Modelling of Thermal Quantities for Quick Process Assessment and Process Capability Space Refinement at an Early Design Phase During Laser Welding

2021 ◽  
Author(s):  
Anand Mohan ◽  
Dariusz Ceglarek ◽  
Michael Auinger
Keyword(s):  
Heat Transfer ◽  
Laser Welding ◽  
Cooling Rate ◽  
Process Parameters ◽  
Thermal Cycle ◽  
Welding Process ◽  
Process Capability ◽  
Peak Temperature ◽  
Transfer Model ◽  
Process Assessment

Abstract This research aims at understanding the impact of welding process parameters and beam oscillation on the weld thermal cycle during laser welding. A three-dimensional heat transfer model is developed to simulate the welding process, based on the finite element (FE) method. The calculated thermal cycle and weld morphology are in good agreement with experimental results from literature. By utilizing the developed heat transfer model, the effect of welding process parameters such as heat source power, welding speed, radius of oscillation, and frequency of oscillation on the intermediate performance indicators (IPIs) such as peak temperature, heat-affected zone volume (HAZ), and cooling rate is quantified. Parametric contour maps for peak temperature, HAZ volume, and cooling rate are developed for the estimation of the process capability space. An integrated approach for rapid process assessment, process capability space refinement, based on IPIs is proposed. The process capability space will guide the identification of the initial welding process parameters window and help in reducing the number of experiments required by refining the feasible region of process parameters based on the interactions with the IPIs. Here, the peak temperature indicates the mode of welding performed while the HAZ volume and cooling rate are weld quality indicators. The regression relationship between the welding process parameters and the IPIs is established for quick estimation of IPIs to replace time-consuming numerical simulations. The proposed approach provides a unique ability to simulate the laser welding process and provides a robust range of process parameters.

Numerical Studies on Heat Transfer and Fluid Flow during Laser Welding of Thin Sheet

2014 ◽  
Vol 592-594 ◽  
pp. 571-578
Author(s):  
K.R. Balasubramanian ◽  
T. Suthakar ◽  
K. Sankaranarayanasamy ◽  
G. Buvanashekaran
Keyword(s):  
Heat Transfer ◽  
Fluid Flow ◽  
Laser Welding ◽  
Cooling Rate ◽  
Process Parameters ◽  
Weld Bead ◽  
High Heating Rate ◽  
Bead Geometry ◽  
Thermal Cycles ◽  
Influence Of Process Parameters

Numerical simulation of heat transfer and fluid flow analysis of laser welding is essential to understand the physics of fluid motion, thermal cycles, heating and cooling rate and its effect on the formation of the final weld bead profile. The fusion geometry, weld thermal cycles, temperature and velocity field will vary depending on the welding process parameters. The influence of process parameters on the formation of weld bead geometry was analyzed in this study. In the simulation a plane Gaussian profile heat source was used to model the laser beam considering the equations of mass, momentum and energy. It was observed that due to the difference in surface tension coefficient the fluid moves from the central region of the molten pool to the outside. Increase in beam power or decrease in welding speed resulted in a high heating rate and less cooling rate due to high heat input. The simulated bead profiles were compared with the experimentally measured profile and was found to be in agreement.

Investigation of Fluid Flow and Heat Transfer in 3D Dual-Beam Laser Keyhole Welding

2010 ◽  
Author(s):  
Jun Zhou ◽  
Hai-Lung Tsai
Keyword(s):  
Heat Transfer ◽  
Laser Welding ◽  
Welding Process ◽  
Solidification Process ◽  
Melt Flow ◽  
Single Beam ◽  
Keyhole Welding ◽  
Beam Laser ◽  
Dual Beam ◽  
Laser Keyhole Welding

Dual-beam laser welding has become an emerging joining technique. Studies have demonstrated that it can provide benefits over conventional single-beam laser welding, such as increasing keyhole stability, slowing down cooling rate and delaying the humping onset to a higher welding speed. It is also reported to be able to improve weld quality significantly. However, due to its complexity the development of this promising technique has been limited to the trial-and-error procedure. In this study, mathematical models are developed to investigate the heat transfer, melt flow, and solidification process in three-dimensional dual-beam laser keyhole welding. Effects of key parameters, such as laser-beam configuration on melt flow, weld shape, and keyhole dynamics are studied. Some experimentally observed phenomena, such as the changes of the weld pool shape from oval to circle and from circle to oval during the welding process are analyzed in current study.

Effect of laser welding process parameters on dissimilar joints of AISI 316 and nickel 201

2020 ◽  
Vol 22 ◽  
pp. 2964-2973 ◽  
Author(s):  
G. Krishna Kumar ◽  
C. Velmurugan ◽  
R.S. Jayaram ◽  
M. Manikandan
Keyword(s):  
Laser Welding ◽  
Process Parameters ◽  
Welding Process ◽  
Dissimilar Joints ◽  
Aisi 316 ◽  
Laser Welding Process ◽  
Nickel 201

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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