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UBC Theses and Dissertations

Feature-based methodologies for milling process modeling in Virtual Machining Wang, Jue

Abstract

Virtual Machining is used to simulate the machining process prior to actual machining, thereby avoiding costly test trials on the shop floor. To realize Virtual Machining, two major methodologies, Geometric Modeling and Process Modeling, are required. In geometric modeling, Cutter/Workpiece Engagements (CWEs) are extracted to support force prediction in process modeling. In process modeling, the physics of the machining process, such as cutting forces, torque and power, are predicted by integrating the laws of the metal cutting process with CWEs generated in geometric modeling. Based on these predictions, process parameters can be optimized for productivity. Methodologies in geometric modeling for CWE extraction require a large number of calculations, however, the robustness and computational stability of these approaches is a significant challenge. In this thesis, methodologies are developed to address these problems in CWE extraction for the milling process. These methodologies achieve computational efficiency and stability by reducing the number of intersections that need to be performed and by parallelizing computational activities in the process of CWE extraction. A feature-based approach is presented for CWE extraction in 2 1/2 D end milling by introducing in-process machining features into process modeling. In hole milling, an analytical approach is presented for CWE extraction, and a NURBS based approach is developed for generating the swept volume for in-process workpiece modeling. A Multi-Agent System based framework is developed to improve efficiency of the CWE calculation, by parallelizing computational activities and utilizing free resources over a network.

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