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Research Papers

Automated Robot Tool Trajectory Connection for Spray Forming Process

[+] Author and Article Information
Heping Chen1

Ingram School of Engineering,  Texas State University, 601 University Drive, San Marcos, TX 78666hc15@txstate.edu

Ning Xi

Department of Electrical and Computer Engineering,  Michigan State University, East Lansing, MI 48824

1

Corresponding author.

J. Manuf. Sci. Eng 134(2), 021017 (Apr 06, 2012) (9 pages) doi:10.1115/1.4005798 History: Received November 05, 2010; Revised September 24, 2011; Accepted October 05, 2011; Published April 06, 2012; Online April 06, 2012

Automated robot tool trajectory planning for spray forming is highly desirable for today’s automotive manufacturing. Generating a robot tool trajectory to manufacture an automotive part to satisfy material distribution requirements is still very challenging due to the complexity of the problems. An industrial part may need to be partitioned into multiple patches because of its complexity. The trajectories of all patches must be connected to form a complete trajectory for the industrial part in order to minimize the material waste and process cycle time. In this paper, the methodology for automated robot tool trajectory connection is developed. Experimental tests were carried out to generate trajectories for automotive parts and the results validate the developed approach. A user-friendly software packages were developed and used at the Ford Motor Company to generate robot tool trajectories to manufacture automotive parts. The developed algorithm can also be extended to other applications in surface manufacturing such as spray painting.

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Copyright © 2012 by American Society of Mechanical Engineers
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Figures

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Figure 1

(a) The spray forming robotic system. (b) A chopper gun used to spray glass fiber on a mold. (Courtesy of Dr. Yifan Chen and Mr. Jeffery Dahl, the Ford Motor Company)

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Figure 2

The automated CAD-guided optimal tool trajectory planning system

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Figure 3

The triangular approximation of part of a car hood

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Figure 4

(a) A tool model. (b) A typical tool profile.

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Figure 5

The automated optimal tool trajectory planning process

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Figure 6

The possible starting and ending points of a trajectory for a patch

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Figure 7

A bounding box of a patch and its corner points

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Figure 8

The different patterns of the trajectories of a patch. The starting and ending points are (a) P1 and P3 ; (b) P1 and P4 or P1 and P2 ; (c) P2 and P4 ; (d) P3 and P4 or P2 and P3 .

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Figure 9

Path connection. The vertices belong to different groups. Two vertices in each group are selected to connect the path. The weight of the connection between two groups with vertices i and j is dij . The weight between two vertices m and n in a group is tmn .

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Figure 10

Two parts are used to test the developed trajectory connection algorithm: (a) the radiator upper panel (front view); (b) the radiator upper panel (side view); (c) the hood inner (front view); (d) the hood inner (side view). Form the front view and the side view, we can see the curvature of the parts

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Figure 11

The parts are partitioned into patches: (a) the radiator upper panel; (b) the hood inner

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Figure 12

The tool path connection based on the bounding box. (a) A shortest path to connect all patches; (b) two points are found in each patch to connect the tool trajectory.

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Figure 13

The connected robot path: (a) the radiator upper panel; (b) the hood inner

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Figure 14

The connected robot path for the radiator upper panel with more turns

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