The global market competition has drawn the manufacturer’s attention on automated manufacturing processes using condition monitoring systems. These systems have been used for improving product quality, eliminating inspection, and enhancing manufacturing productivity. Fixtures are essential devices in machining processes to hold the tool or workpiece, hence they are influenced directly by the stability of the cutting tool. Therefore, tool and fixturing faults play an important part in the inaccuracy of the machining processes causing deterioration of surface roughness. For the above mentioned reasons, and the limited work in this domain, this thesis develops an experimental investigation to evaluate the effect of fixturing quality on the design of condition monitoring systems. The proposed monitoring system implements multisensors and signal processing methods able to analyse the sensory information and make an appropriate decision. Therefore, several sensors namely force, vibration, acoustic emission, eddy current, power, strain and sound, are combined with a newly suggested approach, named Taylor’s Equation Induced Pattern (TIP), and neural networks to detect tool wear and tool breakage. It also evaluates the monitoring system to provide valuable data to show the effect of fixturing quality. Surface roughness of the workpiece has been measured and compared with the sensitivity of the monitoring system, which reflects the state of tool and fixturing conditions. A novel approach, termed ASPSF, (Automated Sensor and Signal Processing Selection for Fixturing) has been implemented to select the most sensitive sensors and signal processing method. The aim is to reduce the number of sensors needed in the overall system and reduce the cost. New automated detection methods (Principal Component Analysis (PCA), Fuzzy logic, correlation coefficients) have been implemented to prove the capability of the approach. A cost reduction is performed based on removing least utilised sensors without losing the performance of the condition monitoring system. The results prove that the ASPSF is capable of detection the effect of fixturing quality on the design of the condition monitoring system and the trend in surface roughness. Consequently, the findings of this thesis prove that the change in the fixturing quality could have significant effect on the design of the condition monitoring system and the behaviour of the system. Therefore, continuous condition monitoring design process will be needed regularly for every machine, to allow compensation in the change in the characteristics.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:629298 |
| Date | January 2013 |
| Creators | Abbas, J. K. |
| Publisher | Nottingham Trent University |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://irep.ntu.ac.uk/id/eprint/7/ |
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