Gülser Köksal

Review: A review of data mining applications for quality improvement in manufacturing industry

Many quality improvement (QI) programs including six sigma, design for six sigma, and kaizen require collection and analysis of data to solve quality problems. Due to advances in data collection systems and analysis tools, data mining (DM) has widely been applied for QI in manufacturing. Although a few review papers have recently been published to discuss DM applications in manufacturing, these only cover a small portion of the applications for specific QI problems (quality tasks). In this study, an extensive review covering the literature from 1997 to 2007 and several analyses on selected quality tasks are provided on DM applications in the manufacturing industry. The quality tasks considered are; product/process quality description, predicting quality, classification of quality, and parameter optimisation. The review provides a comprehensive analysis of the literature from various points of view: data handling practices, DM applications for each quality task and for each manufacturing industry, patterns in the use of DM methods, application results, and software used in the applications are analysed. Several summary tables and figures are also provided along with the discussion of the analyses and results. Finally, conclusions and future research directions are presented.

CMARS: a new contribution to nonparametric regression with multivariate adaptive regression splines supported by continuous optimization

Regression analysis is a widely used statistical method for modelling relationships between variables. Multivariate adaptive regression splines (MARS) especially is very useful for high-dimensional problems and fitting nonlinear multivariate functions. A special advantage of MARS lies in its ability to estimate contributions of some basis functions so that both additive and interactive effects of the predictors are allowed to determine the response variable. The MARS method consists of two parts: forward and backward algorithms. Through these algorithms, it seeks to achieve two objectives: a good fit to the data, but a simple model. In this article, we use a penalized residual sum of squares for MARS as a Tikhonov regularization problem, and treat this with continuous optimization technique, in particular, the framework of conic quadratic programming. We call this new approach to MARS as CMARS, and consider it as becoming an important complementary and model-based alternative to the backward stepwise algorithm. The performance of CMARS is also evaluated using different data sets with different features, and the results are discussed.

Classification models based on Tanaka’s fuzzy linear regression approach: The case of customer satisfaction modeling

Fuzzy linear regression (FLR) approaches are widely used for modeling relations between variables that involve human judgments, qualitative and imprecise data. Tanaka’s FLR analysis is the first one developed and widely used for this purpose. However, this method is not appropriate for classification problems, because it can only handle continuous type dependent variables rather than categorical. In this study, we propose three alternative approaches for building classification models, for a customer satisfaction survey data, based on Tanaka’s FLR approach. In these models, we aim to reflect both random and fuzzy types of uncertainties in the data in different ways, and compare their performances using several classification performance measures. Thus, this study contributes to the field of fuzzy classification by developing Tanaka based classification models.

The effect of Phase I sample size on the run length performance of control charts for autocorrelated data

Abstract Traditional control charts assume independence of observations obtained from the monitored process. However, if the observations are autocorrelated, these charts often do not perform as intended by the design requirements. Recently, several control charts have been proposed to deal with autocorrelated observations. The residual chart, modified Shewhart chart, EWMAST chart, and ARMA chart are such charts widely used for monitoring the occurrence of assignable causes in a process when the process exhibits inherent autocorrelation. Besides autocorrelation, one other issue is the unknown values of true process parameters to be used in the control chart design, which are often estimated from a reference sample of in-control observations. Performances of the above-mentioned control charts for autocorrelated processes are significantly affected by the sample size used in a Phase I study to estimate the control chart parameters. In this study, we investigate the effect of Phase I sample size on the run length performance of these four charts for monitoring the changes in the mean of an autocorrelated process, namely an AR(1) process. A discussion of the practical implications of the results and suggestions on the sample size requirements for effective process monitoring are provided.

A case study in off-line quality control: characterisation and optimisation of batch dyeing process design

A method is provided and demonstrated for robust design of the batch dyeing process. This method is used to identify optimal batch dyeing process parameter settings, which produce target colour with the least colour variation within and among dyed fabric pieces. The robust design problem is defined in terms of the design objectives, control factors and noise factors. Performance measures are presented to evaluate mean and dispersion characteristics of the dyeing output. Design and conduct of experiments are discussed for developing empirical models of the performance measures, and these models are developed for the study case. The robust design problem is formulated and solved as a nonlinear programming problem. Confirmation of results and iterative use of the proposed design method are discussed.

EVALUATING THE CMARS PERFORMANCE FOR MODELING NON‐LINEARITIES

Multivariate Adaptive Regression Splines (MARS) is a very popular nonparametric regression method particularly useful for modeling nonlinear relationships that may exist among the variables. Recently, we developed CMARS method as an alternative to backward stepwise part of the MARS algorithm. Comparative studies have indicated that CMARS performs better than MARS for modeling nonlinear relationships. In those studies, however, only main and two‐factor interaction effects were sufficient to model the nonlinearity between the variables in the data sets. In this study, therefore, we aim at evaluating the model performances when there is a need for representing higher‐order interaction effects in a nonlinear model. Results based on the comparison studies show that CMARS method performs better than MARS method according to most of the performance measures.

An Academic Performance Measurement System and its Impact on Quality of Engineering Faculty Work at Middle East Technical University

A system has been developed for measuring the performance of faculty members at Middle East Technical University (METU) in 1998. This system has been designed to provide feedback for improving the quality of academic work. It involves a separate set of criteria and measures for each college within the university. The criteria used for the College of Engineering include publications, editorial work and translation, professional and other research activities, educational activities, memberships and awards, and other activities. Faculty members are recruited based on these criteria. They report their activities and works annually as required by this system, and they can access their measurement results through the university Intranet. These results play a significant role in promotion of the faculty members. Awards are given to faculty members who demonstrate superior performance. This system has also been utilised to measure the efficiency of engineering departments, and guide and support departments in improving their efficiency. Since the beginning of implementation of this system, significant changes have been observed in preferences and activities of faculty members, and their performance. The paper discusses impact of this system on performance of the Engineering College.

Desirability Functions in Multiresponse Optimization

Desirability functions (DFs) play an increasing role for solving the optimization of process or product quality problems having various quality characteristics to obtain a good compromise between these characteristics. There are many alternative formulations to these functions and solution strategies suggested for handling their weaknesses and improving their strength. Although the DFs of Derringer and Suich are the most popular ones in multiple-response optimization literature, there is a limited number of solution strategies to their optimization which need to be updated with new research results obtained in the area of nonlinear optimization.

Determining the Climate Zones of Turkey by Center-Based Clustering Methods

There is a growing evidence that the climate change has already had significant impacts on the world’s physical, biological, and human systems, and it is expected that these impacts will become more severe in the near future. Alterations in the weather patterns and the existence of extreme events can be considered as important indicators of this change. The validity of this reality can be judged by analyzing climate data thoroughly. In this study, for determining the climate zones of Turkey, temperature measures obtained from the Turkish State Meteorological Service stations in the period 1950–2006 are examined by using two center-based clustering methods, namely k-means and fuzzy k-means. The clusters obtained from these methods are compared using objective criteria. They are also evaluated subjectively by the domain experts.

AN ATTEMPT TO MINIMIZE THE COST OF EXTRACTING LITHIUM FROM BORON CLAYS THROUGH ROBUST PROCESS DESIGN

In this study a robust design method is developed for extracting Li from boron (B) clays with the aim of minimizing cost and maximizing productivity. Lithium is commercially extracted from brines and certain minerals. Its extraction from clays has previously been found to be expensive, a major part of the extraction cost being attributed to the raw materials used. In this study, raw materials from lower-cost resources are used without applying any standardization to them and this might increase variation in the results. To minimize the variation, and achieve high extraction levels, robust design, statistical design and analysis of experiments, and response surface methodologies are utilized. As a result, consistently higher extraction levels have been achieved compared to previous studies. The experiments were conducted using the Bigadiç boron clay fields in Turkey. However, the method is generally applicable to other cases also.