Emergent Structure Detection for Multi-Axis Machining

This paper examines the phenomenon of emergent structures that occur in the transient stock material during multi-axis rough machining from a plurality of fixed orientations. Taking the form of thin webs and strings, emergent structures are stock material conditions that can lead to catastrophic failure during machining, even when tool path verification is successful. We begin by discussing the motivation for use of fixed orientations in multi-axis machining using multiple automated setups via rotary axes, which enables fast processing and ‘first part correct’ machining. Next, we demonstrate how unintended emergent structures occur in this paradigm of machining and can lead to catastrophic failure of the tool or work piece. Our original work focuses on the problem of geometric detection of these structures during process planning and prior to tool path planning, to the end of altogether avoiding emergent structure formation. To quickly simulate the machining process, we present an object-space method for determining the transient state of stock material based on the inverse tool offset. To identify emergent structures within this transient stock state, we propose a metric based on the medial axis transformation. Finally, we present our implementation of these methods and demonstrate realtime computation appropriate for an optimization scheme to eliminate emergent structures. Our methods provide consistent and logical results, as demonstrated with several freeform component examples. This work enables the development of robust algorithms for autonomous tool path planning and machining in multi-axis environments

On setup level tool sequence selection for 2.5-D pocket machining

Abstract This paper describes algorithms for efficiently machining an entire setup. Previously, the author developed a graph based algorithm to find the optimal tool sequence for machining a single 2.5-axis pocket. This paper extends this algorithm for finding an efficient tool sequence to machine an entire setup. A setup consists of a set of features with precedence constraints, that are machined when the stock is clamped in a particular orientation. The precedence constraints between the features primarily result from nesting of some features within others. Four extensions to the basic graph algorithm are investigated in this research. The first method finds optimal tool sequences on a feature by feature basis. This is a local optimization method that does not consider inter feature toolpath interactions. The second method uses a composite graph for finding an efficient tool sequence for the entire setup. The constrained graph and subgraph approaches have been developed for situations where different features in the setup have distinct critical tools. It is found that the first two methods can produce erroneous results which can lead to machine crashes and incomplete machining. Illustrative examples have been generated for each method.

ME-EM 2004 Annual Report

Table of Contents Overview Mission, Vision, and Strategic Plan Enrollment and Expenditure Data Faculty & Staff Industrial Advisory Committee Students Academic Programs Alumni Donations Contracts & Grants Graduates Publicationshttps://digitalcommons.mtu.edu/mechanical-annualreports/1014/thumbnail.jp