Intelligent adaptive control and monitoring of band sawing using a neural-fuzzy system |
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| Authors: | ?lhan Asiltürk Ali Ünüvar |
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| Affiliation: | 1. Faculty of Technical Education, Selcuk University, Konya 42250, Turkey;2. Faculty of Mechanical Engineering, Selcuk University, Konya 42250, Turkey;1. University of Selcuk, Faculty of Technology, Konya 42075, Turkey;2. University of Selcuk, Faculty of Engineering, Konya 42075, Turkey;1. RWTH Aachen University, Institute of Technical and Macromolecular Chemistry, Worringerweg 2, 52074, Aachen, Germany;2. University of Potsdam, Institute of Earth and Environmental Science, Karl-Liebknecht-Str. 24-25, 14476, Potsdam, Germany;3. Paul Scherrer Institut, NIAG, Paul Scherrer Institute, 5232, Villigen – PSI, Switzerland;4. Research Center Jülich, IBG-3, 52425, Jülich, Germany;1. School of Instrument Science and Opto-Electronic Engineering, Hefei University of Technology, Hefei 230009, China;2. College of Electronic and Information Engineering, Anhui JIANZHU University, Hefei 230601, China |
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| Abstract: | In bandsaw machines, it is desired to feed the bandsaw blade into the workpiece with an appropriate feeding force in order to perform an efficient cutting operation. This can be accomplished by controlling the feed rate and thrust force by accurately detecting the cutting resistance against the bandsaw blade during cutting operation. In this study, a neural-fuzzy-based force model for controlling band sawing process was established. Cutting parameters were continuously updated by a secondary neural network, to compensate the effect of environmental disturbances. Required feed rate and cutting speed were adjusted by developed fuzzy logic controller. Results of cutting experiments using several steel specimens show that the developed neural-fuzzy system performs well in real time in controlling cutting speed and feed rate during band sawing. A material identification system was developed by using the measured cutting forces. Materials were identified at the beginning of the cutting operation and cutting force model was updated by using the detected material type. Consequently, cutting speed and feed rate were adjusted by using the updated model. The new methodology is found to be easily integrable to existing production systems. |
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