Keshari, Anupam (2011) Advanced Techniques for Monitoring, Simulation and Optimization of Machining Processes. [Tesi di dottorato] (Unpublished)
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|Item Type:||Tesi di dottorato|
|Uncontrolled Keywords:||Machining Processes Simulation, Sensor Monitoring, Decision Support Systems|
|Date Deposited:||07 Dec 2011 15:46|
|Last Modified:||30 Apr 2014 19:48|
In today’s manufacturing industry, pressure for productivity, higher quality and cost saving is heavier than ever. Surviving in today’s highly competitive world is not an easy task, contemporary technology updates and heavy investments are needed in state of the art machinery and modern cutting tool systems. If the machining resources are underutilized, feasible techniques are needed to utilize resources efficiently. The new enhancements in the machine tools sector have 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 for sustainability of existing manufacturing resources and enhancement of the machining efficiency, researchers are working on 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 thesis 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 through sensor data clustering and classification. 2) Real time tool wear measurement through tool wear image processing. 3) Machining operation verification and optimization using machining process simulation.
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