Numerical study of keyhole dynamics and keyhole-induced porosity formation in remote laser welding of Al alloys

2017 ◽  
Vol 108 ◽  
pp. 244-256 ◽  
Author(s):  
Runqi Lin ◽  
Hui-ping Wang ◽  
Fenggui Lu ◽  
Joshua Solomon ◽  
Blair E. Carlson
Keyword(s):  
Laser Welding ◽  
Numerical Study ◽  
Al Alloys ◽  
Porosity Formation ◽  
Keyhole Dynamics ◽  
Remote Laser Welding

scholarly journals Effect of Micro Solidification Crack on Mechanical Performance of Remote Laser Welded AA6063 Fillet Lap Joint in Automotive Battery Tray Construction

2021 ◽  
Vol 11 (10) ◽  
pp. 4522
Author(s):  
Tianzhu Sun ◽  
Pasquale Franciosa ◽  
Conghui Liu ◽  
Fabio Pierro ◽  
Darek Ceglarek
Keyword(s):  
Laser Welding ◽  
Fusion Zone ◽  
Aluminium Alloys ◽  
Mechanical Performance ◽  
Lap Joints ◽  
Fatigue Durability ◽  
Micro Cracks ◽  
Remote Laser Welding ◽  
Weld Fatigue ◽  
Weld Root

Remote laser welding (RLW) has shown a number of benefits of joining 6xxx aluminium alloys such as high processing speed and process flexibility. However, the crack susceptibility of 6xxx aluminium alloys during RLW process is still an open problem. This paper experimentally assesses the impact of transverse micro cracks on joint strength and fatigue durability in remote laser welding of AA6063-T6 fillet lap joints. Distribution and morphology of transverse micro cracks were acquired by scanning electron microscope (SEM) on cross-sections. Grain morphology in the weld zone was determined by electron backscatter diffraction (EBSD) while static tensile and dynamic fatigue tests were carried out to evaluate weld mechanical performance. Results revealed that increasing welding speed from 2 m/min to 6 m/min did not introduce additional transverse micro cracks. Additionally, welding at 2 m/min resulted in tensile strength improvement by 30% compared to 6 m/min due to the expansion of fusion zone, measured by the throat thickness, and refinement of columnar grains near fusion lines. Furthermore, the weld fatigue durability is significantly higher when fracture occurs in weld root instead of fusion zone. This can be achieved by increasing weld root angle with optimum weld fatigue durability at around 55°.

scholarly journals Remote Laser Welding of Steel and Aluminum Alloys

2017 ◽  
Vol 14 (1) ◽  
pp. 32-35 ◽  
Author(s):  
Florian Albert ◽  
Philip Marben ◽  
Tom Graham
Keyword(s):  
Aluminum Alloys ◽  
Laser Welding ◽  
Remote Laser Welding

scholarly journals Remote-laser welding system with in-line adaptive 3D seam tracking and power control

Procedia CIRP ◽  
2019 ◽  
Vol 81 ◽  
pp. 1189-1194 ◽  
Author(s):  
Matjaž Kos ◽  
Erih Arko ◽  
Hubert Kosler ◽  
Matija Jezeršek
Keyword(s):  
Laser Welding ◽  
Power Control ◽  
Seam Tracking ◽  
Remote Laser Welding ◽  
Welding System

Application of Laser Remote Welding to an Aluminum Automotive Part

2010 ◽  
Vol 654-656 ◽  
pp. 966-969
Author(s):  
Cheol Hee Kim ◽  
Won Ho Choi ◽  
Ki Young Park
Keyword(s):  
Laser Welding ◽  
Automotive Industry ◽  
Spot Welding ◽  
Thin Disk ◽  
Al Alloys ◽  
Thermal Distortion ◽  
Focal Distance ◽  
High Brightness ◽  
Automotive Part ◽  
Remote Welding

Aluminum alloys are extensively used in the automotive industry in response to increasing requirements for weight reduction in car body architecture. Laser welding of Al alloys has many advantages such as low heat input, narrow heat affected zone, minimal thermal distortion and flexibility. Recently, the development of high brightness lasers - thin disk lasers and fiber lasers enable long focal distance welding so that laser remote welding with scanning mirrors can be used in laser welding of Al alloys. In this study, laser remote welding was implemented to a heat shield part of an automobile by utilizing a 4kW disk laser and a 2-axis scanner. By replacing conventional resistance spot welding, the weld flange length could be reduced from 15mm to 8mm and a cycle time for each welding point could also be reduced from 3.5s to 0.6s.

scholarly journals Deep Learning Approach of Energy Estimation Model of Remote Laser Welding

Energies ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 1799 ◽  
Author(s):  
Jumyung Um ◽  
Ian Anthony Stroud ◽  
Yong-keun Park
Keyword(s):  
Deep Learning ◽  
Laser Welding ◽  
Process Parameters ◽  
Energy Use ◽  
Technical Specification ◽  
Average Energy ◽  
Welding Process ◽  
Learning Approach ◽  
Estimation Model ◽  
Remote Laser Welding

Due to concerns about energy use in production systems, energy-efficient processes have received much interest from the automotive industry recently. Remote laser welding is an innovative assembly process, but has a critical issue with the energy consumption. Robot companies provide only the average energy use in the technical specification, but process parameters such as robot movement, laser use, and welding path also affect the energy use. Existing literature focuses on measuring energy in standardized conditions in which the welding process is most frequently operated or on modularizing unified blocks in which energy can be estimated using simple calculations. In this paper, the authors propose an integrated approach considering both process variation and machine specification and multiple methods’ comparison. A deep learning approach is used for building the neural network integrated with the effects of process parameters and machine specification. The training dataset used is experimental data measured from a remote laser welding robot producing a car back door assembly. The proposed estimation model is compared with a linear regression approach and shows higher accuracy than other methods.

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