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This paper introduces a novel approach for generating new three-dimensional ceramic models through a rule-based system by integrating procedural generation methods. This system ensures structural integrity and manufacturability in creating diverse ceramic pieces, supports extensive customization and allows the generation of unique ceramic collections and the replication of ceramic pieces. Given the handiness of the method, a three-dimensional ceramic tableware dataset was created and made available for use in several fields of research. The system was made available through a web-based application that enhances user interaction and integrates augmented reality, offering a more immersive experience and allowing users to visualize the generated models overlaid in real environments. This work bridges the gap between digital design and physical production, creating a robust system for designing, customizing, replicating, and visualizing ceramic designs, benefiting both industry professionals and consumers and showing significant promise to be adapted to other industrial fields, potentially transforming how these industries approach automated design.<\/jats:p>","DOI":"10.1007\/s10055-025-01194-1","type":"journal-article","created":{"date-parts":[[2025,8,13]],"date-time":"2025-08-13T16:03:21Z","timestamp":1755101001000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Rule-based generative modeling in ceramic tableware industry applications"],"prefix":"10.1007","volume":"29","author":[{"given":"F\u00e1bio","family":"Gaspar","sequence":"first","affiliation":[]},{"given":"Daniel","family":"Carreira","sequence":"additional","affiliation":[]},{"given":"Nuno","family":"Rodrigues","sequence":"additional","affiliation":[]},{"given":"Rolando","family":"Miragaia","sequence":"additional","affiliation":[]},{"given":"Jos\u00e9","family":"Ribeiro","sequence":"additional","affiliation":[]},{"given":"Carlos","family":"Grilo","sequence":"additional","affiliation":[]},{"given":"Ant\u00f3nio","family":"Pereira","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,8,13]]},"reference":[{"key":"1194_CR1","unstructured":"Achlioptas P, Diamanti O, Mitliagkas I, Guibas L (2018) Learning representations and generative models for 3D point clouds (pp. 40\u201349). 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