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Projects / Programmes source: ARIS

Intelligent monitoring and optimization system for high speed milling

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Research activity

Code Science Field Subfield
2.10.00  Engineering sciences and technologies  Manufacturing technologies and systems   

Code Science Field
T130  Technological sciences  Production technology 
T125  Technological sciences  Automation, robotics, control engineering 
Keywords
Monitoring of machining process, artifficial intelligence, high speed machining
Evaluation (rules)
source: COBISS
Evaluation of bibliographic research performance indicators according to ARIS methodology
Citations Citations for bibliographic records in COBIB.SI that are linked to records in citation databases
source: WoS source: Scopus source: COBISS
Researchers (2)
no. Code Name and surname Research area Role Period No. of publications
1.  06883  PhD Janez Kopač  Manufacturing technologies and systems  Head  2004 - 2006  1,837 
2.  19093  PhD Matjaž Milfelner  Manufacturing technologies and systems  Researcher  2004 - 2006  201 
Organisations (2)
no. Code Research organisation City Registration number No. of publications
1.  0782  University of Ljubljana, Faculty of Mechanical Engineering  Ljubljana  1627031  29,252 
2.  0795  University ob Maribor, Faculty of mechanical engineering  Maribor  5089638010  23,914 
Abstract
The main objective of this project is to develop the most advanced and intelligent manufacturing system for high speed machining, which could be applied in the next generation manufacturing enterprises. The system will combines different methods and technologies like evolutionary methods, manufacturing technology, high speed machining technology, measuring and control technology and intelligent process technology with the adequate hardware and software support. The aim of this project is to develop the intelligent monitoring and optimization system (IMOS) that can reliably identify the machining process conditions based on the information obtained with a sensor system. Based on the knowledge of the machine, process, tooling, and machining task, the monitoring system can provide the machine tool control system with information to optimize the machining process with the use of artificial intelligence. The project covers a broad range of high speeed machining tasks such as milling, drilling, grinding and turning. They can lead to the development of generic interfaces, systems, and algorithms portable to different processes, machine tools, and numerical controllers. The introduction of user-knowledge-based approach and autonomous intelligent algorithms into machine tool control systems will enable machining systems to achieve higher accuracy, higher productivity, and minimum malfunctions.
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