International Journal of Science, Engineering and TechnologyAuthors: Mohammad Arhum A Haque
Abstract: Wall thickness analysis is a critical step in product design because it directly affects strength, manufacturability, weight, material usage, cooling behaviour, and overall cost. In casting, molding, and similar manufacturing processes, uneven wall thickness can lead to sink marks, warpage, shrinkage, weak sections, and unnecessary material buildup. Traditionally, this check was performed through manual sectioning and visual measurement, making the process slow, repetitive, and highly dependent on the designer’s experience. Geom-Caliper improved this workflow by enabling automated wall thickness inspection directly on three-dimensional computer-aided design models, allowing faster and more repeatable identification of thin and thick regions. However, optimization of wall thickness still depends largely on manual interpretation, engineering judgment, and repeated trial-and-error modifications. This research proposes an artificial intelligence-based framework to optimize wall thickness analysis through Geom-Caliper. The objective is not to replace Geom-Caliper, but to extend its capability by combining accurate geometric thickness measurement with intelligent prediction. Geom-Caliper is used to generate reference thickness data from CAD models, while artificial intelligence is trained to learn patterns from past models and identify regions likely to become thin, thick, or manufacturability critical. For this purpose, the three-dimensional geometry is converted into voxel-based data so that a three-dimensional convolutional neural network can learn spatial relationships between shape features and wall thickness behaviour. The proposed framework supports early detection of problematic zones before final design validation, reducing manual review effort, improving design consistency, and supporting efficient material usage for advanced manufacturability engineering.
DOI: http://doi.org/
