Research Papers

Automated Manufacturing Planning Approach Based on Volume Decomposition and Graph-Grammars

[+] Author and Article Information
Matthew I. Campbell

e-mail: mc1@mail.utexas.edu
Advanced Manufacturing Center,
Department of Mechanical Engineering,
University of Texas at Austin,
Austin, TX 78712

(.STEP) is used as the standard format for the exchange and conversion of solid models.

1Corresponding author.

Contributed by the Computers and Information Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received July 12, 2012; final manuscript received February 11, 2013; published online April 26, 2013. Editor: Bahram Ravani.

J. Comput. Inf. Sci. Eng 13(2), 021010 (Apr 26, 2013) (13 pages) Paper No: JCISE-12-1110; doi: 10.1115/1.4023860 History: Received July 12, 2012; Revised February 11, 2013

A new graph grammar based reasoning is proposed to reason about the manufacturability of 3D solid models. The knowledge captured in the graph grammar rules serves as a virtual machinist in its ability to recognize arbitrary geometries and match them to various machining operations. For a given part, its 3D CAD geometry is first decomposed into multiple subvolumes, where each is assumed to be machined in one operation. The decomposed part is then converted into a graph so that the graph-grammar rules can perform further reasoning and determine the machining details. A candidate plan is a feasible sequence of all of the necessary machining operations needed to manufacture this part. For each operation, the rules determine the face on the part that the tool enters, the type of tools used, the type of machine used, and how the part is fixed within the machine. If a given geometry is not machinable, the rules will fail to find a complete manufacturing plan for all of the subvolumes. As a result of this reasoning, designers can quickly get insights into how a part can be made and how it can be improved (e.g., change features to reduce time and cost) based upon the feedback of the rules. A variety of tests on this algorithm on both simple and complex engineering parts show its effectiveness and efficiency.

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Fig. 1

Flow chart of the reasoning scheme

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Fig. 2

Volume decomposition tree

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Fig. 3

The simple solid model shown as part A in Fig. 1 as a seed graph in the reasoning process

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Fig. 4

A sample face representation in the seed lexicon

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Fig. 5

An example of infeasible tool entry face

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Fig. 6

Two drilling rules in graph synth (a) drilling rule 1 for hole of type 1 (b) drilling rule 2 for hole of type 2

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Fig. 7

The VMC rule from rule set #5 in the grammar reasoning

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Fig. 8

Rule sets flow chart

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Fig. 9

A sample manufacturing plan for the solid model shown as part A in Fig. 1

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Fig. 10

Summary report of the search space for the part discussed in Fig. 1

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Fig. 11

The main chassis part

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Fig. 12

A sample manufacturing plan for the chassis part

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Fig. 13

3D model of the radio-box (a) decomposed negative solid (b)

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Fig. 14

One machining sequence for the radio-box

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Fig. 15

A sample manufacturing plan for the radio-box

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Fig. 16

A nonmanufacturable part due to design flaws (a), the machined model in FeatureCAM software (b), sample manufacturing plan generated in AMFA for the part (c)

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Fig. 17

A sample manufacturing plan generated from FeatureCAM

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Fig. 18

The consolidated plan for the part shown in Fig. 1




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