Weld-Quality Prediction Algorithm Based on Multiple Models Using Process Signals in Resistance Spot Welding

Park

et al. 2023

Metals

Self Cite

The manufacture of complicated automobile components that are joined by resistance spot welding requires considerable cost and time. The use of additive manufacturing technology to manufacture automobile components helps reduce the overall time consumption and yields high accuracy. In this study, the weldability of conventional (C) 316L stainless steel and additive manufactured (AM) 316L stainless steel was evaluated and analyzed. After deriving the lobe diagram for both the materials, the monitoring data, nugget diameter, tensile shear strength, and hardness were analyzed. The findings of the study have opened up a massive potential for use in resistance spot welding technology for additive manufactured materials’ industries in the forthcoming years. When AM 316L stainless steel was welded in the constant current control mode, a nugget diameter of up to 4.7 mm, which is below the international standard, could be secured. Through the constant power control mode, however, the nugget diameter could be improved to a sufficient level of 5.8 mm.

Section: Methodsmentioning

confidence: 99%

Weldability of Additive Manufactured Stainless Steel in Resistance Spot Welding

Park

et al. 2023

Metals

Self Cite

“… Experimental method which involves selecting welding times through trial and error. Experiments are conducted on different cells with different welding times, and the quality of the connections is evaluated to determine the optimal welding times [17];  Theoretical method that involves mathematical modelling of the welding process and determining optimal welding times based on the model [18];  Hybrid method that combines experimental and theoretical methods to select optimal welding times [19].…”

Section: Spot Welding Parameters Affecting the Qualitymentioning

confidence: 99%

Quality Analysis of Welds Made with an Automatic Battery Pack Spot Welding Machine

Racewicz,

Fijałkowski

2023

ELEKTRON ELEKTROTECH

This paper presents quality testing of battery pack welds for different welding time parameters of an automatic resistance spot welding machine. Several quality testing methods commonly used in studies of welded joints were described. Finally, the breakage test destructive method was chosen and used in this research. To investigate the influence of welder parameters on the quality of the welded joints, the programmable resistance spot welding machine was designed and constructed. As the studied samples, the 18650 cell type and the 8 mm × 0,15 mm cell packaging nickel plated steel tape were chosen as the most commonly used for the construction of battery packs for personal electric vehicles. The impact of each time constants set on the spot welding process was measured by the self-designed and constructed strength testing machine. After a series of tests with different parameters of spot welding process, the final conclusions were formulated.

“…In [21], was proposed the use of Random Forest regression model, based on particle swarm optimization (PSO-RFR), to predict the welding parameters [22]. To improve the weld quality and to enhance the automation capability, an automatic reading and writing of the weld process parameters for the PLCs is designed.…”

Section: Parametric Analysis Using Machine Learningmentioning

confidence: 99%

Machine Learning for Parametrical Analysis of Friction Stir Welded Aluminum Metal Matrix Composites

Saravanan

Giridharan

2022

AWET

The research focuses on the behaviour and process parametric influence on friction stir welded Al metal matrix composites reinforced with varied percentages of SiC, B4C, and Mg. The experimentation involves fabrication of Al metal matrix composites followed by friction stir welding and, subsequently, evaluation of the joint properties in terms of mechanical strength, microstructural integrity, and quality. In comparison to other joints with varied base material compositions, the weld exhibits refined grains and uniform distribution of hybrid particles in the joint region, resulting in increased strength. Higher SiC composition adds to greater strength, better wear characteristics, and harness, whereas B4C percentage is linked to hardness. The maximum ultimate tensile stress for a particular sample was determined to be around 160MPa, while the maximum percentage elongation was found to be around 165 for 10% SiC and 3% B4C. As the amount of SiC declines and that of B4C rises, the percentage elongation decreases. In samples with a B4C weight percentage of 10%, the greatest hardness measured was around 103Hv. For a load of 30N, the wear rate was as high as 12gm/s with a SiC weight percentage of 10. For lower load values and a higher percentage of B4C, the wear rate often decreased. Chemical properties are barely changed. Therefore, the materials keep their original qualities after welding. During the non-destructive testing process, no large cracks, pores, or clusters of pores are found, indicating that the weld is of good quality. To achieve a satisfactory weld, optimal ranges based on analysis using machine learning of rotary tool speed, tool linear velocity, transverse speed are maintained. Linear Regression algorithm, Random Forest algorithm and Lasso Regression algorithms are being used and the results are also compared. This work covers a wide range of topics, and the results are found to have improved significantly in most cases and is in good agreement with data previously presented in the literatures.