Advanced Techniques for Monitoring, Simulation and Optimization of Machining Processes

The new enhancements in the machine tools sector has enabled opportunities for rapid growth in production rate and high varieties in products in minimum time. But they also raised questions concerning sustainability of exiting manufacturing resources. Buying new machines and tooling lead to huge investments. However, buying new machines and tooling doesn't solve production profitability problems if they are underutilized. In order to find the answer of sustainability of existing manufacturing resources and enhancement of the machining efficiency, researchers are looking forward finding possible supplementary computer supports. In recent years, the indirect computer supported techniques such as sensor base monitoring and control and virtual machining process simulation have been greatly accepted for real-time decision making, sustainable development and efficient use of machining resources. This research work is focused on the development of advanced techniques for monitoring, simulation and optimization of machining processes. The thesis presents the contributions to the development and application of decision support systems for three fundamental aspects of machining processes. 1) Chip form monitoring and process condition monitoring by sensor data clustering and classification. 2) Real time tool wear measurement by tool wear image processing. 3) Machining operation verification and optimization using machining process simulation.