IOts Technology and Deep Learning for Process Parameters with Laser Welding Crack of Alloy

2021 ◽  
pp. 941-946
Author(s):  
Hai-yun Gao ◽  
Run He ◽  
Dong-yun Zhang ◽  
Kun Lu
Keyword(s):  
Deep Learning ◽  
Laser Welding ◽  
Process Parameters

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.

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.

Modeling of Weld Lap-Shear Strength for Laser Transmission Welding of Thermoplastic Using Artificial Neural Network

2012 ◽  
Vol 445 ◽  
pp. 454-459 ◽  
Author(s):  
M.R. Nakhaei ◽  
N.B. Mostafa Arab ◽  
F. Kordestani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Shear Strength ◽  
Laser Welding ◽  
Process Parameters ◽  
Welding Speed ◽  
Laser Power ◽  
Laser Transmission Welding ◽  
Lap Shear Strength ◽  
Lap Shear

Laser welding of plastic materials has a wide range of applications in the packaging, medical, electronics and automobile industries provided it can predict high quality welds compared with other joining methods. Laser welding process parameters can affect the quality of welds. In this paper, Artificial Neural Network (ANN) is used to model the effects of laser power, welding speed, clamp pressure and stand-off distance on weld lap-shear strength in laser transmission welding (LTW) of acrylic (polymathy methacrylate). A set of experimental data on diode laser weld lap-shear strengths was used to train and test the ANN from which the neurons relations were gradually extracted to develop a model. The developed ANN model can be used for the analysis and prediction of the complex relationships between the above mentioned process parameters and weld lap-shear strength. The results indicated that increase in laser power and clamp pressure increases the weld lap-shear strength whereas welding speed and stand off distance had a decreasing affect on shear strength at high value.

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