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Öğe Analysis of key attributes of wooden toys via an interval-valued spherical fuzzy analytic hierarchy process(University of Zagreb, Faculty of Forestry, 2023) Singer, Hilal; Özşahin, Şükrü& BULL; The evaluation of wooden toys is a complicated process and can be overwhelming for decision -makers in the presence of many conflicting criteria. Hence, this study proposes a fuzzy decision-making model to identify and prioritize the key attributes of wooden toys. For this purpose, the interval-valued spherical fuzzy analytic hierarchy process (AHP), which is one of the fuzzy multicriteria decision-making methods, is applied to obtain weight vectors. Firstly, the wooden toy evaluation problem is formulated as a multicriteria decision-making problem. Then five main criteria and twenty subcriteria are defined with the help of experts. The decision-making team carries out the pairwise comparisons of the criteria. As a result, the priority weights are computed and the ranking order of the criteria is revealed. Additionally, the validity of the obtained results is supported by conduct-ing a comparative analysis between other popular fuzzy methods: interval type-2 fuzzy AHP, interval-valued Py-thagorean fuzzy AHP, and spherical fuzzy AHP. According to the modeling results, the most important criteria are absence of small parts and sharp edges, free of harmful wood preservatives and paints, workmanship qual-ity, contribution to psychomotor development, and contribution to cognitive development. The proposed framework can be adapted to similar decision processes for the evaluation or improvement of toys. Consequently, the findings of this research will help manufacturers, designers, and consumers in making conscious decisions.Öğe Application of a Performance Evaluation Model to the Paper and Paper Products Printing Sector: The DEA-AHP Hybrid Algorithm(2024) Singer, HilalThe paper and paper products printing sector plays a crucial role in generating income, creating employment opportunities, and supporting exports and various industries. Measuring the efficiency of companies operating in this sector is important in identifying areas for improvement and enhancing overall performance. In this study, a two-stage DEA (data envelopment analysis)-AHP (analytic hierarchy process) approach is proposed to analyze the efficiency of twelve paper and paper products printing companies traded on Borsa Istanbul. The modified DEA method is employed to make pairwise comparisons of the companies. Total assets, total equity, and the number of employees are selected as inputs, while revenue and net profit are considered as outputs. The AHP method prioritizes the companies by considering the outputs of the mathematical models constructed via DEA. The proposed framework presents a different view because it contributes to identifying the most efficient company, benchmarking company performance, and determining areas for improvement.Öğe Applying an interval-valued pythagorean fuzzy analytic hierarchy process to rank factors influencing wooden outdoor furniture selection(Taylor & Francis Ltd, 2022) Singer, Hilal; Özşahin, ŞükrüWooden outdoor furniture is a worthwhile investment. When evaluating outdoor furniture, it is important to weigh up decision factors. In situations where there are many conflicting factors, decision-makers become undecided about determining the best furniture option. Hence, this study proposes an interval-valued Pythagorean fuzzy analytic hierarchy process-based model to prioritise the key factors influencing wooden outdoor furniture selection. In light of the aim, five main factors were determined: "safety and health properties", "comfort and appearance properties", "durability and mechanical properties", "economic aspects", and "environmental aspects". Each main factor was then subdivided into various subfactors. Pairwise comparisons were performed to obtain the priorities of the factors. According to the results, "safety and health properties" was the most significant main factor. The most important subfactors were found as "tipping resistance", "environmental friendliness", and "free of harmful substances".Öğe Artificial Neural Network-based Prediction Model to Minimize Dust Emission in the Machining Process(Elsevier, 2024) Singer, Hilal; Ilce, Abdullah C.; Senel, Yunus E.; Burdurlu, ErolBackground: Dust generated during various wood-related activities, such as cutting, sanding, or processing wood materials, can pose significant health and environmental risks due to its potential to cause respiratory problems and contribute to air pollution. Understanding the factors influencing dust emission is important for devising effective mitigation strategies, ensuring a safer working environment, and minimizing environmental impact. This study focuses on developing an artificial neural network (ANN) model to predict dust emission values in the machining of black poplar ( Populus nigra L.), oriental beech ( Fagus orientalis L.), and medium-density fiberboards. Methods: The multilayer feed-forward ANN model is developed using a customized application built with MATLAB code. The inputs to the ANN model include material type, cutting width, number of blades, and cutting depth, whereas the output is the dust emission. Model performance is assessed through graphical and statistical comparisons. Results: The results reveal that the developed ANN model can provide adequate predictions for dust emission with an acceptable level of accuracy. Through the implementation of the ANN model, the study predicts intermediate dust emission values for different cutting widths and cutting depths, which are not considered in the experimental work. It is observed that dust emission tends to decrease with reductions in cutting width and cutting depth. Conclusion: This study introduces an alternative approach to optimize machining-process conditions for minimizing dust emissions. The findings of this research will assist industries in obtaining dust emission values without the need for additional experimental activities, thereby reducing experimental time and costs. (c) 2024 Occupational Safety and Health Research Institute. Published by Elsevier B.V. on behalf of Institute, Occupational Safety and Health Research Institute, Korea Occupational Safety and Health Agency. This is an open access article under the CC BY-NC-ND licenseÖğe Artificial Neural Network–based Prediction Model to Minimize Dust Emission in the Machining Process(Elsevier B.V., 2024) Singer, Hilal; İlçe, Abdullah C.; Şenel, Yunus E.; Burdurlu, ErolBackground: Dust generated during various wood-related activities, such as cutting, sanding, or processing wood materials, can pose significant health and environmental risks due to its potential to cause respiratory problems and contribute to air pollution. Understanding the factors influencing dust emission is important for devising effective mitigation strategies, ensuring a safer working environment, and minimizing environmental impact. This study focuses on developing an artificial neural network (ANN) model to predict dust emission values in the machining of black poplar (Populus nigra L.), oriental beech (Fagus orientalis L.), and medium-density fiberboards. Methods: The multilayer feed-forward ANN model is developed using a customized application built with MATLAB code. The inputs to the ANN model include material type, cutting width, number of blades, and cutting depth, whereas the output is the dust emission. Model performance is assessed through graphical and statistical comparisons. Results: The results reveal that the developed ANN model can provide adequate predictions for dust emission with an acceptable level of accuracy. Through the implementation of the ANN model, the study predicts intermediate dust emission values for different cutting widths and cutting depths, which are not considered in the experimental work. It is observed that dust emission tends to decrease with reductions in cutting width and cutting depth. Conclusion: This study introduces an alternative approach to optimize machining-process conditions for minimizing dust emissions. The findings of this research will assist industries in obtaining dust emission values without the need for additional experimental activities, thereby reducing experimental time and costs. © 2024 Occupational Safety and Health Research InstituteÖğe Classifying the properties of stainless steel materials for biomedical applications under an intuitionistic fuzzy environment: An FMEA-based TOPSIS-sort methodology(Elsevier, 2024) Singer, Hilal; Ozcelik, Tijen OverThe demand for biomedical metal materials has increased in parallel with the rise in the elderly population. Stainless steels are commonly chosen for biomedical applications. Evaluating material properties plays a crucial role in minimizing the risk of undesirable events. Therefore, this study analyzes the properties of stainless steel materials from a risk perspective. The TOPSIS-Sort and FMEA methods are hybridized by considering the intuitionistic fuzzy set to handle the issue. The intuitionistic fuzzy FMEA procedure is employed to construct the risk matrix, while the intuitionistic fuzzy TOPSIS-Sort method is used to categorize the material properties into three distinct levels based on their significance. Furthermore, the adequacy of the evaluation framework is assessed through sensitivity analyses. As a result of the calculations, the degrees of importance corresponding to the high importance category are assigned to compatibility (0.1530), reactivity (0.1797), corrosion resistance (0.1840), ion release (0.1919), longevity (0.2154), infection (0.2221), osseointegration (0.2298), fatigue resistance (0.2385), and funding trends (0.2568). The research methodology provides a valuable guide for the evaluation of biomaterials.Öğe Development of an artificial neural network model to minimize power consumption in the milling of heat-treated and untreated wood(2019) Özşahin, Şükrü; Singer, HilalAim of study: The power consumption of machining operations is an important part of the total production cost. Therefore, in this study, an artificial neural network (ANN) model was developed to model the effects of treatment, rotation speed, cutting depth, and feed rate on power consumption in the wood milling process. Material and methods: A multilayer feed-forward ANN was employed for the prediction of power consumption. The accuracy of the model was assessed by performance indicators such as MAPE, RMSE, and R². Main results: It has been observed that the ANN model yielded very satisfactory results with acceptable deviations. The MAPE, RMSE, and R2 values were obtained as 7.533, 0.027, and 0.9737 %, respectively, in the testing phase. Furthermore, it was found that power consumption decreased with decreasing of feed rate and cutting depth. Research highlights: The findings of this study can be used effectively in the forest industry to reduce the experimental time and costs.Öğe Doğu Kayını Ahşabının Yüzey Pürüzlülüğünün Bir Yapay Sinir Ağı ile Modellenmesi(2019) Ilçe, Abdullah Cemil; Singer, HilalBu çalışmanın amacı yapay sinir ağı (YSA) yaklaşımı ile doğu kayını (Fagus orientalis Lipsky) ahşabının yüzeypürüzlülüğünü modellemektir. İlk olarak, yatay bant zımpara makinesinin çalışma parametreleri (60-80-100zımpara numarası, 4-7-10 m/dk besleme hızı ve 0,1-0,2-0,3 mm kesme derinliği) belirlenmiştir. Numunelerinyüzey pürüzlülüğü deneysel olarak kaydedildikten sonra veriler eğitim ve test veri setlerine ayrılmıştır. Dahasonra, mevcut veriler Ra, Rq ve Rz’nin tahmin değerlerini doğru bir şekilde elde etmek için YSA yaklaşımı ilemodellenmiştir. Deneysel sonuçlar ile teorik bulgular arasındaki karşılaştırma (RRa = 0,99869, RRq = 0,9982 veRRz = 0,99882) birbirleriyle iyi bir uyum içinde olduğunu göstermektedir. Bu bağlamda, bu çalışma, kayın yüzeypürüzlülüğünün yapay sinir ağları yaklaşımı kullanılarak çok daha yüksek doğrulukta ve daha düşük hatalardamükemmel bir şekilde tahmin edildiğini göstermiştir.Öğe An interval-valued intuitionistic fuzzy analytic hierarchy process model for understanding consumer decision-making in non-wood forest product purchases(Elsevier Sci Ltd, 2024) Singer, Hilal; Ozsahin, SuekrueIn the current fast-paced market, it is essential for individuals to make well-informed choices when purchasing products. There is a growing consumer preference for products derived from renewable resources, particularly non-wood forest products (NWFPs). The process of making purchasing decisions for NWFPs is complex and influenced by various criteria. As the demand for NWFPs continues to rise, it becomes imperative to establish a comprehensive decision-making framework that examines the most significant criteria affecting consumers' purchasing decisions. This study proposes an interval-valued intuitionistic fuzzy analytic hierarchy process (AHP)-based decision-making framework for identifying and analyzing the key criteria influencing consumers' decisions when purchasing NWFPs. In light of the aim, six main criteria and thirty subcriteria are determined. A three-level hierarchical model is devised to systematically evaluate the identified criteria. The interval-valued intuitionistic fuzzy AHP method is employed to assign weights to the criteria. According to the results, health aspects is the most significant main criterion. The five most important subcriteria are determined as health benefits and harmlessness, absence of additives and preservatives, product price, quality standard and guarantee, and nutritional content and value. The findings of this study hold significance for consumers, businesses, and policymakers, as they provide valuable insights to facilitate informed decision-making.Öğe A LINEAR PROGRAMMING APPROACH TO ANALYZE MUSCULOSKELETAL DISORDER RISK FACTORS IN HAZELNUT HARVESTING WORKERS(2024) Singer, HilalHarvesting hazelnuts is a labor-intensive agricultural activity crucial for sustaining the global nut industry. Despite its significance, this activity poses a potential risk to workers’ musculoskeletal health due to the demanding nature of the work. This study proposes a linear programming approach to analyze risk factors associated with work-related musculoskeletal disorders among hazelnut harvesting workers. The initial phase of the study includes the identification of key risk factors through a literature review, field observations, and expert consultations. An expert team is formed to evaluate these factors from both academic and producer perspectives. The selection of the experts is done by considering their experience, educational background, knowledge, and publications relevant to the research topic. To determine the importance of the factors, the LP-GW-AHP method (a linear programming method to generate weights in the analytic hierarchy process) is employed. Once the pairwise comparison matrix is established, a mathematical model is created to obtain optimal weights. Additionally, a comparative analysis is conducted to support the validity of the model results. According to the results, harvest area, repetitive movements, and prolonged standing are the top three most important factors. Furthermore, the least important factors are determined to be experience, vibration, and mental and occupational stress. This study presents its novelty by formulating the evaluation of musculoskeletal disorder risk factors as a linear programming-driven multicriteria decision-making problem and applying the LP-GW-AHP method to the problem.Öğe Location Selection for a Lumber Drying Facility via a Hybrid Pythagorean Fuzzy Decision-making Approach(North Carolina State Univ Dept Wood & Paper Sci, 2024) Singer, HilalThe strategic selection of facility locations plays a critical role in optimizing operational efficiency, reducing costs, and enhancing customer satisfaction, thereby contributing significantly to the success and competitiveness of businesses. In this study, an interval-valued Pythagorean fuzzy decision-making framework is proposed to select the best location for the lumber drying industry. A four-level hierarchical model is devised with four main criteria, 16 subcriteria, and five alternatives. The opinions of different experts are gathered to obtain input data. The weights of the criteria are calculated using the interval-valued Pythagorean fuzzy analytic hierarchy process (AHP) method. The interval-valued Pythagorean fuzzy weighted aggregated sum product assessment (WASPAS) method is employed to evaluate the alternative locations. A sensitivity analysis is conducted to support the validity of the model results. The study concludes by revealing the optimal location for the lumber drying industry in Turkey. This study presents its novelty by formulating the lumber drying facility location selection problem as a complex fuzzy multicriteria decision-making problem and integrating the Pythagorean fuzzy AHP and WASPAS methods to solve the problem.Öğe Metallic biomaterial assessment via a risk-based decision-making approach(Gazi Univ, Fac Engineering Architecture, 2022) Singer, Hilal; Özçelik, Tijen ÖverThis study examined metallic biomaterials via a decision-making approach combining the fuzzy analytic hierarchy process (AHP), the fuzzy failure modes and effects analysis (FMEA), and the fuzzy evaluation based on distance from an average solution (EDAS) method. In the study, stainless steel, titanium, and cobalt-chromium alloys were assessed by employing six main criteria, thirty-one subcriteria, and three risk factors. The fuzzy AHP method was used to determine the importance of the evaluation criteria and risk factors, while the fuzzy EDAS method was employed to analyze the risk priority numbers obtained from the fuzzy FMEA method. According to the results, the first three important criteria were infection, carcinogenicity, and tensile strength. Furthermore, the ranking of the materials in the descending order of the crisp scores was titanium, stainless steel, and cobalt-chromium alloys. Consequently, this study established a foundation for the unbiased assessment and prioritization of current materials.Öğe Mobilya Üretiminde Malzeme Kombinasyonu Seçimi İçin Çok Kriterli Bir Çözüm Yaklaşımı(2024) Singer, Hilal; Ilçe, Abdullah CemilGünümüzde, çevresel sürdürülebilirlik ve teknolojideki ilerlemeler endüstrilerin ilgisini çevreye daha duyarlı ve yenilikçi malzemelere yönlendirmiştir. Odun-plastik kompozit (OPK) malzemeler, doğal kaynakların korunmasına ve çevre kirliliğinin azaltılmasına katkıda bulunurken aynı zamanda dayanıklı bir malzeme seçeneği sunmaktadır. Bu kompozit malzemelerin performansı içerdikleri malzemelerin kombinasyonları ile yakından ilişkilidir. En uygun malzeme kombinasyonunun belirlenmesi spesifik uygulama gereksinimlerini karşılayan ürünler geliştirmede üreticilere, tasarımcılara ve malzeme mühendislerine yardımcı olabilmektedir. Bu çalışma, mobilya üretimi için uygun malzeme kombinasyonlarını seçme sürecinde kullanılmak üzere bütünleşik bir BWM-WASPAS yaklaşımı sunmaktadır. Doğu kayını ve polikarbonat levhaların farklı kombinasyonları fiziksel ve mekanik özellikler göz önüne alınarak değerlendirilmektedir. BWM yöntemi karar kriterlerini önceliklendirirken, alternatiflerin öncelik sıralamasını belirlemek için WASPAS yöntemi kullanılmaktadır. Çalışmanın son aşamasında, sıralama sonuçlarını desteklemek için bir duyarlılık analizi gerçekleştirilmektedir. Bu çalışma, mobilya endüstrisinde malzeme katman organizasyonu değerlendirme problemini karmaşık bir çok kriterli karar verme problemi olarak formüle ederek ve malzeme kombinasyonu seçimi için BWM ve WASPAS yöntemlerini bütünleştirerek yeniliğini sunmaktadır.Öğe Multicriteria evaluation of structural composite lumber products(2020) Singer, Hilal; Özşahin, ŞükrüIn this study, laminated veneer lumber, parallel strand lumber, and laminated strand lumber were evaluated via multicriteria decision-making methods. Within the model, nine evaluation criteria were defined: moisture content, density, bending strength, modulus of elasticity, compression strength parallel to grain, dynamic bending strength, tensile strength parallel to surface, tensile strength perpendicular to surface, and screw holding capacity. The weights of the criteria were computed using the fuzzy analytic hierarchy process (FAHP). The evaluation based on distance from an average solution (EDAS) and the technique for order preference by similarity to an ideal solution (TOPSIS) were employed to determine the ranking of the alternatives. After the borda count method was used, an integrated ranking was obtained. According to the results, the first three important subcriteria were density, bending strength, and modulus of elasticity. Furthermore, laminated veneer lumber was determined as the best alternative. Consequently, this study can present a road map to evaluate wooden materials.Öğe A multiple criteria analysis of factors influencing surface roughness of wood and wood-based materials in the planing process(Univ Federal Lavras-Ufla, 2020) Singer, Hilal; Özsahin, ŞükrüThis paper presents a study of the fuzzy analytical hierarchy process (FAHP) for the prioritization of factors having important effects on the surface roughness of wood and wood-based materials in the planing process. Firstly, a three-level hierarchical model was devised. Secondly, the FAHP method was employed to determine the weights of the factors. Finally, the prioritization of the factors was carried out taking into account the weights. The results showed that the most significant factors are feed speed (0.300), tool geometry (0.222), and material defect (0.107). Consequently, this study provides a valuable guide to the wood industry to improve the surface quality of wood and wood-based products.Öğe Odun Yüzey Pürüzlülüğü Tahmininde Bir Yapay Sinir Ağı Modelinin Kullanılması(2019) Özşahin, Şükrü; Singer, HilalAğaç malzemelerin yüzey pürüzlülüğü, nihai ürünlerin kalitesinin değerlendirilmesi açısından çok önemlidir. Bunedenle bu çalışmada, odun türü, bıçak sayısı, besleme hızı ve kesme derinliğinin planyalama işleminde yüzeypürüzlülüğü üzerindeki etkisini modellemek için bir yapay sinir ağı (YSA) modeli geliştirilmiştir. Farklı YSAmodelleri oluşturulmuş ve bunların performansı ortalama mutlak yüzde hata (MAPE), ortalama karesel hatanınkarekökü (RMSE) ve determinasyon katsayısı (R2) kullanılarak değerlendirilmiştir. Önerilen modelin testsafhasındaki MAPE, RMSE ve R2 değerleri sırasıyla %7,27, 0,57 ve 0,903 olmuştur. Sonuç olarak YSA,planyalanan odunun yüzey pürüzlülüğünü tahmin etmede etkili bir araçtır ve maliyetli ve zaman alıcıaraştırmalar yerine oldukça yararlıdır.Öğe Prediction of noise emission in the machining of wood materials by means of an artificial neural network(Scion, 2022) Özşahin, Şükrü; Singer, HilalBackground: Noise produced during machining of wood materials can be a source of harm to workers and an environmental hazard. Understanding the factors that contribute to this noise will aid the development of mitigation strategies. In this study, an artificial neural network (ANN) model was developed to model the effects of wood species, cutting width, number of blades, and cutting depth on noise emission in the machining process. Methods: A custom application created with MATLAB codes was used for the development of the multilayer feed-forward ANN model. Model performance was evaluated by numerical indicators such as MAPE, RMSE, and R-2. Results: The ANN model performed well with acceptable deviations. The MANE, RMSE, and R-2 values were 0.553%, 0.600, and 0.9824, respectively, in the testing phase. Furthermore, this study predicted the intermediate values not provided from the experimental study. The model predicted that lower noise emissions would occur with decreased cutting width and cutting depth. Conclusions: ANNs are quite effective in predicting the noise emission. Practitioners relying on the ANN approach for investigating the effects of various factors on noise emission can save time and costs by reducing the number of experimental combinations studied to generate predictive models.Öğe Prioritization of factors affecting surface roughness of wood and wood-based materials in CNC machining: a fuzzy analytic hierarchy process model(Taylor & Francis Ltd, 2020) Singer, Hilal; Özşahin, ŞükrüIn this study, the fuzzy analytic hierarchy process was employed to prioritize some factors affecting the surface roughness of wood and wood-based materials in CNC machining. Within the model, four main factors and eighteen subfactors were defined. After constructing the hierarchical structure, the factors were analyzed through experts' opinions. The results demonstrated that wood properties and machining parameters were the most significant main factors. Furthermore, density was found to be the most important subfactor. Consequently, the findings of this study will help the wood industry in enhancing the surface quality of final products.Öğe Prioritization of laminate flooring selection criteria from experts' perspectives: a spherical fuzzy AHP-based model(Taylor & Francis Ltd, 2022) Singer, Hilal; Ozsahin, SukruLaminate flooring selection is an important phase in the design and construction of a building. Owing to the subjectivity and conflicting factors, the fuzzy multicriteria decision-making technique can be used for handling the problem. In this study, it is aimed to prioritize laminate flooring selection criteria from experts' perspectives. Five main criteria and twenty subcriteria are determined with the help of experts. A three-level hierarchy is devised for comparisons. The spherical fuzzy analytic hierarchy process is used to determine the importance of each criterion. According to the results, 'safety and health properties' (29.0%) and 'durability properties' (20.6%) are the most significant main criteria. Furthermore, 'walking safety' (10.7%), 'free of harmful substances' (8.6%), and 'scratch resistance' (6.7%) are found as the most important subcriteria. The proposed approach presents a different view because it contributes to analyzing the key attributes of laminate flooring. The findings of this study will help building owners, architects, and designers in making informed choices.Öğe A risk-based decision making framework to analyze the properties of cobalt-chromium alloys(Emerald Group Publishing Ltd, 2023) Singer, Hilal; Oezcelik, Tijen OeverIn this study, the properties of cobalt-chromium alloys are systematically prioritized to aid in the minimization of orthopedic implant failures and risks in biomedical applications. Within the model, six main groups (including a total of 31 properties) are defined: economic aspects, design and production properties, mechanical properties, physical properties, chemical properties and biological properties. A risk-based fuzzy decision making framework is proposed for prioritization. First, a risk-management decision matrix is created by employing interval type-2 fuzzy failure modes and effects analysis. Afterward, the properties of cobalt-chromium alloys are analyzed by utilizing interval type-2 fuzzy measurement of alternatives and ranking according to compromise solution. In the last phase, a prediction model is devised with an adaptive network fuzzy inference system to save computational time and effort and to enable the incorporation of new scientific results into the biomaterial evaluation process. The results of the current study demonstrate that compatibility, osseointegration, corrosion resistance, fatigue resistance and time-dependent deformation are the top five properties contributing to potential orthopedic implant failures and risks. Furthermore, the developed model produces very satisfactory results with acceptable deviations. Consequently, this study presents a new and reliable guide for the unbiased evaluation of cobalt-chromium alloys.
