Browsing by Author "Kilic, S. E."

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Citation - WoS: 10
Citation - Scopus: 13
A Multi-Agent System Model for Partner Selection Process in Virtual Enterprise
(Elsevier Science Bv, 2014) Sadigh, B. Lotfi; Arikan, F.; Ozbayoglu, A. M.; Unver, H. O.; Kilic, S. E.; Department of Mechanical Engineering; Manufacturing Engineering; 15. Graduate School of Natural and Applied Sciences; 06. School Of Engineering; Mechanical Engineering; 01. Atılım University
Virtual Enterprise (VE) is a collaboration model between multiple business partners in a value chain. VE information system deals with highly dynamic information from heterogeneous data sources. In order to manage and store dynamic VE information in the database, an ontology based VE model has been developed. To select winner enterprises in VE, a Multi Agent System (MAS) has been developed. Communication and data transition among agents and system entities are based on defined rules in VE ontology model. One of the most important contributions of agents in VE system is in partner selection step of VE formation phase. In this step several agents with different goals and strategies are collaborating and competing each other to win the negotiation procedure or maximize the profit for their assigned enterprise. Different strategies are developed for the agents depending on their appetite for winning the auction against maximizing the profit. Several simulations were run and the results are stored. These results are fed into a neural network in order to predict which enterprise will win the auction and what will be the profit margin. The motivation is to provide a forecasting agent for the customers about the outcomes of the auctions so that they can plan ahead and take the necessary action. Early results indicate such simulated multi-agent VE formations can be used in real systems. A Multi-Agent System Model for Partner Selection Process in Virtual Enterprise (C) 2014 Published by Elsevier B.V.
Citation - WoS: 15
Citation - Scopus: 21
Tool-Life Modelling of Carbide and Ceramic Cutting Tools Using Multi-Linear Regression Analysis
(Sage Publications Ltd, 2006) Amaitik, S. M.; Tasgin, T. T.; Kilic, S. E.; 01. Atılım University
This paper presents a study for the development of tool-life models for machining operations by means of a statistical approach called multi-linear regression analysis. The study was applied to a milling process for machining SAE 121 cast iron in a factory without interrupting the mass production. Different cutting tool materials under dry conditions were used in the cutting tests. Several machining experiments were performed and mathematical models for tool life have been postulated by using least-square regression analysis. The analysis was based on a first-order model in which the tool life is expressed as a function of two independent variables; cutting speed and feed rate. Analysis of variance was applied to check the adequacy of the mathematical models and their respective parameters. In order to demonstrate the usefulness of the developed models, tool-life contours have been generated and presented in different plots.