Prediction and optimization of mechanical strength of diffusion bonds using integrated ANN-GA approach with process variables and metallographic characteristics

Britto, A. Sagai Francis; Raj, R. Edwin; Mabel, M. Carolin

doi:10.1016/j.jmapro.2018.04.015

Cited by 20 publications

(1 citation statement)

References 24 publications

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“…The cryo-rolled AA2219 alloy has been welded by means of FSW, in which the ANN-GA is employed for optimizing the welding parameters such as the tool tilt angle, travel speed and rotational speed [22]. Edwin et al [23] have proposed the ANN-GA for optimizing the parameter of the diffusion bonding process to improve the tensile and shear strength of both the AA6061 and AA7075 joints. Hongyang et al [24] have employed the hybrid laser TIG welding process to join the carbon fiber reinforced polymer and an Al alloy.…”

Section: Introductionmentioning

confidence: 99%

Bulletin of the Polish Academy of Sciences: Technical Sciences

Dharmalingam,

Lenin,

Srinivasan

2021

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The influence of friction stir welding (FSW) in automotive applications is significantly high in recent days as it can boast beneficial factors such as less distortion, minimized residual stresses and enhanced mechanical properties. Since there is no emission of harmful gases, it is regarded as a green technology, which has an energy efficient clean environmental solid-state welding process. In this research work, the FSW technique is employed to weld the AA8011-AZ31B alloy. In addition, the L16 orthogonal array is employed to conduct the experiments. The influences of parameters on the factors such as microstructure, hardness and tensile strength are determined. Microstructure images have shown tunnel formation at low rotational speed and vortex occurrence at high rotational speed. To attain high quality welding, the process parameters are optimized by using a hybrid method called an artificial neural network based genetic algorithm (ANN-GA). The confirmation tests are carried out under optimal welding conditions. The results obtained are highly reliable, which exhibits the optimal features of the hybrid method.

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Section: Introductionmentioning

confidence: 99%

Bulletin of the Polish Academy of Sciences: Technical Sciences

Dharmalingam,

Lenin,

Srinivasan

2021

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A technical perspective on integrating artificial intelligence to solid-state welding

Yaknesh,

Rajamurugu,

Babu

et al. 2024

Int J Adv Manuf Technol

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The implementation of artificial intelligence (AI) techniques in industrial applications, especially solid-state welding (SSW), has transformed modeling, optimization, forecasting, and controlling sophisticated systems. SSW is a better method for joining due to the least melting of material thus maintaining Nugget region integrity. This study investigates thoroughly how AI-based predictions have impacted SSW by looking at methods like Artificial Neural Networks (ANN), Fuzzy Logic (FL), Machine Learning (ML), Meta-Heuristic Algorithms, and Hybrid Methods (HM) as applied to Friction Stir Welding (FSW), Ultrasonic Welding (UW), and Diffusion Bonding (DB). Studies on Diffusion Bonding reveal that ANN and Generic Algorithms can predict outcomes with an accuracy range of 85 – 99%, while Response Surface Methodology such as Optimization Strategy can achieve up to 95 percent confidence levels in improving bonding strength and optimizing process parameters. Using ANNs for FSW gives an average percentage error of about 95%, but using metaheuristics refined it at an incrementally improved accuracy rate of about 2%. In UW, ANN, Hybrid ANN, and ML models predict output parameters with accuracy levels ranging from 85 to 96%. Integrating AI techniques with optimization algorithms, for instance, GA and Particle Swarm Optimization (PSO) significantly improves accuracy, enhancing parameter prediction and optimizing UW processes. ANN’s high accuracy of nearly 95% compared to other techniques like FL and ML in predicting welding parameters. HM exhibits superior precision, showcasing their potential to enhance weld quality, minimize trial welds, and reduce costs and time. Various emerging hybrid methods offer better prediction accuracy.

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