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Research Papers

On-The-Fly Laser Machining: A Case Study for In Situ Balancing of Rotative Parts

[+] Author and Article Information
M. Stoesslein

Rolls-Royce Manufacturing and
On-Wing University Technology Centre,
Faculty of Engineering,
University of Nottingham,
Nottingham NG7 2RD, UK;
Department of Mechanical,
Materials and Manufacturing Engineering,
Coates Building, Room C31,
University Park,
Nottingham NG7 2RD, UK
e-mail: moritz@stoesslein.com

D. Axinte

Rolls-Royce Manufacturing and
On-Wing University Technology Centre,
Faculty of Engineering,
University of Nottingham,
Nottingham NG7 2RD, UK;
Department of Mechanical,
Materials and Manufacturing Engineering,
Coates Building, Room A63,
University Park,
Nottingham, NG7 2RD, UK
e-mail: dragos.axinte@nottingham.ac.uk

D. Gilbert

Rolls-Royce Manufacturing and
On-Wing University Technology Centre,
Faculty of Engineering,
University of Nottingham,
Nottingham NG7 2RD, UK;
Department of Mechanical,
Materials and Manufacturing Engineering,
Coates Building, Room C31,
University Park,
Nottingham NG7 2RD, UK
e-mail: epxdg4@exmail.nottingham.ac.uk

1Corresponding author.

Manuscript received March 4, 2016; final manuscript received August 6, 2016; published online October 3, 2016. Assoc. Editor: Y. B. Guo.

J. Manuf. Sci. Eng 139(3), 031002 (Oct 03, 2016) (14 pages) Paper No: MANU-16-1139; doi: 10.1115/1.4034476 History: Received March 04, 2016; Revised August 06, 2016

On-the-fly laser machining is defined as a process that aims to generate pockets/patches on target components that are rotated or moved at a constant velocity. Since it is a nonintegrated process (i.e., linear/rotary stage system moving the part is independent of that of the laser), it can be deployed to/into large industrial installations to perform in situ machining, i.e., without the need of disassembly. This allows a high degree of flexibility in its applications (e.g., balancing) and can result in significant cost savings for the user (e.g., no dis(assembly) cost). This paper introduces the concept of on-the-fly laser machining encompassing models for generating user-defined ablated features as well as error budgeting to understand the sources of errors on this highly dynamic process. Additionally, the paper presents laser pulse placement strategies aimed at increasing the surface finish of the targeted component by reducing the area surface roughness that are possible for on-the-fly laser machining. The overall concept was validated by balancing a rotor system through ablation of different pocket shapes by the use of a Yb:YAG pulsed fiber laser. In this respect, first, two different laser pulse placement strategies (square and hexagonal) were introduced in this research and have been validated on Inconel 718 target material; thus, it was concluded that hexagonal pulse placement reduces surface roughness by up to 17% compared to the traditional square laser pulse placement. The concept of on-the-fly laser machining has been validated by ablating two different features (4 × 60 mm and 12 × 4 mm) on a rotative target part at constant speed (100 rpm and 86 rpm) with the scope of being balanced. The mass removal of the ablated features to enable online balancing has been achieved within < 4 mg of the predicted value. Additionally, the error modeling revealed that most of the uncertainties in the dimensions of the feature/pocket originate from the stability of the rotor speed, which led to the conclusion that for the same mass of material to be removed it is advisable to ablate features (pockets) with longer circumferential dimensions, i.e., stretched and shallower pockets rather than compact and deep.

FIGURES IN THIS ARTICLE
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Copyright © 2017 by ASME
Topics: Lasers , Machining , Errors
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References

Figures

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Fig. 1

Schematic of on-the-fly pulse laser ablation with main sources of errors

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Fig. 2

Flowchart of the on-the-fly laser machining approach

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Fig. 3

Example of pulse placement grids for a rectangle using hexagonal and square pulse placement

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Fig. 4

Example calibrated relationship between depth of ablation and normalized fluence

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Fig. 5

Example of laser beam energy distribution (top) and crater depth profile (bottom), with the shaded (right and left side) area indicating fluence below the ablation threshold level

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Fig. 6

Schematic of the artificial neural network for the prediction of the ablation depth in on-the-fly laser machining

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Fig. 7

Schematic of the error sources in the circumferential (y) direction

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Fig. 8

Validation performance of the trained neural network showing the point on which minimum error between the predicted and measured ablated depth was achieved

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Fig. 9

Comparison between the surface finishes of (a) one layer hexagonal pulse placement, (b) three layers hexagonal pulse placement, (c) one layer square pulse placement, and (d) three layers square pulse placement

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Fig. 10

View of the testing rig setup

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Fig. 11

Velocity error dependent on rotor speed

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Fig. 12

Rotary stage with Inconel 718 sample attached (see experimental setup in Fig.10)

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Fig. 13

Error contribution of ablated features A and B to ΔDy (see Eq. (18))

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Fig. 14

The error contribution of ablated features A and B to Δm (see Eq. (20))

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