Modelling Uncertainty in Process Capability Analysis: A Fuzzy Logic Perspective
2025 International Conference on Decision Aid Sciences and Applications, DASA 2025, Manama, Bahreyn, 1 - 02 Aralık 2025, ss.246-250, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/dasa68193.2025.11498873
- Basıldığı Şehir: Manama
- Basıldığı Ülke: Bahreyn
- Sayfa Sayıları: ss.246-250
- Anahtar Kelimeler: fuzzy logic, process capability analysis, process capability index, SWOT
- Yıldız Teknik Üniversitesi Adresli: Evet
Özet
Process Capability Analysis (PCA) is a critical statistical methodology used to evaluate whether manufacturing processes are capable based on producing outputs within predefined quality limits. Among its key metrics, Process Capability Indices (PCIs) such as Cp, Cpk, Cpm and Cpmk provide quantitative measures of a process' performance relative to specification limits (SLs). However, traditional PCA and PCIs assume precise, crisp data, which limits their applicability in real-world environments where uncertainty, imprecision, and vagueness frequently arise due to measurement variability, subjective assessments, or lack of complete information. To address these challenges, the fuzzy set theory (FST) has been integrated into PCA to model imprecision and ambiguity more effectively. This study presents a detailed literature analysis focusing on the integration of FST into PCA, offering a more flexible and robust framework to manage uncertainties. The main idea is not only analyzing the current state of the literature but also identifies critical gaps and opportunities, encouraging researchers to explore richer models, broader applications, and more robust methodological integrations in the future.