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Process characterization and evaluation of NC machining processes based on macroscopic engagement simulation

: Minoufekr, M.; Schug, P.; Joshi, M.


Filipe, J. ; Institute for Systems and Technologies of Information, Control and Communication -INSTICC-, Setubal:
11th International Conference on Informatics in Control, Automation and Robotics 2014. Proceedings. Vol.2 : Vienna, Austria, 1 - 3 September, 2014; Including the Special Session on Intelligent Vehicle Controls & Intelligent Transportation Systems (IVC&ITS), and the Special Session on Adaptive Stochastic Algorithms and their Applications at Human-Machine Interfaces (ASAAHMI)
Piscataway, NJ: IEEE, 2014
ISBN: 978-989-758-040-6
ISBN: 978-1-4799-7920-2
International Conference on Informatics in Control, Automation and Robotics (ICINCO) <11, 2014, Vienna>
Conference Paper
Fraunhofer IPT ()

With a view to achieve stable production, nowadays the process design and planning goes through a time and resource intensive correction loop. The process output after machining trials is used to determine the critical process sections, and hence the experience of the process designer is decisive in the productivity of the optimization loop and the process. The implementation of a machining simulation can enhance the productivity of the process design and planning phase. The macroscopic engagement simulation provides an efficient tool for process evaluation. Moreover, it provides a basis for derivation of microscopic geometric process parameters, which have a direct correlation to mechanical and thermal loads. Thus, detailed information relating to the cutting loads on the tool is derivable at every point on the toolpath, enabling analysis of NC machining process based purely on the macroscopic geometric engagement between the cutting tool and workpiece. This information regarding the engagement conditions can be used to proactively identify potential critical process sections in a virtual environment thereby increasing the process reliability. Thus a process design for an optimal tool load is possible resulting in improved tool life, process efficiency and reduction in utilised resources.