MEASUREMENT AND PREDICTION OF ERRORS FOR A THREE AXIS MACHINE TOOL THROUGH METROLOGY FEEDBACK
Muhammad Hassan Yaqoob
Industrial Engineering, MSc. Thesis, 2017
Prof. Dr. Erhan Budak (Thesis Supervisor)
Assoc. Prof. Dr. Bahattin Koç
Assoc. Prof. Dr. Umut Karagüzel
Date & Time: 28th, July, 2017 – 10:00 AM
Place: FASS G022
Keywords : Error identification, workpiece measurement, metrology feedback, thermal error measurement, dynamic error measurement
The evolution of machine tools, driven by ever growing requirement for high precision machining, has warranted the importance of understanding and compensation of errors in machine tools. There are a considerable number of research studies dealing with the modelling, measurement and compensation of errors. Machined workpiece geometry provides an opportunity for determination of errors generated during machining process. However, examination of the current literature reveals that the overall progress, in error determination through workpiece measurements, is limited to reporting of mainly positional errors.
There is need for development of a comprehensive methodology that can help determine not only the mechanical errors but also the errors being generated due to process parameters, geometry of workpiece and changes in thermal state of the machine tool. Such a methodology would not only provide comprehensive error magnitude in real-time scenario but would also provide the decision makers with the ability to decide whether to compensate the errors on workpiece or to carry out corrective measures on a machine tool. The current research seeks to develop such a methodology for error measurement and prediction in a Three Axis machining center. A combination of Machining under different conditions followed by subsequent on-machine probing and measurements on a coordinate measuring machine (CMM) are used to obtain error database with appreciation for process, thermal, control and mechanical errors. The proposed methodology is generic with respect to the shape and size of the workpiece, tool geometry and machine tool of similar configuration. The results include a prediction model that enables the user with the ability of pre-machining assessment of expected errors and final geometrical dimensions of a workpiece. This in turns reduces the quality costs, improves decision making meanwhile the simplicity of experimentation essentially offers a low-cost shop-floor friendly solution.