paper proposes a comprehensive framework for predicting surface roughness, in real-time, using digital images. The key to this framework is that the model used to perform these predictions is constructed offline from both digital images and point cloud data. This model, which integrates multilinear principal component analysis for feature extraction with convolutional neural networks for prediction, is used online with only the need for image data. For this approach to be viable in industry, it must be accurate across a wide range of surface types. Therefore, these predictions models are developed from two disparate surfaces, namely, sandpaper and C-9 comparator plates. To assess the robustness of the approach to multiple surfaces, two predictive modelling strategies are compared; (1) One model that created from both surface types and (2) Two individual models created separately for each surface type. This approach demonstrates the potential for integrating multi-sensor data to enhance surface quality assessments in the foundry industry without sacrificing cycle times.</p>

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Predicting Surface Roughness in Foundry Applications Through MPCA and Convolutional Neural Networks

  • 期刊论文

  • 发表日期：

    2025年5月15日

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

摘要

In the foundry industry, the quality of a cast part is often assessed by its surface roughness. This paper proposes a comprehensive framework for predicting surface roughness, in real-time, using digital images. The key to this framework is that the model used to perform these predictions is constructed offline from both digital images and point cloud data. This model, which integrates multilinear principal component analysis for feature extraction with convolutional neural networks for prediction, is used online with only the need for image data. For this approach to be viable in industry, it must be accurate across a wide range of surface types. Therefore, these predictions models are developed from two disparate surfaces, namely, sandpaper and C-9 comparator plates. To assess the robustness of the approach to multiple surfaces, two predictive modelling strategies are compared; (1) One model that created from both surface types and (2) Two individual models created separately for each surface type. This approach demonstrates the potential for integrating multi-sensor data to enhance surface quality assessments in the foundry industry without sacrificing cycle times.