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Abstract

Sustainability, as well as high-quality outcomes, pose significant challenges within the context of current manufacturing cycles, in alignment with European strategies aimed at decarbonization. This framework encourages a systematic evaluation of manufacturing processes in terms of their performance and carbon footprint. One sector where this is particularly relevant is the production of batteries for electric mobility, thanks to its exponential growth. Out of all the processes involved, laser welding stands out as being a critical step since it offers potential energy savings through optimization. With the dual goals of achieving mechanical strength and environmental sustainability, this study investigates alternative solutions for laser welding of aluminum sheets. Different laser welding configurations are tested to evaluate the effect of process setups on weld quality and carbon emissions across different productivity scenarios. The key findings can be summarized as follows: (1) the selection of welding setup significantly influences both quality and sustainability requirements; (2) the optimal conditions for meeting strength requirements may diverge from those aimed at minimizing environmental impact; (3) the choice of the final solution is influenced by the specific industrial scenario. The study specifically demonstrated that aluminum alloys can be welded with higher quality (porosity below 1% and equivalent ultimate strength up to 204 MPa) when filler wire is introduced alongside an active wobbling scanning strategy. Conversely, filler wire can be omitted in scenarios prioritizing high-productivity and low-carbon emissions, such as when employing a linear scanning strategy, resulting in a reduction of equivalent carbon emissions by up to 140%.

References

1.
IEA
,
n.d.
, “Global CO2 Emissions From Energy Combustion and Industrial Processes, 1900–2022,” IEA, Paris. https://www.iea.org/data-and-statistics/charts/global-co2-emissions-from-energy-combustion-and-industrial-processes-1900-2022.
3.
Chen
,
Q.
,
Lai
,
X.
,
Gu
,
H.
,
Tang
,
X.
,
Gao
,
F.
,
Han
,
X.
, and
Zheng
,
Y.
,
2022
, “
Investigating Carbon Footprint and Carbon Reduction Potential Using a Cradle-to-Cradle LCA Approach on Lithium-Ion Batteries for Electric Vehicles in China
,”
J. Clean. Prod.
,
369
, p.
133342
.
4.
Goffin
,
N.
,
Jones
,
L. C. R.
,
Tyrer
,
J.
,
Ouyang
,
J.
,
Mativenga
,
P.
, and
Woolley
,
E.
,
2021
, “
Mathematical Modelling for Energy Efficiency Improvement in Laser Welding
,”
J. Clean. Prod.
,
322
, p.
129012
.
5.
Capello
,
E.
,
2008
, “Le lavorazioni industriali mediante laser di potenza.”
6.
Cao
,
X.
,
Jahazi
,
M.
,
Immarigeon
,
J. P.
, and
Wallace
,
W.
,
2006
, “
A Review of Laser Welding Techniques for Magnesium Alloys
,”
J. Mater. Process. Technol.
,
171
(
2
), pp.
188
204
.
7.
Liverani
,
E.
,
Ascari
,
A.
, and
Fortunato
,
A.
,
2023
, “
The Role of Filler Wire and Scanning Strategy in Laser Welding of Difficult-to-Weld Aluminum Alloys
,”
Int. J. Adv. Manuf. Technol.
,
128
(
1–2
), pp.
763
777
.
8.
Saariluoma
,
H.
,
Piiroinen
,
A.
,
Unt
,
A.
,
Hakanen
,
J.
,
Rautava
,
T.
, and
Salminen
,
A.
,
2020
, “
Overview of Optical Digital Measuring Challenges and Technologies in Laser Welded Components in EV Battery Module Design and Manufacturing
,”
Batteries
,
6
(
3
), p.
47
.
9.
ISO
,
2006
, “14044:2006-Environmental Management – Life Cycle Assessment – Requirements and Guidelines.”
10.
Yilbas
,
B. S.
,
Shaukat
,
M. M.
,
Afzal
,
A. A.
, and
Ashraf
,
F.
,
2020
, “
Life Cycle Analysis for Laser Welding of Alloys
,”
Opt. Laser Technol.
,
126
, p.
106064
.
11.
Sproesser
,
G.
,
Chang
,
Y. J.
,
Pittner
,
A.
,
Finkbeiner
,
M.
, and
Rethmeier
,
M.
,
2015
, “
Life Cycle Assessment of Welding Technologies for Thick Metal Plate Welds
,”
J. Clean. Prod.
,
108
, pp.
46
53
.
12.
Sangwan
,
K. S.
,
Herrmann
,
C.
,
Egede
,
P.
,
Bhakar
,
V.
, and
Singer
,
J.
,
2016
, “
Life Cycle Assessment of Arc Welding and Gas Welding Processes
,”
Proc. CIRP
,
48
, pp.
62
67
.
13.
Xydea
,
E.
,
Panagiotopoulou
,
V. C.
, and
Stavropoulos
,
P.
,
2024
, “
A Strategy Framework for Identifying Carbon Intensive Elements in Welding Processes
,”
Proc. CIRP
,
121
, pp.
103
108
.
14.
Wei
,
H.
,
Zhang
,
Y.
,
Tan
,
L.
, and
Zhong
,
Z.
,
2015
, “
Energy Efficiency Evaluation of Hot-Wire Laser Welding Based on Process Characteristic and Power Consumption
,”
J. Clean. Prod.
,
87
, pp.
255
262
.
15.
Feng
,
S.
,
Senthilkumaran
,
K.
,
Brown
,
C.
, and
Kulvatunyou
,
B.
,
2014
, “
Energy Metrics for Product Assembly Equipment and Processes
,”
J. Clean. Prod.
,
65
, pp.
142
151
.
16.
Ge
,
W.
,
Li
,
H.
,
Cao
,
H.
,
Li
,
C.
,
Wen
,
X.
,
Zhang
,
C.
, and
Mativenga
,
P.
,
2023
, “
Welding Parameters and Sequences Integrated Decision-Making Considering Carbon Emission and Processing Time for Multi-characteristic Laser Welding Cell
,”
J. Manuf. Syst.
,
70
, pp.
1
17
.
17.
Ge
,
W.
,
Cao
,
H.
,
Li
,
H.
,
Zhang
,
C.
,
Li
,
C.
, and
Wen
,
X.
,
2022
, “
Multi-Feature Driven Carbon Emission Time Series Coupling Model for Laser Welding System
,”
J. Manuf. Syst.
,
65
, pp.
767
784
.
18.
Wu
,
J.
,
Zhang
,
C.
,
Giam
,
A.
,
Chia
,
H. Y.
,
Cao
,
H.
,
Ge
,
W.
, and
Yan
,
W.
,
2024
, “
Physics-Assisted Transfer Learning Metamodels to Predict Bead Geometry and Carbon Emission in Laser Butt Welding
,”
Appl. Energy
,
359
, p.
122682
.
19.
Chen
,
C.
,
Liu
,
Y.
,
Kumar
,
M.
,
Qin
,
J.
, and
Ren
,
Y.
,
2019
, “
Energy Consumption Modelling Using Deep Learning Embedded Semi-Supervised Learning
,”
Comput. Ind. Eng.
,
135
, pp.
757
765
.
20.
Li
,
J.
,
Cao
,
L.
,
Hu
,
J.
,
Sheng
,
M.
,
Zhou
,
Q.
, and
Jin
,
P.
,
2022
, “
A Prediction Approach of SLM Based on the Ensemble of Metamodels Considering Material Efficiency, Energy Consumption, and Tensile Strength
,”
J. Intell. Manuf.
,
33
(
3
), pp.
687
702
.
21.
Wu
,
J.
,
Zhang
,
C.
,
Lian
,
K.
,
Cao
,
H.
, and
Li
,
C.
,
2022
, “
Carbon Emission Modeling and Mechanical Properties of Laser, Arc and Laser–Arc Hybrid Welded Aluminum Alloy Joints
,”
J. Clean. Prod.
,
378
, p.
134437
.
22.
Yan
,
W.
,
Zhang
,
H.
,
Jiang
,
Z.
, and
Hon
,
K. K. B.
,
2017
, “
Multi-Objective Optimization of Arc Welding Parameters: The Trade-Offs Between Energy and Thermal Efficiency
,”
J. Clean. Prod.
,
140
, pp.
1842
1849
.
23.
Peng
,
S.
,
Li
,
T.
,
Zhao
,
J.
,
Lv
,
S.
,
Tan
,
G. Z.
,
Dong
,
M.
, and
Zhang
,
H.
,
2019
, “
Towards Energy and Material Efficient Laser Cladding Process: Modeling and Optimization Using a Hybrid TS-GEP Algorithm and the NSGA-II
,”
J. Clean. Prod.
,
227
, pp.
58
69
.
24.
Zhang
,
C.
,
Zhou
,
Z.
,
Tian
,
G.
,
Xie
,
Y.
,
Lin
,
W.
, and
Huang
,
Z.
,
2018
, “
Energy Consumption Modeling and Prediction of the Milling Process: A Multistage Perspective
,”
Proc. Inst. Mech. Eng. B
,
232
(
11
), pp.
1973
1985
.
25.
Huang
,
Z.
,
Cao
,
H.
,
Zeng
,
D.
,
Ge
,
W.
, and
Duan
,
C.
,
2021
, “
A Carbon Efficiency Approach for Laser Welding Environmental Performance Assessment and the Process Parameters Decision-Making
,”
Int. J. Adv. Manuf. Technol.
,
114
(
7–8
), pp.
2433
2446
.
26.
Sun
,
T.
,
Franciosa
,
P.
,
Sokolov
,
M.
, and
Ceglarek
,
D.
,
2020
, “
Challenges and Opportunities in Laser Welding of 6xxx High Strength Aluminium Extrusions in Automotive Battery Tray Construction
,”
Proc. CIRP
,
94
, pp.
565
570
.
27.
Daintith
,
J.
,
2009
,
A Dictionary of Physics
, 6th ed.,
Oxford University Press
,
Oxford, UK
.
28.
Wang
,
G. G.
, and
Shan
,
S.
,
2007
, “
Review of Metamodeling Techniques in Support of Engineering Design Optimization
,”
ASME J. Mech. Des.
,
129
(
4
), pp.
370
380
.
29.
Bastos
,
J.
,
Lo Vullo
,
E.
,
Muntean
,
M.
,
Duerr
,
M.
,
Kona
,
A.
, and
Bertoldi
,
P.
,
2020
, “GHG Emission Factors for Electricity Consumption,” European Commission, Joint Research Centre (JRC) [Dataset] PID. http://data.europa.eu/89h/919df040-0252-4e4e-ad82-c054896e1641
30.
Janjua
,
R.
, and
Maciel
,
F.
,
2023
, CO2 Data Collection, Review 2022. World Steel Association, https://worldsteel.org/climate-action/climate-action-data-collection/.
31.
International Aluminium Institute (IAI)
,
2023
, “Greenhouse Gas Emissions Intensity-Primary Aluminium, Reference Period: 2022.” https://international-aluminium.org/statistics/greenhouse-gas-emissions-intensity-primary-aluminium
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