A fuzzy development for attribute control chart with Monte Carlo simulation method

Abstract

This paper presents the case study of fuzzy statistical process control which has been simulated for variable and discontinuous production within a particular time frame in a key manufacturing workshop. In order to reduce waste production and increase productivity, dimensional inspection from raw product is categorized into three groups: product of type A, product of type B, and discard. In first part, the appearance characteristics of product is defined as fuzzy membership function as the input of the system in order to allocate the output obtained from fuzzy inference of product to one of the three quality levels. Afterwards, each quality level is assigned to its own group by means of Monte Carlo simulation techniques. In the second part, with fuzzy development of a multinomial p chart, the production process is illustrated as a control chart within the particular period of time.

Design of a Control Chart Based on COM‐Poisson Distribution for the Uncertainty Environment

“…Reference [21] presented the algorithm for the control chart under the fuzzy logic. Reference [22] studied the fuzzy attribute chart using Monte Carlo simulation. More information on the application of fuzzy logic can be seen in [23][24][25].…”

Fazi Kontrol Grafi̇kleri̇ İle Klasi̇k Kontrol Grafi̇kleri̇ni̇n Karşilaştirilmasi Üzeri̇ne Bi̇r Deneme

“…Sonuç olarak, işletmelere dilsel değişkenlerin kullanıldığı durumlarda klasik kontrol grafikleri yerine fazi kontrol grafiklerini kullanmaları önerilmiştir. Madadi ve Mahmoudzadeh (2017), fazi kontrol grafiklerinin uygulanabilirliği üzerine bir çalışma yapmışlardır. Çalışma kapsamında, oldukça hassas ve olağandışı olarak kabul edilen anahtar üretim atölyesinde uygulama gerçekleştirmişlerdir.…”

Solving Fuzzy Attribute Quality Control Charts with proposed Ranking Function

“…P.Fernández and other IR (2015), the use of fuzzy control charts becomes inevitable when statistical data considered are vague or affected by uncertainty [12]. M.Hadi and M.Mahmoudzadeh (2017), presented the fuzzy statistical process control development for attribute quality control chart by using Monte Carlo simulation method [13]. The aim of the study is applying the crisp control chart and fuzzy control chart for real data by utilizing of triangular membership function.…”