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1.
A model of tool wear monitoring system for turning
Aco Antić, Goran Šimunović, Tomislav Šarić, Mijodrag Milošević, Mirko Ficko, 2013, izvirni znanstveni članek

Opis: Acquiring high-quality and timely information on the tool wear condition in real time, presents a necessary prerequisite for identification of tool wear degree, which significantly improves the stability and quality of the machining process. Defined in this paper is a model of tool wear monitoring system with special emphasis on the module for acquisition and processing of vibration acceleration signal by applying discrete wavelet transformations (DWT) in signal decomposition. The paper presents a model of the developed fuzzy system for tool wear classification. The system comprises three modules: module for data acquisition and processing, module for tool wear classification, and module for decision-making. The selected method for feature extraction is presented within the module for data classification and processing. The selected model for the fuzzy classifier and classification in experimental laboratory conditions is shown within data classification and clustering. The proposed model has been tested in longitudinal and transversal machining operations.
Ključne besede: artificial intelligence, tool wear monitoring, feature extraction
Objavljeno: 10.07.2015; Ogledov: 314; Prenosov: 46
.pdf Celotno besedilo (1,87 MB)
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2.
Intelligent design and optimization of machining fixtures
Djordje Vukelić, Goran Šimunović, Branko Tadic, Borut Buchmeister, Tomislav Šarić, Nenad Simeunovic, 2016, izvirni znanstveni članek

Opis: This work presents an integral system for machining fixture layout design and optimization. The optimization module of this system allows determination of optimal positions of locating and clamping elements, which provides required accuracy and surface quality, while at the same time guarantees design of collision-free fixtures. The design module performs selection of required fixture elements based on a set of predefined production rules. Adequate criteria for the selection of fixture elements are defined for locating, clamping, tool guiding, and tool adjustment elements, as well as for fixture body elements, connecting elements and add-on elements. The system uses geometry and feature workpiece characteristics, as well as the additional machining, and process planning information. It has been developed to accommodate machining processes of turning, drilling, milling, and grinding of rotational and prismatic workpieces. A segment of output results is also shown. Finally, conclusions are presented with directions for future investigation.
Ključne besede: artificial intelligence, fixture, process planning
Objavljeno: 12.07.2017; Ogledov: 279; Prenosov: 181
.pdf Celotno besedilo (2,97 MB)
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