MARKOV CHAINS IN QUALITY FUNCTION DEPLOYMENT: AN EXAMPLE OF AUTOMATIVE SECTOR

Kalite Fonksiyon Göçerimi (KFG), müsteriyi tatmin etmeyi ve müsterinin talep ettiklerini tasarım hedeflerine ve üretim sırasında kullanılacak baslıca kalite güvence noktalarına dönüstürmek amacıyla tasarım kalitesini gelistirmeyi amaçlayan bir yöntemdir. Bu yöntemin bir asaması, müsteri gereksinimleri ile teknik gereksinimler arasındaki iliskiyi belirlemektir. Bu çalısmada iliskinin modellenebilmesi için Markov zincirlerinden yararlanılmıs ve otomotiv sektöründe otomobil sahiplerinin isteklerine yönelik otomobil tasarımı için kalite fonksiyon göçerimi uygulanmıstır. Bu anlamda Markov zincirlerinin temelinde bulunan olasılık ve geçis matrisleri yardımıyla müsteri gereksinimleri ile teknik gereksinimler arasındaki iliski, beklenen degerler bazında degerlendirilmis ve teknik gereksinimlerin gelecekte farklı dönemlerde alacagı degerler gözlemlenerek bir analiz yapılmıstır. Quality function deployment (QFD) is a method that aims satisfying customers and improving design quality for transforming customer requirements into design targets and quality assurance points that used during production. First step of this method determines the relationship between customer requirements and the technical requirements. Because of the uncertainty of quality by its nature, Markov chains are used for modeling the relationship correctly and applied to quality function deployment for the requirements of automobile owners at automobile industry. In this basis the relationship between customer requirements and the technical requirements is evaluated on the expected value base by means of probability and transition matrices which are the basic of Markov chains. An analysis is made by observing the value of technical requirements through different time periods

MARKOV CHAINS IN QUALITY FUNCTION DEPLOYMENT: AN EXAMPLE OF AUTOMATIVE SECTOR

Kalite Fonksiyon Göçerimi (KFG), müsteriyi tatmin etmeyi ve müsterinin talep ettiklerini tasarım hedeflerine ve üretim sırasında kullanılacak baslıca kalite güvence noktalarına dönüstürmek amacıyla tasarım kalitesini gelistirmeyi amaçlayan bir yöntemdir. Bu yöntemin bir asaması, müsteri gereksinimleri ile teknik gereksinimler arasındaki iliskiyi belirlemektir. Bu çalısmada iliskinin modellenebilmesi için Markov zincirlerinden yararlanılmıs ve otomotiv sektöründe otomobil sahiplerinin isteklerine yönelik otomobil tasarımı için kalite fonksiyon göçerimi uygulanmıstır. Bu anlamda Markov zincirlerinin temelinde bulunan olasılık ve geçis matrisleri yardımıyla müsteri gereksinimleri ile teknik gereksinimler arasındaki iliski, beklenen degerler bazında degerlendirilmis ve teknik gereksinimlerin gelecekte farklı dönemlerde alacagı degerler gözlemlenerek bir analiz yapılmıstır. Quality function deployment (QFD) is a method that aims satisfying customers and improving design quality for transforming customer requirements into design targets and quality assurance points that used during production. First step of this method determines the relationship between customer requirements and the technical requirements. Because of the uncertainty of quality by its nature, Markov chains are used for modeling the relationship correctly and applied to quality function deployment for the requirements of automobile owners at automobile industry. In this basis the relationship between customer requirements and the technical requirements is evaluated on the expected value base by means of probability and transition matrices which are the basic of Markov chains. An analysis is made by observing the value of technical requirements through different time periods

Control Charts to Enhance Quality

Control charts are important tools of statistical quality control to enhance quality. Quality improvement methods have been applied in the last few 10 years to fulfill the needs of consumers. The product has to retain the desired properties with the least possible defects, while maximizing profit. There are natural variations in production, but there are also assignable causes which do not form part of chance. Control charts are used to monitor production; in particular, their application may serve as an “early warning” index regarding potential “out-of-control” processes. In order to keep production under control, different control charts which are prepared for dissimilar cases are established incorporating upper and lower control limits. There are a number of control charts in use and are grouped mainly as control charts for variables and control charts for attributes. Points plotted on the charts may reveal certain patterns, which in turn allows the user to obtain specific information. Patterns showing deviations from normal behavior are raw material, machine setting or measuring method, human, and environmental factors, inadvertently affecting the quality of product. The information obtained from control charts assists the user to take corrective actions, hence opting for specified nominal values enhancing as such quality

Multivariate statistical process control using dynamic ensemble methods

One important challenge with some applications such as credit card fraud detection, intrusion detection and network traffic monitoring is that data arrive in streams over time and leads to changes in concepts which are known in data mining as concept drift. Thus, models analyzing such data become obsolete and efficient learning should be able to identify these changes and quickly update the system to them. The objective of this dissertation is to investigate the effectiveness of ensemble methods and Statistical Process Control (SPC) techniques in detecting changes in processes in order to improve the robustness of tracking concept drift and coping with the dynamics of online data stream processes. For reaching this objective, different heuristics were proposed. First, an improved dynamic weighted majority Winnow algorithm based on ensemble methods is proposed. Furthermore, parameters optimization based on genetic algorithm of the proposed method as well as an analysis of its robustness are investigated. Second, in order to handle the problem of concept drift while monitoring nonstationary environment using SPC tools, a time adjusting control chart based on a recursive adaptive formulas of the charting statistics is proposed. Results show that the updating charts cope much better with the nonstationarity of the environment. Also, two new heuristics are proposed based on both ensemble methods and adaptive control charts. The first is an offline learning chart model while the second is an online batch learning algorithm. Results show that quick adaptation of the system and accurate shift point identification are achieved when using both heuristics together. Also, the new adaptive ensemble charts have better performance in learning concept drifts along with a good suitability to nonlinearity and noise issues

SIMULATING SEISMIC WAVE PROPAGATION IN TWO-DIMENSIONAL MEDIA USING DISCONTINUOUS SPECTRAL ELEMENT METHODS

We introduce a discontinuous spectral element method for simulating seismic wave in 2- dimensional elastic media. The methods combine the flexibility of a discontinuous finite element method with the accuracy of a spectral method. The elastodynamic equations are discretized using high-degree of Lagrange interpolants and integration over an element is accomplished based upon the Gauss-Lobatto-Legendre integration rule. This combination of discretization and integration results in a diagonal mass matrix and the use of discontinuous finite element method makes the calculation can be done locally in each element. Thus, the algorithm is simplified drastically. We validated the results of one-dimensional problem by comparing them with finite-difference time-domain method and exact solution. The comparisons show excellent agreement