Prediction and numerical simulation of residual stress in multi-pass pipe welds

Alhafadhi, Mahmood Hasan; Krállics, György

doi:10.1556/606.2020.00127

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…It calculated the residual stress distribution, temperature and hardness distribution of multi pass welding through two-dimensional thermal finite element model, and takes temperature dependence as an important factor to consider. The results showed that the simulation results have a high degree of consistency with the test results, which can save time [15]. P.Asadi's team raised a non coupled thermal 3D model for numerical analysis of residual stress.…”

Section: Related Workmentioning

confidence: 84%

Multi-Layer and Multi-Channel Dynamic Routing Planning and Initial Point Positioning of Weld Seam Based on Machine Vision

Li,

Zheng

2023

IEEE Access

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The welding process for medium and thick plates typically involves multi-layering and multichanneling, but its quality and reliability require further improvement. Therefore, this study introduces the convolution neural network algorithm to establish a deep learning model for weld seam recognition. Additionally, the structured light imaging method is used to accurately position the V-shaped weld groove. Meanwhile, a machine vision based multi-layer and multi-pass dynamic routing planning algorithm was also studied and designed, and a contour feature point recognition algorithm for the filling layer was developed. Thus, dynamic routing planning is achieved. It is demonstrated that the difference between the coordinates acquired by the deep learning model and the ideal region decreases steadily and reaches a minimum of (200,80). The confidence level of weld seam detection gradually increases with the adjustment of the welding robot, reaching a maximum of 98%. The confidence level of the detected feature points reaches 100%. In the meantime, the remaining height of the fusion after welding is 2.5mm. There are no negative phenomena present on the surface of the weld seam, meeting the necessary process requirements. Such discrepancies as undercut, incomplete penetration, slag inclusion, and porosity are absent. It shows that the welding technology based on machine vision has strong feasibility, effectively improves the automation level and efficiency of welding technology, and provides reliable technical support for the development of modern machine vision welding technology.

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Section: Related Workmentioning

confidence: 84%

Multi-Layer and Multi-Channel Dynamic Routing Planning and Initial Point Positioning of Weld Seam Based on Machine Vision

Li,

Zheng

2023

IEEE Access

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“…The most recent study by [5] has used simple BERT architecture without considering the importance of preprocessing steps like handling of Not a Number (NaN) values, stopword removal, PoS tagging, contractions, stemming and lemmatization, which suggests that probably their model was not trained on good data, which may have led to model over-fitting [21][22][23][24]. The researchers also did not consider the fine-tuning strategies [25][26], which supplement the model to achieve better results. In this study, all the steps mentioned above were performed and try to identify the abuse in the multilingual text as it is shown in Table 1.…”

Section: Resultsmentioning

confidence: 99%

Identification of online harassment using ensemble fine-tuned pre-trained Bert

Ganie

Dadvandipour

2022

Pollack

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Identification of online hate is the prime concern for natural language processing researchers; social media has augmented this menace by providing a virtual platform for online harassment. This study identifies online harassment using the trolling aggression and cyber-bullying dataset from shared tasks workshop. This work concentrates on extreme pre-processing and ensemble approach for model building; this study also considers the existing algorithms like the random forest, logistic regression, multinomial Naïve Bayes. Logistic regression proves to be more efficient with the highest accuracy of 57.91%. Ensemble bidirectional encoder representation from transformers showed promising results with 62% precision, which is better than most existing models.

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Digital radiography in integrity evaluation of metal injection molding products

Konda

Sotja

Zeqo

2022

Pollack

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Metal injection molding has undergone great growth in the last years and is widely used in the manufacturing of small-sized and geometrically complex metal parts in high-volume production series in many applications. This paper makes overview of the integrity evaluation of metal injection molding production. Digital radiography can automatize the process of controlling various discontinuities inside the material, with sensitivity acceptable by the standard. Image processing through software Rythm Review 2.2, allows the detection of discontinuities in complicated geometric shapes. Tests were made on items with thicknesses 3 – up to 8 mm, with complicated geometry. The result is satisfactory in terms of localization and evaluation of defects in both dimensions and typology.

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Prediction and numerical simulation of residual stress in multi-pass pipe welds

Cited by 3 publications

References 16 publications

Multi-Layer and Multi-Channel Dynamic Routing Planning and Initial Point Positioning of Weld Seam Based on Machine Vision

Multi-Layer and Multi-Channel Dynamic Routing Planning and Initial Point Positioning of Weld Seam Based on Machine Vision

Identification of online harassment using ensemble fine-tuned pre-trained Bert

Digital radiography in integrity evaluation of metal injection molding products

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