13 research outputs found
Brass alloy blending problem from quality and cost perspectives: A multi-objective optimization approach
WOS:000595657400032Brass alloy is a composition of copper and zinc and it also includes lead, iron, tin, aluminum, nickel, antimony if necessary. One of the basic problems in brass casting is to determine which pure and scrap materials will be mixed at what quantities; this problem is known as the blending problem. The ingredient ratios of pure materials are exactly known, however they are expensive. The scrap materials are cheaper than the pure ones with varying ingredient ratios. Stochastic mathematical models aiming to minimize blend cost have been developed in the literature. In the solutions of these models, some of the ingredient ratios exactly equal to the specification limits. Because of the variation, some of them may violate the specification limits and cause quality problems in the actual blends. There is only one study in the literature to solve the quality problem by maximizing the process capability index. However, the blend cost increases when the process capability index maximized. In this study, a multiobjective stochastic mathematical model, which aims both to minimize blend cost and to maximize process capability index, has been developed. The developed model has been converted to a deterministic non-linear counterpart by using chance-constrained programming. Then, fuzzy programming is used to transform the multiobjective model into a single objective one. A solution procedure has been proposed to use it effectively in real life applications. The developed model and solution procedure have been tested by the data supplied from a brass factory. The solution of the numerical example has shown that the developed model and solution procedure can be used successfully in real life applications
Confidence interval estimation of Weibull lower percentiles in small samples via Bayesian inference
Birgoren, Burak/0000-0001-9045-6092WOS: 000400531500024Weibull distribution has been vastly used for modeling fracture strength of ceramic and composite materials. Confidence interval estimation of Weibull parameters and percentiles in small samples has been an important concern due to high experimental costs. It was shown previously that in classical inference the Maximum Likelihood Estimation Method is the best method among several alternatives for estimating 95% one-sided confidence lower bounds on the 1st and 10th Weibull percentiles, namely A-basis and B-basis material properties. This study proposes the Bayesian Weibull Method as an alternative using the information that ceramic and composite materials have increasing failure rates, which requires the Weibull shape parameter to be at least 1. Through Monte Carlo simulations, it is shown that the performance of the Bayesian Weibull Method is superior in that it achieves the precision levels of the Maximum Likelihood Estimation Method with significantly smaller sample sizes. (C) 2017 Elsevier Ltd. All rights reserved
Estimating confidence lower bounds of Weibull lower percentiles with small samples in material reliability analysis
WOS: 000514814600023Weibull distribution is widely used in the modeling of mechanical properties such as tensile strength of ceramic and composite materials. The 95% one-sided confidence lower bounds on the 1st and 10th Weibull percentiles, namely A-basis and B-basis material properties, are important in reliability studies for understanding early failures and reducing risks. These lower bounds are generally estimated by small samples due to the high costs of the experiments, hence the precision of estimation remain low. Therefore, in the literature, many exact and approximate interval estimation methods for Weibull percentiles have been proposed for achieving better performance. In this study, a comprehensive comparison of the exact methods with Monte-Carlo simulations has been made. In addition, some methods developed for Weibull parameters are also included in this comparison since they can be used for exact lower bound estimation but have never used for this purpose in the literature. In the study, the lower bounds have been estimated by the maximum likelihood method, the Menon method and 25 different models of weighted/ unweighted least squares methods (such as improved estimators, interchanged axes), and average false coverage probabilities are used for the comparison criterion. According to the simulation results, the maximum likelihood and the weighted least squares method with Faucher & Tyson weight factors have very similar performances for sample sizes less than 8; and the maximum likelihood method has always shown the best performance for sample sizes greater than or equal to 20. However, it is emphasized that linear regression methods are more practical in terms of ease of calculation when performance differences are negligibl
Joint optimization of quality and cost in brass casting using stochastic programming
Birgoren, Burak/0000-0001-9045-6092WOS: 000488498300001A critical process in brass casting is the blending of pure and scrap materials to satisfy specified metal ratios. The primary focus in such blending problems has always been cost minimization. The optimal blends produced by mathematical models use large amounts of scrap materials, which are cheaper but have high variations in ingredient ratios. This gives rise to quality problems. This study aims at joint optimization of cost and quality. A chance-constrained nonlinear mathematical model is developed for maximizing the minimum process capability level for a fixed cost. Then parametric programming is used to run the model for different costs to produce a Pareto-optimal frontier. An application to data from a brass factory showed that the frontier is highly nonlinear, enabling the decision maker to select a competitive process capability and cost value combination. The proposed approach is applicable to any blending problem in which ingredient amounts have statistical variation
A spreadsheet-based decision support tool for blending problems in brass casting industry
35th International Conference on Computers and Industrial Engineering -- JUN 19-22, 2005-2006 -- Istanbul, TURKEYBirgoren, Burak/0000-0001-9045-6092WOS: 000264037900018This paper has discussed development and implementation of spreadsheet-based decision support tools for modeling and solving blending problems in a large-scale brass factory in Turkey. The user interfaces have been designed in Microsoft Excel which is linked with Lingo modeling language and optimizer. One decision support tool was developed from a single-blend LP model and has been in use at the foundry; it is run several times a day by foremen to obtain optimal raw material quantities for melting operations. That the users were foremen without any engineering and optimization background posed a serious challenge to produce a decision support tool that is easily applicable at the foundry, for which spreadsheet interfaces have produced an effective solution. The paper has elaborated on difficulties faced in the development and implementation and their solutions as well as design of the interface. A similar tool has also been developed for master production planning, which has not been put to use yet. Issues have been discussed regarding its integration into the production planning system and its relationship with the single-blend tool. (C) 2008 Elsevier Ltd. All rights reserved
MODELING AND ANALYZING CUSTOMER DATA IN CUSTOMER RELATIONSHIP MANAGEMENT WITH ARTIFICIAL NEURAL NETWORKS
Birgoren, Burak/0000-0001-9045-6092WOS: 000273014200003Customers must keep customer satisfaction as a top priority in order to keep up with increasing competition. In order to achieve, they need to be able to analyze their customers properly and pay attention to their individual expectations. It is important for companies to maximize customer satisfaction and dependence. The success of Companies depends on the extent to how they manage to become 'indispensable' for their customers. It is related with how they determine important points for the satisfaction of their customers, and reflect it back accordingly. In order to make this assessment, companies must first consider their customers as groups. This study aims to analyze customer information, by artificial neural networks, which cannot be handled by mathematical models and optimization techniques, thus improve marketing process for determining important factors and their levels for customer satisfaction
Design optimization of cutting parameters when turning hardened AISI 4140 steel (63 HRC) with Al2O3+TiCN mixed ceramic tool
Birgoren, Burak/0000-0001-9045-6092WOS: 000245047300021Due to their high hardness and wear resistance, Al2O3-based ceramics are one of the most suitable cutting tool materials for machining hardened steels. However, their high degree of brittleness usually leads to inconsistent results and sudden catastrophic failures. This necessitates a process optimization when machining hardened steels with Al2O3 based ceramic cutting tools. The present paper outlines an experimental study to achieve this by employing Taguchi techniques. Combined effects of three cutting parameters, namely cutting speed, feed rate and depth of cut on two performance measures, flank wear (VB) and surface roughness (R-a), were investigated employing an orthogonal array and the analysis of variance (ANOVA). Optimal cutting parameters for each performance measure were obtained; also the relationship between the parameters and the performance measures were determined using multiple linear regression. (c) 2006 Elsevier Ltd. All rights reserved
A statistical design optimization study of a multi-chamber reactive type silencer using simplex centroid mixture design
Arslan, Hakan/0000-0002-2019-1882WOS: 000509202000001This study aims to optimize the acoustic performance of a silencer with baffles having extension tubes. It considers the position, the number and the extension geometry of the baffles as design variables and sound transmission loss as the response variable to be optimized. The finite element analysis software ABAQUS is used to compute the response values for different combinations of design variables. The statistical design of the experiments provides a mathematical framework for such computer design optimization studies with multiple design variables. Yet, it has not been used for design optimization of silencers in the literature. In this study, simplex centroid mixture designs, a type of response surface method, are used in the statistical design of experiments. They can provide faster convergence on the optimization problem. The design involves one, two and three baffles with different positions and extension tube lengths. The outcome of this study indicates that obtaining ABAQUS software solutions at design points for each baffle number allows constructing nonlinear regression equations expressing the response variable as a function of the design variables. The equations obtained are then used to compute optimal values. Further evaluation of these equations indicates that better sound transmission loss values are obtained when the baffle number is increased, and the lengths of the extension tubes are set at high values. Moreover, it is possible to use the statistical experimental design approach implemented in this study for other types of silencers with different baffle geometries and design variables
ESTIMATION ALGORITHMS FOR WEIBULL PARAMETERS AND PERCENTILES
Birgoren, Burak/0000-0001-9045-6092WOS: 000273608200013This study concerns the use of Weibull distribution in statistical component reliability. Recently, estimation of confidence intervals and confidence lower bounds for Weibull parameters and percentiles in small samples has received increasing attention in the literature. In expensive or long experiments, it is crucial to keep the sample size to a minimum, however, the estimates become less reliable as the sample size decreases. Therefore, it has become a necessity to perform a comparative study of estimation algorithms for small sample sizes and code them in an efficient manner. In this study, uncensored reliability data have been considered; algorithms have been developed for goodness-of-fit tests, point and confidence interval estimation for parameters and percentiles by the maximum likelihood and weighted least squares methods based on simulation. The algorithms have been generated in the standard C++ language and integrated under a computer interface. Similar studies in the literature were performed only for a limited number of failure probabilities, confidence levels and sample sizes with low simulation run numbers; the user has to use coefficients and formulae obtained from the simulations to produce the estimates. In contrast, the algorithms developed in this study allow the user to perform simulations with any run number, failure probability, confidence level and sample size, and calculate the estimates in a reasonable amount of time. The simulation error can be kept at low levels by specifying large simulation run numbers. Also, the application of the interface has been illustrated on failure times of DC motors
A possibilistic aggregate production planning model for brass casting industry
Birgoren, Burak/0000-0001-9045-6092WOS: 000277651000006This article discusses a possibilistic aggregate production planning (APP) model for blending problem in a brass factory; the problem computing optimal amounts of raw materials for the total production of several types of brass in a planning period. The model basically has a multi-blend model formulation in which demand quantities, percentages of the ingredient in some raw materials, cost coefficients, minimum and maximum procurement amounts are all imprecise and have triangular possibility distributions. A mathematical model and a solution algorithm are proposed for solving this model. In the proposed model, the Lai and Hwang's fuzzy ranking concept is relaxed by using 'Either-or' constraints. An application of the brass casting APP model to a brass factory demonstrates that the proposed model successfully solves the multi-blend problem for brass casting and determines the optimal raw material purchasing policies