Use of Fuzzy Sets in Process Capability Analysis: A Comparative Literature Analysis


Gündoğdu F. K., İLBAHAR E., YAZIR K., KARAŞAN A., KAYA İ.

International Conference on Intelligent and Fuzzy Systems, INFUS 2024, Çanakkale, Türkiye, 16 - 18 Temmuz 2024, cilt.1089 LNNS, ss.260-268 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 1089 LNNS
  • Doi Numarası: 10.1007/978-3-031-67195-1_31
  • Basıldığı Şehir: Çanakkale
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.260-268
  • Anahtar Kelimeler: Fuzzy set theory, Linguistic variables, Process capability indices
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

Process capability analysis (PCA) is the assessment of a process’s ability to meet customer requirements specified by constraints. It is a crucial aspect of statistical process control used to evaluate process variability. PCA provides data on both conforming and nonconforming production rates, which represent the quantity of items that meet and do not meet SLs, respectively. Processes can be categorized as “capable”, or “incapable” based on their process capability indices (PCIs). The fuzzy set theory can effectively address ambiguity and enhance flexibility and sensitivity in classical PCIs. To achieve this goal, upper and lower specification limitations can be represented using linguistic variables. Fuzzy process capability indexes (FPCIs) are generated by utilizing fuzzy mean and fuzzy variance. This study aims to provide an extensive literature evaluation on publications about FPCIs. The research was assessed based on many parameters. The study presented classifications such as FPCI, application area, fuzzy parameters, and type of fuzzy sets. We aimed to create an outline for researchers in this subject and discuss recent advancements in FPCIs and fuzzy PCA.