A multi-level modular control scheme to realize integrated process monitoring, diagnosis and control for intelligent machining is proposed and implemented. PC-based hardware architecture to manipulate machining process cutting parameters, using a PMAC interface card as well as sensing processes performance parameters through sampling, and processing by means of DSP interface cards is presented. Controller hardware, to interface the PC-based PMAC interface card to a machining process for the direct control of speed, feed and depth of cut, is described. Sensors to directly measure on-line process performance parameters, including cutting forces, cutting sound, tool-workpiece vibration, cutting temperature and spindle current are described. The indirect measurement of performance parameter surface roughness and tool wear monitoring, through the use of NF sensor fusion modeling, is described and verified. An object based software architecture, with corresponding user interfaces (using Microsoft Visual C++ Foundation Classes and implemented C++ classes for sending motion control commands to the PMAC and receiving processed on-line sensor data from the DSP) is explained. The software structure indicates all the components necessary for integrating the monitoring, diagnosis and control scheme. C-based software code executed on the DSP for real-time sampling, filtering and FFT processing of sensor signals, is explained. Making use of experimental data and regression analysis, analytical relationships between cutting parameters (independent) and each of the performance parameters (dependent) are obtained and used to simulate the machining process. A fuzzy relation that contains values determined from statistical data (indicating the strength of connection between the independent and dependent variables) is proposed. The fuzzy relation forms the basis of a diagnostic scheme that is able to intelligently determine which independent variable to change when a machining performance parameter exceeds control limits. The intelligent diagnosis scheme is extensively tested using the machining process simulation.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:nmmu/vital:10814 |
| Date | January 2001 |
| Creators | Van Niekerk, Theo |
| Publisher | Port Elizabeth Technikon, Faculty of Engineering |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis, Doctoral, DTech (Engineering) |
| Format | 191 leaves, pdf |
| Rights | Nelson Mandela Metropolitan University |
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