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Öğe A Multi-Objective Perspective to Satellite Design and Reliability Optimization(Pergamon-Elsevier Science Ltd, 2024) Tetik, Taha; Das, Gulesin Sena; Birgoren, BurakDevelopment of a communication satellite project is highly complicated and expensive which costs a few hundred million dollars depending on the mission in space. Once a satellite is launched into orbit, it has to operate in harsh environmental conditions including radiation, solar activity, meteorites, and extreme weather patterns. Since there is no possibility of physical maintenance intervention in space, reliability is a critical attribute for all space and satellite projects. Therefore, the redundancy philosophy and reliability measures are taken into account in the design phase of a satellite to prevent the loss of functionality in case of a failure in orbit. This study aims to optimize the payload design of a communication satellite by considering the system's reliability, power consumption and cost simultaneously. Since these objectives are conflicting in their nature, a multi-objective optimization approach is proposed. We offer a systematic approach to the satellite design by determining the best redundancy strategy considering contradictory objectives and onboard constraints in the multibillion-dollar satellite industry. The proposed approach promotes trade-offs and sensitivity analyses between cost, power consumption and system reliability in the early design phase of satellites using Compromise Programming. By using different sets of weights for the objectives in our model, it is possible to address different types of satellites depending on their mission and priorities. Because of the NP-Hard characteristics of the reliability optimization problem and the nonlinear equation in the proposed model, the Simulated Annealing algorithm is utilized to solve the problem. As a case analysis, the implementation is carried out on the design of a communication satellite system with active hot-standby and warm-standby onboard redundancy schemes. Results reveal that huge savings in million dollars can be attained as a result of approximately 5% reduction in reliability.Öğe A Two-Phase Approach for Reliability-Redundancy Optimization of a Communication Satellite(Gazi Univ, 2024) Tetik, Taha; Das, G. Sena; Birgoren, BurakThe development and launch of communication satellite projects pose significant challenges and costs. The expenses can range from several hundred million dollars, contingent on factors such as mission objectives, satellite system size and complexity including the launch vehicle, and ground infrastructure. Satellites must be designed to withstand harsh conditions in space, such as the extreme temperatures, radiation, and other hazards, while delivering reliable communication services to its users. However, once a satellite is launched, physical maintenance interventions become infeasible in the event of technical problems. Thus, reliability is a critical aspect for these expensive systems. This study aims to minimize the cost of a high-tech communication satellite by addressing design considerations that meet customer reliability requirements without exceeding power and redundant equipment limits. To achieve this goal, we propose an integer non -linear programming model in this research. To solve the satellite design problem, we adopt a two-stage solution approach. Conventional industrial practices in satellite design often involve iterative attempts to determine the redundancy level of onboard units based on customer reliability requirements. These processes rely heavily on the experience of design engineers who evaluate a limited number of alternatives to determine the number of redundant units, resulting in sub -optimal outcomes. In contrast, our proposed approach systematically handles the problem and yields optimal results. Our findings demonstrate that the proposed two-phase approach can achieve optimal redundancy levels within seconds.Öğe Bayesian Confidence Interval Estimation of Weibull Modulus Under Increasing Failure Rate(Gazi Univ, 2021) Yalcinkaya, Meryem; Birgoren, BurakEstimating the confidence interval of the Weibull modulus is an important problem in the fracture strength modeling of ceramic and composite materials. It is particularly important in cases where the sample size is small due to high experimental costs. For this purpose, several classical methods, including the popular maximum likelihood method, and Bayesian methods have been developed in the literature. However, studies on Bayesian inference have remained very limited in the materials science literature. Recently a Bayesian Weibull model has been proposed for estimating confidence lower bounds for Weibull percentiles using the prior knowledge that the failure rates are increasing. This prior argument requires the Weibull modulus to be more than 1 due to wear-out failure. In this study, under the same prior information, two Bayesian Weibull models, one using the same prior argument and the other a relaxed version of it, have been developed for confidence interval estimation of the Weibull modulus. Their estimation performances have been compared against the maximum likelihood method with Monte Carlo simulations. The results show that the Bayesian Weibull models significantly outperform the maximum likelihood method for almost all Weibull modulus and sample size values.Öğe Brass alloy blending problem from quality and cost perspectives: A multi-objective optimization approach(GAZI UNIV, FAC ENGINEERING ARCHITECTURE, 2021) Birgoren, Burak; Sakalli, Umit SamiBrass 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.Öğe Confidence interval estimation of Weibull lower percentiles in small samples via Bayesian inference(Elsevier Sci Ltd, 2017) Yalcinkaya, Meryem; Birgoren, BurakWeibull 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.Öğe Design optimization of cutting parameters when turning hardened AISI 4140 steel (63 HRC) with Al2O3+TiCN mixed ceramic tool(Elsevier Sci Ltd, 2007) Aslan, Ersan; Camuscu, Necip; Birgoren, BurakDue 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.Öğe Detection of Gear Wear and Faults in Spur Gear Systems Using Statistical Parameters and Univariate Statistical Process Control Charts(Springer Heidelberg, 2021) Maras, Sinan; Arslan, Hakan; Birgoren, BurakIn this study, the detection of wear faults in spur gears was examined using vibration analysis, statistical process control method, and statistical parameters. For this purpose, a closed-loop test rig with a power transmission system was established. Defect-free gears were attached to the test assembly, and the system was operated at a specific torsional load and number of cycles until the gears were worn. Vibration amplitudes at vertical and horizontal directions, received via sensors on the bearings, were transferred to the computer with a digital-analog converter. The control charts were plotted by sampling 30 data points per hour. Upper and lower control limits were determined by using the data obtained from the defect-free gears. The gears are worn in the process due to the effect of applied torque and the operation conditions suitable for the formation of defects. As a result, the vibration amplitudes were increased. The accuracy and convergence of the statistical process control method were verified by the statistical parameters root mean square, kurtosis value, skewness value, crest factor, and peak-to-peak values. It was emphasized that great convergence and accuracy between the statistical process control results and statistical parameters results are achieved. The present study showed that the detection of abrasion of a robust gear could be graphically demonstrated through a real-time experimental study. The statistical process control method is convenient and easily applicable, which allows constructing a real-time early warning system detecting malfunctions at the start.Öğe Effects of shear strength variability on the peak and residual horizontal resistance of on-bottom subsea pipelines in clay(Elsevier - Division Reed Elsevier India Pvt Ltd, 2023) Ozkul, Zeynep H.; Birgoren, BurakSubsea production pipelines in deep water oil and gas fields are susceptible to a phenomenon called lateral buckling which is a major pipeline design concern. Accurate estimation of lateral buckle formation and the additional stresses generated are of particular importance for High-Temperature High-Pressure (HTHP) pipelines resting on soft clay and is the subject of pipe-soil interaction studies (PSI). Variability in pipe embedment and soil resistances constitute the largest uncertainty in PSI analyses. Best practice methods for PSI studies constitute multi-step strategies that involve finding solutions to nonlinear and implicit equations. In the present study, the impact of soil strength variability on embedment and subsequently peak (Hpeak/V) and residual (Hres/V) lateral friction factors are evaluated. An R code, PSI-Lateral, is developed and verified against published case studies. It is used to calculate the pipeline embedment and horizontal resistances mobilized in a parametric study on three pipe diameters (0.6, 0.8 m and 1.0 m) with three different wall thicknesses. For each pipe diameter-weight combination, embedment and lateral friction factors (peak and residual) are calculated for a series of mean shear strengths ranging from 2 kPa to 8 kPa and COV ranging from 5% to 37.5%. The high estimate and low estimate shear strengths, suHE and suLE, correspond to values that are +/- two standard deviations from the mean. Hence, the range of strengths encompassed between these two extremes represents approximately 95% of the shear strength distribution. The results of the parametric runs conducted are presented in the form of graphical charts where high and low embedment and lateral friction factors can be read-off for any selected pair of mean shear strength and COV. Similar trends are observed in the charts for all pipelines evaluated. It is found that the range of normalized embedment, z/D, increases with increasing COV and decreases with increasing mean shear strength for all pipelines tested. A similar trend is observed for the residual horizontal resistance. The expected range of peak horizontal resistance Hpeak/V shows the same trend but only for soils with mean strengths less than 4 kPa. For soils with higher mean strengths, the range of Hpeak/V is found to be mostly insensitive to variations in COV and further increases in the mean strength.Öğe Estimation algorithms for Weibull parameters and percentiles(Gazi Univ, Fac Engineering Architecture, 2009) Danaci, Mehmet Akif; Birgoren, Burak; Ersoz, SuleymanThis 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.Öğe Estimation of Confidence Intervals and Lower Bounds for Various Weibull Percentiles(World Scientific Publ Co Pte Ltd, 2023) Birgoren, Burak; Yalcinkaya, MeryemThe design allowables are derived statistically from measured material properties, and the Weibull distribution is one of the most commonly used distributions for statistical modeling. A- and B-basis design allowables are frequently used; they correspond to the confidence lower bounds for the 1st and 10th percentiles, respectively, with a confidence level of 95%. The maximum likelihood method is generally recommended and commonly used for parameter and confidence lower bound estimation. On the other hand, designers are also interested in confidence lower bounds for other percentiles, and in general, confidence intervals to specify uncertainty in percentile estimates. Monte-Carlo simulation methods have been proposed for this purpose; however, they are not easy to code and take a long time to run to obtain reliable results. As an easy-to-use alternative, this study proposes approximate polynomial functions of sample size for various percentiles and confidence levels. The coefficients of the functions are presented in tabular form for each combination of percentiles and confidence levels. They eliminate the need for simulations and provide precise confidence intervals and lower bounds for a large set of Weibull percentiles.Öğe IDENTIFYING FACTORS AFFECTING INTENTION TO USE IN DISTANCE LEARNING SYSTEMS(Anadolu Univ, 2021) Baki, Rahmi; Birgoren, Burak; Aktepe, AdnanThe use and benefit of Distance Learning Systems (DLS) can be increased by a detailed analysis of the factors affecting students' intention to use. This study aims to analyse the effect of various independent variables on the user satisfaction and intention to use DLS via Perceived Ease of Use and Perceived Usefulness. In addition, Time Effectiveness is proposed as a new variable with the claim that the time spent in DLS is valuable. Data were collected from 925 undergraduate students currently enrolled in 9 state universities in Turkey. Data were analysed through Structural Equation Modelling (SEM). Results show that while Interaction, Compatibility and Time Effectiveness have a positive effect on user satisfaction and intention to use via Perceived Usefulness; Self Efficacy, Subjective Norm and Enjoyment have no influence. Moreover, Self Efficacy, Interaction, Anxiety and Time Effectiveness have a significant impact on Perceived Ease of Use, yet Subjective Norm and Enjoyment don't.Öğe Joint optimization of quality and cost in brass casting using stochastic programming(Taylor & Francis Ltd, 2019) Sakalli, Umit Sami; Birgoren, BurakA 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.Öğe Modeling an Industrial Symbiosis Network using Bilevel Programming(IEEE, 2021) Das, Gulesin Sena; Yesilkaya, Murat; Altinkaynak, Busra; Birgoren, BurakIn this study, we propose a bilevel programming model for a theoretical industrial symbiosis network located in an Eco-Industrial Park. Plants in this forest products-based network comprising a sawmill, a plywood manufacturer, a particleboard producer, a fiberboard producer and a pellet producer aim to minimize their production related costs. On the other hand, the park authority aims to minimize the use of raw materials to foster by-product exchange in the park. Therefore, we propose a bilevel programming model where the park authority is the upper-level decision-maker/leader and the plants in the network are the lower-level decision makers/followers. Our aim is to investigate the effects of industrial symbiosis on emissions and the profitability of the companies. Results show only by decreasing raw material use, companies in such a network could stay profitable without increasing emissions.Öğe Modeling and analyzing customer data in customer relationship management with artificial neural networks(Gazi Univ, Fac Engineering Architecture, 2008) Ersöz, Süleyman; Yaman, Nevra; Birgoren, BurakCustomers 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.Öğe Obtaining a Multi-Factor Optimum Blend Using Scrap within the Scope of Sustainable and Environmentally Friendly Steel Production: Application in a Steel-Casting Company(Mdpi, 2024) Bas, Aydogan; Birgoren, Burak; Sakalli, Umit SamiThis study tackles the challenge of optimizing scrap blends in steel production to achieve sustainability and environmental consciousness. Focusing on a steel-casting company as a case study, we develop a mathematical model that minimizes cost, emissions, and energy consumption while maximizing scrap utilization. This model considers the specific elemental composition of various scrap piles and pure elements, alongside their associated costs and environmental impacts in the production of GS52 steel in a foundry company. Through the GAMS program and further verification with Microsoft Excel, we demonstrate that the optimal blend significantly reduces raw material costs by prioritizing scrap (99.7%) over pure elements. Moreover, this optimized blend minimizes energy consumption and associated carbon emissions, thus contributing to a more sustainable and environmentally friendly steel production process. This study offers valuable insights and a practical framework for the steel industry to adopt cost-effective and eco-conscious practices, aligning with global efforts towards sustainable manufacturing.Öğe A possibilistic aggregate production planning model for brass casting industry(Taylor & Francis Ltd, 2010) Sakalli, Umit Sami; Baykoc, Omer Faruk; Birgoren, BurakThis 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.Öğe Shortest Confidence Intervals of Weibull Modulus for Small Samples in Materials Reliability Analysis(Gazi Univ, 2023) Yalcinkaya, Meryem; Birgoren, BurakThe Weibull distribution has been widely used to model strength properties of brittle materials. Estimation of confidence intervals for Weibull shape parameter has been an important concern, since small sample sizes in materials science experiments bring about large intervals. Many methods have been proposed in the literature for constructing shorter intervals; the methods of maximum likelihood, least square, and Menon are among the most extensively studied methods. However, they all use an equal-tails approach. The pivotal quantities used for constructing confidence intervals have right-skewed and unimodal distributions, thus, they clearly do not produce the shortest intervals for a given confidence level in equal tail form. This study constructs the shortest confidence intervals for the three aforementioned methods and compares their performances by their equal-tails counterparts. To this end, a comprehensive simulation study has been conducted for the shape parameter values between 1 to 80 and the sample sizes between 3 to 20. The comparison criterion is chosen as the expected interval length. The results show that the shortest confidence intervals in each of three methods have yielded considerably narrower intervals. Further, the unknown parameter values are more centered in these intervals.Öğe A statistical design optimization study of a multi-chamber reactive type silencer using simplex centroid mixture design(Sage Publications Ltd, 2021) Secgin, Erkan; Arslan, Hakan; Birgoren, BurakThis 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.Öğe Stochastic optimization for blending problem in brass casting industry(Springer, 2011) Sakalli, Umit Sami; Baykoc, Omer Faruk; Birgoren, BurakA critical process in brass casting is blending of the raw materials in a furnace so that the specified metal ratios are satisfied. The uncertainties in raw material compositions may cause violations of the specification limits and extra cost. In this study, we proposed a chance-constrained stochastic programming approach for blending problem in brass casting industry to handle the statistical variations in raw material compositions. The proposed approach is a non-linear mathematical model that is solved global optimally by using GAMS/BARON solver. An application has been performed in MKEK brass factory in KA +/- rA +/- kkale, Turkey and the solution of the application has been compared with alternative solution approaches based on cost and specification violation risk conditions. This comparison demonstrates that the proposed model is the most effective solution approach for managing stochastic uncertainties in blending problems and successfully can be used other industries such as alloy steel or secondary aluminum production.
