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Article Citation - WoS: 77Citation - Scopus: 101An Intelligent Process Planning System for Prismatic Parts Using Step Features(Springer London Ltd, 2007) Amaitik, Saleh M.; Kilic, S. EnginThis paper presents an intelligent process planning system using STEP features (ST-FeatCAPP) for prismatic parts. The system maps a STEP AP224 XML data file, without using a complex feature recognition process, and produces the corresponding machining operations to generate the process plan and corresponding STEP-NC in XML format. It carries out several stages of process planning such as operations selection, tool selection, machining parameters determination, machine tools selection and setup planning. A hybrid approach of most recent techniques ( neural networks, fuzzy logic and rule-based) of artificial intelligence is used as the inference engine of the developed system. An object-oriented approach is used in the definition and implementation of the system. An example part is tested and the corresponding process plan is presented to demonstrate and verify the proposed CAPP system. The paper thus suggests a new feature-based intelligent CAPP system for avoiding complex feature recognition and knowledge acquisition problems.Article Citation - WoS: 11Citation - Scopus: 22Ore-Age: a Hybrid System for Assisting and Teaching Mining Method Selection(Pergamon-elsevier Science Ltd, 2003) Guray, C; Celebi, N; Atalay, V; Pasamehmetoglu, AGMining method selection is among the most critical and problematic points in mining engineering profession. Choosing a suitable method for a given ore-body is very important for the economics, safety and the productivity of the mining work. In the past studies there are attempts to build up a systematic approach to help the engineers to make this selection. But, these approaches work based on static databases and fail in inserting the intuitive feelings and engineering judgments of experienced engineers to the selection process. In this study, a system based on 13 different expert systems and one interface agent is developed, to make mining method selection for the given ore-bodies. The agent Ore-Age, to follow his goal of supplying the maximum assistance to engineers in selecting the most suitable method for a specific ore-body, tries to learn the experiences of the experts he has faced. After this learning process the knowledge base is evolved to include these experiences, making the system more efficient and intuitive in mining method selection work. To realize the above goal, the system's tutoring procedure is executed by the agent, in case an inexperienced user enters the system, to complete his/her missing knowledge about mining method selection. (C) 2002 Elsevier Science Ltd. All rights reserved.Article Citation - WoS: 51Citation - Scopus: 63Cue-based aggregation with a mobile robot swarm: a novel fuzzy-based method(Sage Publications Ltd, 2014) Arvin, Farshad; Turgut, Ali Emre; Bazyari, Farhad; Arikan, Kutluk Bilge; Bellotto, Nicola; Yue, ShigangAggregation in swarm robotics is referred to as the gathering of spatially distributed robots into a single aggregate. Aggregation can be classified as cue-based or self-organized. In cue-based aggregation, there is a cue in the environment that points to the aggregation area, whereas in self-organized aggregation no cue is present. In this paper, we proposed a novel fuzzy-based method for cue-based aggregation based on the state-of-the-art BEECLUST algorithm. In particular, we proposed three different methods: naive, that uses a deterministic decision-making mechanism; vector-averaging, using a vectorial summation of all perceived inputs; and fuzzy, that uses a fuzzy logic controller. We used different experiment settings: one-source and two-source environments with static and dynamic conditions to compare all the methods. We observed that the fuzzy method outperformed all the other methods and it is the most robust method against noise.
