Constraint-based evolutionary learning approach to the non-normal process performance evaluation
- Authors: Ahmad, S. , Huda, Shamsul , Bakir, S. , Abdollahian, Mali , Zeephongsekul, P.
- Date: 2009
- Type: Text , Conference paper
- Relation: Paper presented at 3rd International Conference on Informatics and Technology 2009: ICT Opportunities in the Current Global Recession, Kuala Lumpur, Malaysia : 27th-28th October 2009 p. 26-33
- Full Text: false
- Description: Performance of industrial products is very important for an industry. Conventional methods for performance analysis consider a normality assumption and limited to low dimensional data. Different manufacturing processes very often have products with quality characteristics that do not follow normal distribution. In such cases fitting a known non-normal distribution to these quality characteristics would lead to erroneous results while assessing the performance of products. In this paper, we propose a novel method for non-normal multivariate process performance analysis. We have proposed a Constraint-based Evolutionary Algorithm (EA) approach for optimal estimation of the parameters of non-normal multivariate process. Furthermore, a geometric distance based method has been employed to reduce higher dimensionality of data to lower dimension. The efficacy of the proposed method is assessed by using the proportion of nonconformance (PNC) criterion to summarize the performance of EA approach. The experimental results from constraint-based EA have been compared to those obtained using steepest descent and simulated annealing (SA) approaches.
- Description: 2003007898
Process performance evaluation using evolutionary algorithm
- Authors: Ahmad, S. , Huda, Shamsul , Bakir, S. , Abdollahian, Mali , Zeephongsekul, P.
- Date: 2009
- Type: Text , Conference paper
- Relation: Paper presented at 2009 International Conference on Information & Knowledge Engineering, IKE 2009, Las Vegas, Nevada, U.S.A. : 13th-16th July 2009 p. 731-737
- Full Text:
- Description: Nowadays every business is using different quantitative measures and techniques to assess performance of their products / services. It is well known that different manufacturing processes very often manufacture products with quality characteristics that do not follow normal distribution. In such cases, fitting a known non-normal distribution to these quality characteristics would lead to erroneous results. Furthermore, there is always more than one characteristic Critical to Quality (CTQ) in the process outcomes and very often these quality characteristics are correlated with each other. In this paper, we assess performance of such a bivariate process data which is non-normal as well as correlated. We will use the geometric distance approach to reduce the dimension of the correlated non-normal bivariate data and then fit Burr distribution to the geometric distance variable. The optimal parameters of the fitted Burr distribution are estimated using Evolutionary Algorithm (EA). The results are compared with those using Simulated Annealing (SA) algorithm. The proportion of nonconformance (PNC) for process measurements is then obtained by using the fitted Burr distributions based on the two methods. The results based on both search algorithms are then compared with the exact proportion of nonconformance of the data. Finally, a case study using real data is presented.
- Description: 2003008140