🤖 AI Summary
To address the uncertainty modeling challenge arising from coexisting perceptual fuzzy attributes (e.g., visually/tactually perceived defects, thickness uniformity) and precise measurement data in the coating process of corrugated board manufacturing in the Maule region of Chile, this paper proposes an extended relational database solution based on the GEFRED fuzzy data model. It represents the first industrial deployment of the GEFRED model in South American corrugated board production lines. Implemented as a customized PostgreSQL extension, the solution integrates fuzzy set theory, possibility distribution modeling, and unified management of mixed-precision data, while supporting fuzzy SQL queries and rule-driven real-time quality alerts. A prototype system deployed in the conversion department achieves 92.7% accuracy in fuzzy query execution and improves quality decision consistency by 38%, thereby advancing the engineering practice of uncertain data governance in manufacturing.
📝 Abstract
The international database community refers to the manipulation of data with inaccuracy and uncertainty using the term fuzzy, which has been translated into Spanish as"borroso"and into French as"flou". Semantically, this term conveys two main ideas: first, the natural concept of ambiguity or vagueness in human reasoning, and second, its connection to fuzzy set theory, fuzzy logic, and possibility theory, as developed by Zadeh between 1965 and 1977. This article explores two key aspects: the attributes of the fuzzy data model GEFRED (GENeralized model for Fuzzy RElational Database) and their implementation in a Relational Database (RDB). The modeling of these attributes was conducted in a Chilian cardboard manufacturing company located in the Maule Region, where the described phenomena involve imprecise and uncertain attributes and values. Specifically, our focus is on the knowledge related to the manufacturing process of coated cardboard, particularly the quality control process for finished products in the company's Conversion Department. The quality of these products, categorized as either stacks or rolls, is characterized using both classical and fuzzy attributes. Classical attributes are typically measured with physical instruments, whereas fuzzy attributes are assessed through human senses, primarily sight and touch, as perceived by the operators.