Fuzzy process capability indices for simple linear profile

2019 ◽  
Vol 47 (12) ◽  
pp. 2136-2158
Author(s):  
Zainab Abbasi Ganji ◽  
Bahram Sadeghpour Gildeh
Keyword(s):  
Process Capability ◽  
Process Capability Indices ◽  
Linear Profile ◽  
Fuzzy Process ◽  
Capability Indices ◽  
Fuzzy Process Capability

scholarly journals Measuring Loss-Based Process Capability IndexLeand Its GenerationLe′′with Fuzzy Numbers

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Mohammad Abdolshah
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Numbers ◽  
Process Capability ◽  
Process Capability Index ◽  
Process Capability Indices ◽  
Capability Index ◽  
Fuzzy Process ◽  
Capability Indices ◽  
Fuzzy Process Capability ◽  
The Membership Function

Loss-based process capability indices are appropriate and realistic tools in order to measure the process capability. Among them, indexLeand its generationLe′′are well-known loss-based process capability indices, whose concepts are based on the worth (the opposite concept of loss). Sometimes, in order to calculateLeandLe′′there are some uncertainties in observations, so fuzzy logic can be employed to manage the uncertainties. This paper investigates fuzzification of process capability indexL~eand its generationL~e′′. In order to find the membership function of process capability indicesL~eandL~e′′, theα-cuts of fuzzy observation were employed. Then with an example of fuzzy process capability index,L~eandL~e′′were calculated and compared. Results showed that fuzzyL~e′′was more sensitive compared withL~eand was increased while the target departs (asymmetric tolerance). This example also showed that, with departure from the target, variation of fuzzyL~e′′and consequently its fuzziness were increased.

scholarly journals Measuring process capability index C pmk with fuzzy data and compare it with other fuzzy process capability indices

2011 ◽  
Vol 38 (6) ◽  
pp. 6452-6457 ◽  
Author(s):  
Mohammad Abdolshah ◽  
Rosnah Mohd. Yusuff ◽  
Tang Sai Hong ◽  
Md. Yusof B. Ismail ◽  
Aghdas Naimi Sadigh
Keyword(s):  
Process Capability ◽  
Process Capability Index ◽  
Process Capability Indices ◽  
Fuzzy Data ◽  
Measuring Process ◽  
Capability Index ◽  
Fuzzy Process ◽  
Capability Indices ◽  
Fuzzy Process Capability

scholarly journals Process Capability Indices as Fuzzy Numbers

2016 ◽  
Vol 34 (4) ◽  
Author(s):  
Abbas Parchami ◽  
Mashaallah Mashinchi ◽  
Ali Reza Yavari ◽  
Hamid Reza Maleki
Keyword(s):  
Fuzzy Numbers ◽  
Process Capability ◽  
Process Capability Indices ◽  
Manufacturing Processes ◽  
Membership Functions ◽  
Specification Limits ◽  
Fuzzy Process ◽  
Capability Indices ◽  
Fuzzy Specification Limits ◽  
Fuzzy Process Capability

Most of the traditional methods for assessing the capability of manufacturing processes are dealing with crisp quality. In this paper we discuss the fuzzy quality and introduce fuzzy process capability indices, where instead of precise quality we have two membership functions for specification limits. These indices are necessary when the specification limits are fuzzy and they are helpful for comparing manufacturing processes with fuzzy specification limits. Some interesting relations among the introduced indices are obtained. Numerical examples are given to clarify the method.

A New Fuzzy Process Capability Index for Asymmetric Tolerance Interval

2017 ◽  
Vol 6 (3) ◽  
pp. 74-104 ◽  
Author(s):  
Zainab Abbasi Ganji ◽  
Bahram Sadeghpour Gildeh
Keyword(s):  
Process Capability ◽  
Process Capability Indices ◽  
Tolerance Interval ◽  
Tolerance Intervals ◽  
Specification Limits ◽  
Target Value ◽  
Fuzzy Process ◽  
Capability Indices ◽  
And Performance ◽  
Fuzzy Process Capability

Process capability indices are used to evaluate the performance of the manufacturing process. When the specification limits and the target value are not precise, the authors cannot use the traditional methods to assess the capability of the process. For the processes with asymmetric tolerance intervals, some fuzzy process capability indices have been introduced such as and . In some cases, these indices may fail to account the process performance. To overcome the problem with them, the authors propose two new fuzzy indices in the case that the specification limits and the target value are fuzzy while the data are crisp. Also, the authors present an application example to demonstrate effectiveness and performance of the proposed indices.

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