A brief review on the Tube-to-Tube plate welding process

Srivastava, Divyansh; Chadha, Utkarsh; Gunreddy, Neha; Saini, Neeraj Kumar; Matara, Nadheem Nassar; Machindar, Omkar; Mishra, Udit Vendant; Khanna, Mayank; Kishore, S. Ram; Selvaraj, Senthil Kumaran

doi:10.1016/j.matpr.2022.05.397

Cited by 7 publications

(2 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The plasticized material will spill out if the shoulder even lifts upwards for a small time. Even at low welding speeds, one can achieve a proper weld [44]. Until the melting point, welding pressures are directly proportional to welding temperature.…”

Section: General Properties and Features Of Friction Stir Weldingmentioning

confidence: 99%

A Survey of Machine Learning in Friction Stir Welding, including Unresolved Issues and Future Research Directions

Chadha

Selvaraj

Gunreddy

et al. 2022

Material Design & Processing Communications

View full text Add to dashboard Cite

Friction stir welding is a method used to weld together materials considered challenging by fusion welding. FSW is primarily a solid phase method that has been proven efficient due to its ability to manufacture low-cost, low-distortion welds. The quality of weld and stresses can be determined by calculating the amount of heat transferred. Recently, many researchers have developed algorithms to optimize manufacturing techniques. These machine learning techniques have been applied to FSW, which allows it to predict the defect before its occurrence. ML methods such as the adaptive neurofuzzy interference system, regression model, support vector machine, and artificial neural networks were studied to predict the error percentage for the friction stir welding technique. This article examines machine learning applications in FSW by utilizing an artificial neural network (ANN) to control fracture failure and a convolutional neural network (CNN) to detect faults. The ultimate tensile strength is predicted using a regression and classification model, a decision tree model, a support vector machine for defecting classification, and Gaussian process regression (UTS). Machine learning implementation mainly promotes uniformity in the process and precision and maximally averts human error and involvement.

show abstract

Section: General Properties and Features Of Friction Stir Weldingmentioning

confidence: 99%

A Survey of Machine Learning in Friction Stir Welding, including Unresolved Issues and Future Research Directions

Chadha

Selvaraj

Gunreddy

et al. 2022

Material Design & Processing Communications

View full text Add to dashboard Cite

show abstract

“…This necessitates replacing the conventional arc welding operation with an alternative approach that inflicts significantly reduced thermal stress on the metal. This objective can be achieved by substituting the arc welding process with friction welding during the repair of composite joints [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Application of alternative technologies in repair of rolled and welded tube to tubesheet joints

Tokarev,

Karetnikov,

Fairushin

2024

E3S Web of Conf.

View full text Add to dashboard Cite

The article proposes a technology of repair of composite (i.e. obtained by welding and expanding) tube to tubesheet joints of heatexchanging apparatuses using friction welding operations devoid of heat treatment. The objective of the investigation was to determine the influence of using the proposed technology for repairing the composite joints made of 15Cr5Mo steel on the properties of the joint. Samples made by different technologies were tested for strength and tightness. The result of the study was the confirmation of compliance of the characteristics of the samples made by the proposed technology with the samples made by the currently widely used technology utilizes arc welding.

show abstract

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

Yaknesh,

Rajamurugu,

Babu

et al. 2024

Int J Adv Manuf Technol

View full text Add to dashboard Cite

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.

show abstract

A brief review on the Tube-to-Tube plate welding process

Cited by 7 publications

References 55 publications

A Survey of Machine Learning in Friction Stir Welding, including Unresolved Issues and Future Research Directions

A Survey of Machine Learning in Friction Stir Welding, including Unresolved Issues and Future Research Directions

Application of alternative technologies in repair of rolled and welded tube to tubesheet joints

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

Contact Info

Product

Resources

About