A fuzzy logic approach for dealing with qualitative quality characteristics of a process

被引：46

作者：

Tahera, K. ^{[1
]}

Ibrahim, R. N. ^{[1
]}

Lochert, P. B. ^{[1
]}

机构：

[1] Monash Univ, Dept Mech Engn, Clayton, Vic 3800, Australia

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2008年 / 34卷 / 04期

关键词：

fuzzy logic; process adjustment model; qualitative quality characteristic; process mean; production run;

D O I：

10.1016/j.eswa.2007.05.025

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Dealing with qualitative information is quite common in real life problems. So far research focused on developing process adjustment models only for quantitative data. This paper presents a process adjustment approach of a deteriorating process in which quality characteristics are expressed in qualitative form. This approach jointly determines the initial setting of process mean and production run. A fuzzy logic is adopted to implement the process adjustment approach. The features of this approach are lack of mathematical complexity and ability to deal with qualitative data. Detailed implementation of the fuzzy process adjustment model is also given in this paper. (c) 2007 Elsevier Ltd. All rights reserved.

引用

页码：2630 / 2638

页数：9

共 16 条

[1] Designing the optimal process means and the optimal production run for a deteriorating process [J].

Chan, W. M. ;

Ibrahim, R. N. .

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2006, 31 (3-4) :367-373

[2] Determination of the optimal production run and the most profitable process mean for a production process [J].

Chen, SL ;

Chung, KJ .

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 1996, 34 (07) :2051-2058

[3] Joint determination of target value and production run for a process with multiple markets [J].

Hariga, MA ;

Al-Fawzan, MA .

INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS, 2005, 96 (02) :201-212

[4] A hybrid artificial intelligence approach for improving yield in precious stone manufacturing [J].

Holden, T ;

Serearuno, M .

JOURNAL OF INTELLIGENT MANUFACTURING, 2005, 16 (01) :21-38

[5] A fuzzy expert system for optimizing parameters and predicting performance measures in hard-milling process [J].

Iqbal, Asif ;

Ning He ;

Liang Li ;

Dar, Naeem Ullah .

EXPERT SYSTEMS WITH APPLICATIONS, 2007, 32 (04) :1020-1027

[6]

KACPRZYK J, 1999, LINGUISTIC SUMMARIES, P937

[7] Fuzzy multicriteria models for quality function deployment [J].

Kim, KJ ;

Moskowitz, H ;

Dhingra, A ;

Evans, G .

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2000, 121 (03) :504-518

[8]

Lin CT, 1996, Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems

[9]

PAKALA TPM, 2000, INT J RELIABILITY QU, V6, P335

[10] Integrated model for determining the optimal initial settings of the process mean and the optimal production run assuming quadratic loss functions [J].

Rahim, MA ;

Tuffaha, F .

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2004, 42 (16) :3281-3300

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