Kalite iyileştirmede veri madenciliği kullanımı ve geliştirilmesi

TÜBİTAK MAG30.06.2009Bu projede amaç, sanayi kuruluşlarında ürün ve süreçlerin kalitesini iyileştirmeye yönelik veri madenciliği (VM) yaklaşımlarını belirlemek ve daha etkili yaklaşımlar geliştirmektir. Projede imalat sanayi kuruluşlarının ürün ve süreçlerinin kalitesini iyileştirme ile ilgili kalitenin tanımlanması, tahmin edilmesi, sınıflandırılması ve parametrelerinin optimizasyonu problemleri ele alınmıştır. Bu problemlerin çözümü için veri hazırlama ve önişlemenin yanısıra kümeleme, tahmin etme, sınıflandırma, birliktelik analizi ve optimizasyon VM işlevlerinin gerekli olabileceği belirlenmiştir. Bu kapsam dahilinde geniş bir literatür taraması yapılmış ve değişik imalat sektörlerinde etkinlik gösteren altı kuruluş ziyaret edilmiştir. Bunlardan üçünün sağladığı veriler üzerinde uygun VM metotları uygulanmış ve sonuçlar karşılaştırılmıştır. Bu karşılaştırma sonucunda belli VM işlevleri için kalite iyileştirme amaçlarına en uygun VM metotları belirlenmiş ve uygulayıcılara önerilmiştir. Projenin yöntem geliştirme kısmında ise uygulama aşamasında karşılaşılan bazı problemlerin giderilmesi ve mevcut yöntemlerin kullanım kolaylığı ve/veya etkililiğinin artırılması yönünde çalışmalar gerçekleştirilmiştir. Sonuçta, kalite verilerinin yeniden örneklenmesi için bir yöntem; parametrik olmayan alternatif bir regresyon yaklaşımı (CMARS); ikili sınıflandırmada kullanımı kolay olan Mahalanobis Taguchi Sistemi metodunun çok sınıf ve ayrıca parametre optimizasyonu için uyarlamalar; bulanık sınıflandırmada kalite verilerine uygun alternatif yaklaşımlar (bulanık regresyona dayalı modeller) ve parametrik olmayan bulanık tahmin etme ve sınıflandırma fonksiyonları; parametre optimizasyonunda çekicilik fonksiyonlarının optimizasyonu için alternatif yaklaşımlar ve birliktelik kurallarının seçimi için bir yöntem geliştirilmiştir. Bu sonuçların ve metotların kalite iyileştirme alanında uygulayıcıların çalışmalarına yön vermesi ve bunların kullanım kolaylığı ile etkililiğini artırması beklenmektedir.The objective of this project is to identify the data mining (DM) approaches that can effectively improve product and process quality in industrial organizations, and to develop more effective approaches. In the project, quality definition, prediction, classification and parameter optimization problems associated with product and process quality improvement in manufacturing industries are considered. For the solution of these problems, clustering, prediction, classification, association and optimization functions of DM as well as data preparation and preprocessing are determined as relevant. A comprehensive literature survey has been performed and six manufacturing companies operating in different sectors have been visited, within this context. Appropriate DM methods are applied on data sets obtained from three of these companies, and the results are compared. As a result, the most appropriate DM methods are suggested for specific DM functions and quality improvement purposes. In the method development part of the project, studies are performed to overcome some problems encountered during the applications, and to increase ease of use and effectiveness of the VM methods. As a result, a resampling method for quality data; an alternative nonparametric approach (CMARS) for regression; adaptations of an easy to use binary classification method, Mahalanobis Taguchi system, to multiple classes and also to parameter optimization; alternative approaches for fuzzy classification of quality data (models based on fuzzy regression) and nonparametric fuzzy functions; alternative approaches for optimization of desirability functions in parameter optimization; and a method for reduction of association rules are developed. It is expected that these results and approaches guide practitioners in quality improvement area, and incease the ease of use and effectiveness of them

Veri madenciliğinin kalite iyileştirmedeki uygulamalarının geniş bir özeti ve örnek bir çalışma.

In today‘s world, knowledge is the most powerful factor for the success of the organizations. One of the most important resources to reach this knowledge is the huge data stored in their databases. In the analysis of this data, DM techniques are essentially used. In this thesis, firstly, a comprehensive literature review on DM techniques for the quality improvement in manufacturing is presented. Then one of these techniques is applied on a case study. In the case study, the customer quality perception data for driver seat quality is analyzed. Decision tree approach is implemented to identify the most influential variables on the satisfaction of customers regarding the comfort of the driver seat. Results obtained are compared to those of logistic regression analysis implemented in another study.M.S. - Master of Scienc

İmalat sektöründe kalite iyileştirmede veri madenciliği tekniklerinin kullanımı

Günümüzde kalite dünya pazarında rekabetin ana unsurlarından biri haline gelmiştir. İşletmeler artık ürün ve süreçlerin kalite tasarım, kontrol ve iyileştirme çalışmalarına daha fazla önem vermekte, bu çalışmaları da tüm çalışanların katılımıyla gerçekleştirmektedir. Sonuç olarak müşteri memnuniyetinin kazanılmasıyla birlikte maliyetlerin düşürülmesi, verimlilik ve kârlılığın artırılması istenmektedir. Kalite iyileştirme çalışmalarında sıklıkla sahadan, müşteriden ve üretimden veriler toplamak yoluyla çeşitli analizler yapılmaktadır. Bu analizlerde, özellikle karışık tipte ve çok sayıda girdi ve çıktı değişkenine sahip büyük miktardaki veri kümeleri için giderek daha fazla veri madenciliği (VM) yaklaşımları kullanılmaktadır. Ancak VM, kalite iyileştirme çalışmalarında bulunanlar tarafından hâlâ yeterince tanınmayan ve kalite iyileştirmeye olası katkıları yeterince araştırılmamış bir alandır. Bu çalışmada, öncelikle VM süreci tanımlanmış ve ardından 1997-2007 yılları arasını kapsayan literatürden seçilen, imalat sektöründe belirli kalite iyileştirme problemlerine uygulanmış VM çalışmaları değerlendirilmiştir. Kalite iyileştirme problemlerinden süreç ve ürün kalitesinin tanımlanması, kalitenin tahmini, kalitenin sınıflandırılması ve kalite parametrelerinin optimizasyonu üzerinde durulmuştur. Çalışmada ayrıca, en yaygın kullanılan ve etkili VM tekniklerinden karar ağaçlarının bir döküm fabrikasında döküm hatalarına neden olan değişkenleri ve seviyelerini belirlemek amacıyla yapılan uygulamaya yer verilmiştir.Quality is a major requirement of competition in today's world markets. Organizations give much more importance to quality design, control and improvement of products and processes, and accomplish these with the participation of all employees. As a result, it is aimed to achieve customer satisfaction along with reduction in cost and increase in productivity and profitability. In quality improvement (QI) studies, a variety of analyses are performed by collecting data from the field, customer and manufacturing. In these analyses, an increasing number of data mining (DM) approaches are being used, especially for large datasets with too many and mixed type of input and output variables. However, DM is still not widely known and utilized by people practicing QI, and there is no sufficient research into the possible contributions of it to QI. In this study, first of all, the DM process is defined, and then selected DM applications on certain QI problems in manufacturing industry, published in 1997-2007, are examined. Among the QI problems, the followings are studied: description of product and process quality, prediction of quality, classification of quality, and optimization of quality parameters. Moreover, a case study is presented, which utilizes a commonly used and effective DM technique called decision trees for identifying influential process variables and their levels that cause casting defects in a casting company

Effortful foraging activity for uncertain food in pigeons

When well-known food resources are running out, animals extinguish their foraging behavior in that food patch and increasingly work for reward-related information to decrease outcome uncertainty. In the absence of such information, a potentially successful strategy consists of spending more time and effort searching for profitable locations—a phenomenon known from extinction learning experiments conducted in conventional conditioning chambers. Here, we tested this hypothesis by means of a semi-natural “foraging board” allowing pigeons to move and look for food items inside perforated holes. The holes could be covered with a slit plastic tape, hiding the food items they contained while making them accessible to pigeons. Our goal was to determine how pigeons forage on hidden food items in an area associated with uncertainty (one in three holes baited, on average) when visible or hidden food items were available in an adjacent area associated with certainty (each hole baited). The number of food items was equivalent in both areas. We expected longer time spent and more pecks given in the uncertain vs. certain area with the food items visible in the certain area, as well as longer time spent and more pecks given per visit in the uncertain vs. certain area with the food items either visible or hidden in the certain area. Our results confirm these predictions

Defect Cause Modeling with Decision Tree and Regression Analysis

The main aim of this study is to identify the most influential variables that cause defects on the items produced by a casting company located in Turkey. To this end, one of the items produced by the company with high defective percentage rates is selected. Two approaches-the regression analysis and decision trees are used to model the relationship between process parameters and defect types. Although logistic regression models failed, decision tree model gives meaningful results. Based on these results, it can be claimed that the decision tree approach is a promising technique for determining the most important process variables

Diabetes, Hypertension, Atrial Fibrillation and Subsequent Stroke-Shift towards Young Ages in Brunei Darussalam

Southeast Asia harbors a young population of more than 600 million people. Socioeconomic transition within the last decades, driven by globalization and rapid economic growth, has led to significant changes in lifestyle and nutrition in many countries of this region. Hence, an increase in the number of non-communicable diseases is seen in most populations of Southeast Asia. Brunei Darussalam is the smallest country in this region, with a population of around 400,000 inhabitants. Vast hydrocarbon resources have transformed Brunei into a wealthy industrialized country within the last few decades. We compared the age distribution and prevalence of cardiovascular risk factors in ischemic stroke patients between the only stroke unit in Brunei Darussalam and a tertiary stroke center from Frankfurt/Germany. Between 2011 and 2016, a total number of 3877 ischemic stroke patients were treated in both institutions. Even after adjusting for age due to different population demographics, stroke patients in Brunei were younger compared to their German counterparts. The prevalence of hypertension and diabetes mellitus was significantly higher in young age groups in Brunei, whereas no difference was observed for older patients. The rapid socioeconomic transition might be a significant risk factor for the development of non-communicable diseases, including stroke

Mirror self-recognition in pigeons

Spontaneous mirror self-recognition is achieved by only a limited number of species, suggesting a sharp "cognitive Rubicon" that only few can pass. But is the demarcation line that sharp? In studies on monkeys, who do not recognize themselves in a mirror, animals can make a difference between their mirror image and an unknown conspecific. This evidence speaks for a gradualist view of mirror self-recognition. We hypothesize that such a gradual process possibly consists of at least two independent aptitudes, the ability to detect synchronicity between self- and foreign movement and the cognitive understanding that the mirror reflection is oneself. Pigeons are known to achieve the first but fail at the second aptitude. We therefore expected them to treat their mirror image differently from an unknown pigeon, without being able to understand that the mirror reflects their own image. We tested pigeons in a task where they either approached a mirror or a Plexiglas barrier to feed. Behind the Plexiglas an unknown pigeon walked at the same time toward the food bowl. Thus, we pitched a condition with a mirror-self and a foreign bird against each other, with both of them walking close toward the food bowl. By a detailed analysis of a whole suit of behavioral details, our results make it likely that the foreign pigeon was treated as a competitor while the mirror image caused hesitation as if being an uncanny conspecific. Our results are akin to those with monkeys and show that pigeons do not equal their mirror reflection with a conspecific, although being unable to recognize themselves in the mirror