Browsing by Author "Tansel, Yusuf I. C."

Now showing 1 - 2 of 2

Investigation of the Additional Powder Effect on the Strength of Joined Aluminum Alloy Plates in Friction Stir Welding Using the Topsis-Game Theory Model
(Başkent Üniversitesi Mühendislik Fakültesi, 2024-05-03) Yurdakul, Mustafa; Ulke, Ibrahim; Tansel, Yusuf I. C.
Friction stir welding (FSW) is a process that can join many materials by causing minimal internal stress without the need for a direct electric current, contrary to traditional welding methods. The effects of SiC and Al2O3 reinforcing powders on the joining of AA6061-T6 and AA7075-T6 plates, which are difficult to join with conventional welding methods by FSW, are investigated in this study. The metallurgical properties of the combined samples are examined in terms of strength characteristics to investigate the effects of the reinforcement powder. In addition, elemental analysis is carried out for the mixing behavior of the powders. Finally, we used the TOPSIS method to select the most appropriate powder types to improve welding quality. Furthermore, a game theory application is presented to determine which powder type is suitable considering the joined aluminum plate's strength expectations.
A new DoE-MTOPSIS based prediction model suggestion to capture potential SARS-CoV-2 reactivated patients
(2021) Tansel, Yusuf I. C.; 0000-0001-9274-7467; AGE-3003-2022
Difficulties to use convenient data during the Severe Acute Respiratory Syndrome Coronavirus2 (SARS-CoV-2) pandemic outbreak and complexities of the problem attitude crucial challenges in infectious disease modelling studies. Motivated by the on-going reach to predict a potential reactivated SARS-CoV-2 (COVID-19), we suggest a prediction model that beyond the clinical characteristics based evaluation approaches. In particular, we developed a possibly available and more efficient prediction model to predict a potential reactivated SARS-CoV-2 (COVID-19) patient. Our paper aims to explore the applicability of a modified Technique for Order Preference by Similarity to Ideal Solutions (MTOPSIS) integrated Design of Experiment (DoE) method to predict a potential reactivated COVID-19 patient in real-time clinical or laboratory applications. The presented novel model may be of interest to the readers studying similar research areas. We illustrate MTOPSIS integrated DoE method by applying it to the COVID-19 pandemic real clinical cases from Wuhan/China-based data. Despite the small sample size, our study provides an encouraging preliminary model framework. Finally, a step by step algorithm is suggested in the study for future research perspectives.