Generating resource based flexible form manufacturing features through objective driven clustering
Introduction
Manufacturability evaluation (hereafter referred to as ME) is envisioned here as a tool that can provide a designer with a means of assessing a design in order to detect potential manufacturing problems. Such a tool would aid in preventing production related problems by encouraging iteration during the design phase. An automated, easy to use, real-time ME system that could assess a part and make reliable quantitative estimates on characteristics such as cost and production time would enable informed decisions to be made during the design process resulting in designs that strike an ideal balance between functional requirements and producibility.
Section snippets
Background
The various methodologies that were developed for analyzing a product model to ascertain manufacturability can be classified as either qualitative or quantitative. Qualitative approaches to ME have generally involved evaluating a part to determine its conformance with a set of design for manufacturing (DFM) rules [3], [11]. Shah and Mäntylä noted that qualitative based approaches only allow simple judgements to be made about a part design such as good, bad or marginal [12]. Quantitative ME of a
Features: a new approach
A resource based flexible form manufacturing feature comprises portions of a part that can be machined by a single combination of a factory resident machine, a tool, and a setup. This definition is distinct from manufacturing feature definitions used in the past in that it eschews machining volumes, feature taxonomies, and predefined feature templates. A resource-based flexible form manufacturing feature (hereafter called a manufacturing feature) is a function of the part (or some portion of
Feature primitives
The key to making the FPT a useful mechanism for generating “good” feature set descriptions (and therefore operation plans) of a part is to populate the FPT with “good” operation subplans, i.e. primitives. This is accomplished on a face-by-face basis by selecting from the database of available machines, identifying and selecting from a finite set of good setup orientations, and selecting tools that have accessibility. A formal description of this task is in order.
Let fi represent face i in the
Cutting parameters and toolpath generation
Using the product model, factory resource databases and the FPT, both pieces of information can be computed for each primitive and superprimitive. These computations were detailed by Perez in Ref. [8]. The first task is the definition of cutting parameters for each primitive. Historical cutting data is used as well as user selected parameters when automated determination is not desired. In the absence of cutting parameter data, the feed rate and depth of cut is obtained from suggested values
Clustering
Clustering is a decision-making process that conceptually involves transforming an initially empty feature based description of a part into a complete high-level operation plan. Pragmatically, clustering is the process of selecting primitives from the FPT and clustering them into manufacturing features to form a feature set description of a part.
Heuristics and constraints form an integral part of the solution method proposed here. Heuristics are necessary because the number of possible
Prototype system
In this work the purpose of validation testing was to objectively assess the soundness of the proposed methodology. The approach involved implementing major portions of the methodology in a prototype system and then testing the system’s performance.
The Arizona State University Manufacturability Evaluator (ASUME) was the first prototype embodiment of the methodology developed in this research. ASUME was written in C++ on Silicon Graphics workstations (Onyx Reality Engine and Indigo Extreme [2])
Summary
The results from this research effort are significant because they demonstrate a methodology that moves beyond previous research efforts in ME in a number of areas. First, parts that include complex surfaces and interacting form features are easily handled because the definition of a manufacturing feature developed here permits a flexible form. Second, usable high-level operation plans lead directly from feature set descriptions of a part because the feature definition incorporates available
Dr. Roger Stage Dr. Roger Stage is currently a Senior Quality Engineer at Intel Corporation in Chandler, Arizona. He received his PhD in Mechanical Engineering from Arizona State University in 1996, MS from Ohio University in 1991, and BS from the University of Nebraska in 1983. His background is in MCAD with a focus on features based manufacturability evaluation.
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Dr. Roger Stage Dr. Roger Stage is currently a Senior Quality Engineer at Intel Corporation in Chandler, Arizona. He received his PhD in Mechanical Engineering from Arizona State University in 1996, MS from Ohio University in 1991, and BS from the University of Nebraska in 1983. His background is in MCAD with a focus on features based manufacturability evaluation.
Chell Roberts is a professor of Industrial and Management Systems Engineering and Co-director of the Integrated Manufacturing Engineering Laboratory. He received his PhD in industrial engineering from Virginia Tech in 1991. He currently teaches Computer Aided Manufacturing courses. His research interests are in manufacturing automation, CAD/CAM integration and discrete event control.
Mark Henderson is professor of Industrial and Management Systems Engineering, Director of the NSF-funded Manufacturing Across the Curriculum project and Co-Director of the 5-college Partnership for Research in Stereographic Modeling (PRISM) at Arizona State University in Tempe, AZ. He recieved his PhD in mechanical engineering from Purdue University in 1984. Currently, he teaches Solid Modeling as well as Computer-Aided Manufacturing, Integrated Product and Process Design and Rapid Fabrication. Current research interests are in IPPD methods, geometric modeling, CAD/CAM integration, computer graphics and rapid prototyping.