9786209808722 - Non-conformity detection system for manufacturing processes: Using computer intelligence techniques di Ferreira De Lima, Roberto; Duarte Almeida, Rivanildo; Kiyoshi... (8 risultati)

Condizione: New.

Taschenbuch. Condizione: Neu. Non-conformity detection system for manufacturing processes | Using computer intelligence techniques | Roberto Ferreira de Lima (u. a.) | Taschenbuch | Englisch | 2026 | Our Knowledge Publishing | EAN 9786209808722 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu.

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 64 pp. Englisch.

Paperback. Condizione: new. Paperback. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 64 pp. Englisch.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.