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Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395

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Start date: 2020Subject: search.filters.subject.Sensitivity Analysis

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  • Article
    Optimization of Precision Machine Part Manufacturing by Integration of Grey-Taguchi Method with Principal Component Analysis
    (Yildiz Technical University, 2026) Kapan Ulusoy, Selda; Şenyiğit, Ercan; Erol, Kübra; Ulusoy, Selda Kapan
    Determining and optimizing the process parameters impacting the outputs at each production stage is necessary to reduce production costs. The Taguchi Method (TM) and the Grey Relational Analysis (GRA) are commonly utilized two techniques for process parameter optimization. In precision machine part manufacturing, Computer Numerical Control (CNC) production is the most critical process. In this study, the objective is to optimize CNC manufacturing parameters using TM, GRA and Principal Component Analysis (PCA) in metal sector. Process parameters like operator experience level (in years), CNC machine brand, CNC machine age, and CNC machine size were determined and optimized based on their degree of impact on the outputs. The experiments were carried out using a four-factor, four-level Taguchi orthogonal array (L16), and Analysis of Variance (ANOVA) was conducted aiming to determine the effects of these process parameters on production time, dimension conformity, and surface roughness performance factors. Selection of these input parameters and performance factors in the study is to provide a solution to a problem in the company from which the data are obtained with scientific methods and to contribute to the literature. Utilizing TM, the optimal values of process parameters are determined as ten years for operator experience, as Mazak for CNC machine brand, as two years for machine age, and as 500x550x550 for machine size. Utilizing the combination of GRA and PCA optimal parameter values are determined as ten years for operator experience, as Yuntes for CNC machine brand, as two years for machine age, and as 700x450x500 for machine size. A sensitivity analysis was performed using 21 different weight sets for performance factors (production time, dimension conformity, and surface roughness). Compared to the initial CNC production process parameters, 45%, 95%, and 504% improvements were obtained in production time, dimension conformity, and surface roughness process parameters. Companies, especially operating in the metal sector, can benefit from managerial practices by considering the ranking of parameters affecting CNC production according to the results obtained from this study.
  • Article
    Citation - WoS: 38
    Citation - Scopus: 45
    Selection of an Appropriate Acid Type for the Recovery of Zinc From a Flotation Tailing by the Analytic Hierarchy Process
    (Elsevier Sci Ltd, 2021-02) Kursunoglu, Sait; Kursunoglu, Nilufer; Hussaini, Shokrullah; Kaya, Muammer
    The selection of acid type for metal dissolution from minerals is an important issue in leaching operations. Acids are used to recover valuable elements from the minerals by dissolving them in a solution. The acid must offer a high recovery at marginal cost and a low environmental effect. Many parameters can affect the acid type selection for high leaching recovery and low environmental effect and thus, the selection of an acid type is complex. In this study, based on the experimental results obtained from the bench-scale laboratory studies, the selection of acid type for the recovery of zinc from a flotation tailing was investigated using the analytic hierarchy process (AHP). The utilization of AHP was supported by the use of ExpertChoice (R) 2000 software. The outcomes demonstrated that sulfuric acid is the most desirable acid type with a ranking of 0.541, tracked by citric acid, and oxalic acid with scoring of 0.282 and 0.177, respectively. Furthermore, analyses of sensitivity were performed to examine the influence of the main criteria on the different acid type. It emerged that citric acid can be used when the environmental main criterion ascended from 7.8% to 75.3%. (C) 2020 Elsevier Ltd. All rights reserved.