Browsing by Author "Araz O.U."

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    A facility layout problem in a marble factory via simulation
    (Association for Scientific Research, 2011) Edis R.S.; Kahraman B.; Araz O.U.; Özfirat M.K.
    The marble factory in this study is a typical instance of a flow shop based production system. Adding new machines to the plant and/or introducing a new product may convert the actual layout to an inefficient one. Such cases may cause a significant increase in transportation of materials between machines that decreases the utilization rates of machines and operators as well as overall productivity. Therefore, facility planning is a key issue in marble plants in terms of total cost and customer satisfaction. Another important property of these plants is its dynamic and stochastic behavior in terms of scrap rates, demands and processing times. The aim of this study is to develop an efficient plant layout for such dynamic systems. At first, the simulation model of the current system is built on ARENA 10.0. Then, an alternative layout is generated after some analysis and then, it is evaluated via simulation model. The proposed layout provides reduction in total transportation time as well as an increase in productivity. © Association for Scientific Research.
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    Modeling of the lyotropic cholesteric liquid crystal based toxic gas sensor using adaptive neuro-fuzzy inference systems
    (Elsevier Ltd, 2024) Araz O.U.; Kemiklioglu E.; Gurboga B.
    Detection of toxic gases is important in a variety of settings, including industrial facilities, laboratories, and even in homes. In these settings, toxic gas detection can help prevent accidents and protect the health and safety of workers, researchers, and others who may be exposed to these gases. This study evaluates an Adaptive Neuro-Fuzzy Inference System (ANFIS) models in predicting the machining responses in the detection of toxic gases vapor, such as toluene (T), phenol (P) and 1,2 dichloropropane (D) using lyotropic cholesteric crystal (CLC) have been shown to have potential as gas sensors due to their unique optical and liquid crystal (LC) properties, and the ANFIS model may be used to better understand and optimize these properties for toxic gas detection. Experiments were carefully carried out to gather data on the response of a lyotropic CLC toxic gas vapor sensor. The effectiveness of using ANFIS combined with Grid Partitioning (GP) was then carefully studied and evaluated in terms of modeling and predicting the responses of the sensor. The best ANFIS-GP model is chosen from these criteria; RSS, PCC, R2, RMSE, MSE, MAE, and MAPE. In addition, validation was performed between the model and experimental data using the LOOCV method. The results show that the ANFIS-GP5 model with 96 fuzzy inference systems (FIS) rules with high R2 values. According to the ANFIS-GP5 model, R2varied ranges from 0.77 to 1 for train, test, and total data of lyotropic CLC sensor exposed to toluene, phenol and 1,2 dichloropropane toxic gases vapors. © 2023 Elsevier Ltd