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Research Papers

A Semiautomatic, Cleaning Room Grinding Method for the Metalcasting Industry

[+] Author and Article Information
Danni Wang

Intuitive Surgical,
Sunnyvale, CA 94086

Frank E. Peters

Department of Industrial and
Manufacturing Systems Engineering,
Iowa State University,
Ames, IA 50011
e-mail: fpeters@iastate.edu

Matthew C. Frank

Department of Industrial and
Manufacturing Systems Engineering,
Iowa State University,
Ames, IA 50011
e-mail: mfrank@iastate.edu

1Corresponding author.

Manuscript received March 31, 2017; final manuscript received September 10, 2017; published online November 2, 2017. Assoc. Editor: Mark Jackson.

J. Manuf. Sci. Eng 139(12), 121017 (Nov 02, 2017) (8 pages) Paper No: MANU-17-1210; doi: 10.1115/1.4037890 History: Received March 31, 2017; Revised September 10, 2017

This paper presents a semi-automated grinding system for the postprocessing of metalcastings. Grinding is an important procedure in the “cleaning room” of a foundry, where the removal of gate contacts, parting line flash, surface defects, and weld-repaired areas is performed, and almost always manually. While the grinding of repetitive locations on medium to high production castings can be automated using robotics or otherwise, it is not as practical for larger castings (e.g., > 200 kg) that are typically produced in smaller production volumes. Furthermore, automation is even more challenging in that the locations of the required grinding are not a constant depending on the unique conditions and anomalies of each pouring of a component. The proposed approach is intended for a simple x−y−z positioner (gantry) device with a feedback controlled grinding head that enables automated path planning. The process begins with touch probing of the surfaces that contain the anomaly requiring grinding, and then the system automatically handles the path planning and force control to remove the anomaly. A layer-based algorithm for path planning employs a search-and-destroy technique where the surrounding geometry is interpolated across the grind-requiring surface patch. In this manner, each unique condition of the casting surface after initial torch or saw cutting can be handled cost effectively without the need for human shaping and the egregious ergonomic problems associated. Implementation of the proposed grinding control is prototyped at a lab scale to demonstrate the feasibility and versatility of this strategy. The average error for the prototype was on the order of 0.007 in (0.2 mm) with a flatness of the ground surface within 0.035 in (0.9 mm), which is within the cleaning room grinding requirements, as per ISO and ASTM dimensional and surface tolerance requirements. A significant contribution of the work is the layer-based algorithm that allows an effective automation of the process planning for grinding, avoiding robot programming or numerical control code generation altogether. This is a key to addressing the largely unknown and unpredictable conditions of, for example, the riser contact surface removal area on a metalcasting.

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References

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Figures

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Fig. 1

Example of cleaning room grinding anomaly

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Fig. 2

Process planning procedure

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Fig. 3

Machine coordinate system versus grinding wheel WCS. The expectation is that the gantry would span at least 1 × 1 meters, but could be scaled up to accommodate larger castings.

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Fig. 4

Boundary and reference points, for identifying the containment boundary and approximated underlying surface geometry

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Fig. 5

Reference points used to generate interpolated underlying surface, and offset layers used as the path planning motion planes/surfaces

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Fig. 6

Sequence of automated grinding path planning: (a) Grinder attempts path along layer 1, (b) detecting deflection at anomaly, wheel is raised to progressively higher layers to reach peak, (c) grinding proceeds back and forth down through the layers, (d) upon completion at layer 1, wheel seeks another peak, and (e) grinding continues until all anomalies are reduced through layer one to approximated surface

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Fig. 7

Staircase tool paths: (a) top view of layers in an anomaly, (b) staircase toolpath across anomaly boundary, and (c) toolpath executed based on force feedback on peaks of layer 3

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Fig. 8

Real-time path planning flow chart

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Fig. 9

Position and force hybrid control in real time control

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Fig. 10

Lab prototype for testing path planning strategy

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Fig. 11

Example anomalies automatically ground in the lab, illustrating CAD models of part with and without anomaly, physical models before and after grinding, and results of surface analysis after laser scanning, for (a) a planar surface, (b) convex, and (c) concave surface

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