Non-conformity detection system for manufacturing processes: Using computer intelligence techniques

In an increasingly competitive industrial environment, detecting faults quickly and accurately has become a decisive factor in guaranteeing quality, reliability and operational performance. This book presents the development of an Intelligent Non-Conformity Detection System based on Fuzzy Logic, designed to analyze quality indicators in real time and convert production data into clear and structured risk diagnoses.Using the PPM (Parts Per Million) indicator and fuzzy modeling techniques, the system classifies process performance from levels of excellence to critical situations, promoting gradual transitions and greater analytical sensitivity compared to traditional deterministic methods. The model's structure takes into account pertinence functions and rules based on expert knowledge, enabling assessments that are closer to industrial reality.Applied to processes in the automotive sector, the model shows how computational intelligence can improve quality control, reduce operational risks, support strategic decisions and strengthen continuous improvement programs in industrial manufacturing.