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.

References

References
1.
Dubey
,
A. K.
, and
Yadava
,
V.
,
2008
, “
Laser Beam Machining—A Review
,”
Int. J. Mach. Tools Manuf.
,
48
(
6
), pp.
609
628
.
2.
Lausten
,
R.
, and
Balling
,
P.
,
2001
On-the-Fly Depth Profiling During Ablation With Ultrashort Laser Pulses: A Tool for Accurate Micromachining and Laser Surgery
,”
Appl. Phys. Lett.
,
79
(
6
), p.
884
.
3.
Jaeggi
,
B.
,
Neuenschwander
,
B.
,
Meier
,
T.
,
Zimmermann
,
M.
, and
Hennig
,
G.
,
2013
, “
High Precision Surface Structuring With Ultra-Short Laser Pulses and Synchronized Mechanical Axes
,”
Phys. Procedia
,
41
, pp.
319
326
.
4.
Walton
,
J. F.
,
Cronin
,
M.
, and
Mehta
,
R.
,
1991
, “
Advanced Balancing Using Laser Machining
,”
SAE, Aerospace Technology Conference Exposition
, SAE paper No. 912218.
5.
Cheng
,
X.
,
Huang
,
Y.
,
Zhou
,
S.
,
Liu
,
J.
, and
Yang
,
X.
,
2011
, “
Study on the Generative Design Method and Error Budget of a Novel Desktop Multi-Axis Laser Machine for Micro Tool Fabrications
,”
Int. J. Adv. Manuf. Technol.
,
60
(
5–8
), pp.
545
552
.
6.
Poleshchuk
,
A. G.
,
Korolkov
,
V. P.
,
Cherkashin
,
V. V.
,
Reichelt
,
S.
, and
Burge
,
J. H.
,
2001
, “
Polar-Coordinate Laser Writing Systems: Error Analysis of Fabricated DOEs
,”
International Symposium on Optical Science and Technology
, pp.
161
172
.
7.
Feng
,
H.-Y.
,
Liu
,
Y.
, and
Xi
,
F.
,
2001
, “
Analysis of Digitizing Errors of a Laser Scanning System
,”
Precis. Eng.
,
25
(
3
), pp.
185
191
.
8.
Cuartero
,
A.
,
Armesto
,
J.
,
Rodríguez
,
P. G.
, and
Arias
,
P.
,
2010
, “
Error Analysis of Terrestrial Laser Scanning Data by Means of Spherical Statistics and 3D Graphs
,”
Sensors (Basel).
,
10
(
11
), pp.
10128
10145
.
9.
George
,
D. S.
,
Onischenko
,
A.
, and
Holmes
,
A. S.
,
2004
, “
On the Angular Dependence of Focused Laser Ablation by Nanosecond Pulses in Solgel and Polymer Materials
,”
Appl. Phys. Lett.
,
84
(
10
), p.
1680
.
10.
Erkorkmaz
,
K.
,
Alzaydi
,
A.
,
Elfizy
,
A.
, and
Engin
,
S.
,
2014
, “
Time-Optimized Hole Sequence Planning for 5-Axis on-the-Fly Laser Drilling
,”
CIRP Ann. Manuf. Technol.
,
63
(
1
), pp.
377
380
.
11.
Sathiya
,
P.
,
Panneerselvam
,
K.
, and
Abdul Jaleel
,
M. Y.
,
2012
, “
Optimization of Laser Welding Process Parameters for Super Austenitic Stainless Steel Using Artificial Neural Networks and Genetic Algorithm
,”
Mater. Des.
,
36
, pp.
490
498
.
12.
Venkata Rao
,
R.
, and
Kalyankar
,
V. D.
,
2013
, “
Parameter Optimization of Modern Machining Processes Using Teaching–Learning-Based Optimization Algorithm
,”
Eng. Appl. Artif. Intell.
,
26
(
1
), pp.
524
531
.
13.
Adelmann
,
B.
, and
Hellmann
,
R.
,
2011
, “
Fast Laser Cutting Optimization Algorithm
,”
Phys. Procedia
,
12
(
Part A
), pp.
591
598
.
14.
Rao
,
R.
, and
Yadava
,
V.
,
2009
, “
Multi-Objective Optimization of Nd:YAG Laser Cutting of Thin Superalloy Sheet Using Grey Relational Analysis With Entropy Measurement
,”
Opt. Laser Technol.
,
41
(
8
), pp.
922
930
.
15.
Dhupal
,
D.
,
Doloi
,
B.
, and
Bhattacharyya
,
B.
,
2007
, “
Optimization of Process Parameters of Nd:YAG Laser Microgrooving of Al2TiO5 Ceramic Material by Response Surface Methodology and Artificial Neural Network Algorithm
,”
Proc. Inst. Mech. Eng. Part B
,
221
(
8
), pp.
1341
1350
.
16.
Dubey
,
A. K.
, and
Yadava
,
V.
,
2008
, “
Experimental Study of Nd:YAG Laser Beam Machining—An Overview
,”
J. Mater. Process. Technol.
,
195
(
1–3
), pp.
15
26
.
17.
Nakhjavani
,
O. B.
, and
Ghoreishi
,
M.
,
2007
, “
Multi Criteria Optimization of Laser Percussion Drilling Process Using Artificial Neural Network Model Combined with Genetic Algorithm
,”
J. Mater. Manuf. Process.
,
21
(
1
), pp.
11
18
.
18.
Mitchell
,
M.
,
1996
,
An Introduction to Genetic Algorithms
,
London, England
:
A Bradford Book The MIT Press
,
Cambridge, MA
.
19.
Deb
,
K.
,
2001
,
Multi-Objective Optimization Using Evolutionary Algorithms
,
Wiley
,
New York
.
20.
Kar
,
A.
, and
Mazumder
,
J.
,
1990
, “
Two-Dimensional Model for Material Damage Due to Melting and Vaporization During Laser Irradiation
,”
J. Appl. Phys.
,
68
(
8
), p.
3884
.
21.
Chang
,
H.-C.
, and
Wang
,
L.-C.
,
2010
, “
A Simple Proof of Thue's Theorem on Circle Packing
,”
Cornell University Library
,
Cornell, NY
, accessed, June 3, 2015 http://arxiv.org/pdf/1009.4322v1.pdf
22.
Gilbert
,
D.
,
Stoesslein
,
M.
,
Axinte
,
D.
,
Butler-Smith
,
P.
, and
Kell
,
J.
,
2014
, “
A Time Based Method for Predicting the Workpiece Surface Micro-Topography Under Pulsed Laser Ablation
,”
J. Mater. Process. Technol.
,
214
(
12
), pp.
3077
3088
.
23.
Kong
,
M. C.
,
Miron
,
C. B.
,
Axinte
,
D. A.
,
Davies
,
S.
, and
Kell
,
J.
,
2012
, “
On the Relationship Between the Dynamics of the Power Density and Workpiece Surface Texture in Pulsed Laser Ablation
,”
CIRP Ann. Manuf. Technol.
,
61
(
1
), pp.
203
206
.
24.
Billingham
,
J.
,
Miron
,
C. B.
,
Axinte
,
D. A.
, and
Kong
,
M. C.
,
2013
, “
Mathematical Modelling of Abrasive Waterjet Footprints for Arbitrarily Moving Jets: Part II—Overlapped Single and Multiple Straight Paths
,”
Int. J. Mach. Tools Manuf.
,
68
, pp.
30
39
.
25.
Sharma
,
A.
, and
Yadava
,
V.
,
2012
, “
Modelling and Optimization of Cut Quality During Pulsed Nd:YAG Laser Cutting of Thin Al-Alloy Sheet for Straight Profile
,”
Opt. Laser Technol.
,
44
(
1
), pp.
159
168
.
26.
Dorofki
,
M.
,
Elshafie
,
A.
, and
Jaafar
,
O.
,
2012
, “
Comparison of Artificial Neural Network Transfer Functions Abilities to Simulate Extreme Runoff Data
,”
International Proceedings of Chemical, Biological and Environment
, Vol.
33
, p.
39
.
27.
Yu
,
H.
, and
Wilamowski
,
B.
,
2011
, “
Levenberg-Marquardt Training
,” Industrial Electronics Handbook – Intelligent Systems, Vol. 5,
CRC Press
,
Boca Raton, FL
, pp.
12-1
12-15
.
28.
Hughes
,
I. G.
, and
Hase
,
T. P. A.
,
2010
,
Measurements and Their Uncertainties
,
Oxford University Press
,
Oxford, UK
.
29.
MathWorks
,
2015
, “
How the Genetic Algorithm Works
,”
Matlab Documentation
,
Natick, MA
, accessed, Nov. 15, 2015, http://uk.mathworks.com/help/gads/how-the-genetic-algorithm-works.html
30.
ISO
,
2012
, “
Geometrical Product Specifications (GPS)—Surface Texture: Areal—Part 2: Terms, Definitions and Surface Texture Parameters
,”
International Organization for Standardization, Geneva
,
Switzerland
, Standard No. ISO 25178-2:2012.
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