Welding distortion prediction and mitigation in thick steel plate structures on ships

Cao, Yu; Song, Yuze; Liu, Ziyan; Wu, Taifeng; Bai, Yong

doi:10.1080/17445302.2021.2010447

Cited by 6 publications

(1 citation statement)

References 30 publications

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“…Jiangchao Wang et al have applied elastic finite element methods to predict deformations in structural welding applications, developing optimization strategies to minimize these effects [13]. Yu Cao et al have introduced an approach tailored for shipbuilding, integrating thermoelastic-plastic finite element methods with inherent deformation theory, to preemptively address deformation concerns [14]. These investigations highlight the ongoing efforts to bridge the gap between theoretical models and practical production needs, with the ultimate goal of enhancing the precision and reliability of laser welding processes.…”

Section: Introductionmentioning

confidence: 99%

Research on Predicting Welding Deformation in Automated Laser Welding Processes with an Enhanced DEWOA-BP Algorithm

Zhang,

Hu,

et al. 2024

Machines

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Welding stands as a critical focus for the intelligent and digital transformation of the machinery industry, with automated laser welding playing a pivotal role in the sector’s technological advancement. The management of welding deformation in such operations is fundamental, relying on advanced analysis and prediction methods. The endeavor to accurately analyze welding deformation in practical applications is compounded by the interplay of numerous variables, a pronounced coupling effect among these factors, and a reliance on expert intuition. Thus, effective deformation control in automated laser welding operations necessitates the gathering of pre-test laser welding data to develop a predictive approach that accurately reflects real-world conditions and is characterized by improved reliability and stability. To address the technological evolution in automated laser welding, a predictive model based on neural network technology is proposed to map the intricate relationship between process variables and the resulting deformation. At the heart of this approach is the formulation of a predictive model utilizing a back-propagation neural network (BP), with an emphasis on four essential welding parameters: speed, peak power, duty cycle, and defocusing amount. The model’s predictive accuracy is then honed through the application of the whale optimization algorithm (WOA) and the differential evolutionary (DE) algorithm. Finally, extensive testing in an automated laser welding experimental setup is conducted to validate the accuracy and reliability of the proposed prediction model. It is demonstrated through these experiments that the deformation prediction model, enhanced by the DEWOA-BP neural network, accurately forecasts the relationship between laser welding parameters and the induced deformation, maintaining a prediction error margin of ±0.1mm. The model is employed to fulfill the requirements for a pre-welding quality evaluation, thereby facilitating a more calculated and informed approach to welding operations. This method of intelligent prediction is not only crucial for the intelligent transformation of laser welding but also holds significant implications for traditional machining technologies such as milling, grinding, and spraying. It offers innovative ideas and methods that are pivotal for the industrial revolution and technological advancement of the traditional machining industry.

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Section: Introductionmentioning

confidence: 99%