Evidence map›Paper›PMID 38339719›Full record

ArticleSensors (Basel, Switzerland)2024

Digital Twin Virtual Welding Approach of Robotic Friction Stir Welding Based on Co-Simulation of FEA Model and Robotic Model.

Shujun Chen, Guanchen Zong, Cunfeng Kang, Xiaoqing Jiang

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Shujun ChenCollege of Mechanical and Energy Engineering, Beijing University of Technology, Beijing 100124, China.
Guanchen ZongCollege of Mechanical and Energy Engineering, Beijing University of Technology, Beijing 100124, China.ORCID 0000-0002-4131-700X
Cunfeng KangCollege of Mechanical and Energy Engineering, Beijing University of Technology, Beijing 100124, China.ORCID 0000-0001-9153-0792
Xiaoqing JiangCollege of Mechanical and Energy Engineering, Beijing University of Technology, Beijing 100124, China.

Funding

National Key Research and Development Program of China 2018YFB1306400"QiHang Programme" for the Faculty of Materials and Manufacturing BJUT (QH202309)
6 · The paper itself

Abstract

Robotic friction stir welding has become an important research direction in friction stir welding technology. However, the low stiffness of serial industrial robots leads to substantial, difficult-to-measure end-effector deviations under the welding forces during the friction stir welding process, impacting the welding quality. To more effectively measure the deviations in the end-effector, this study introduces a digital twin model based on the five-dimensional digital twin theory. The model obtains the current data of the robot and six-axis force sensor and calculates the real-time end deviations using the robot model. Based on this, a virtual welding model was realized by integrating the FEA model with the digital twin model using a co-simulation approach. This model achieves pre-process simulation by iteratively cycling through the simulated force from the FEA model and the end displacement from the robot model. The virtual welding model effectively predicts the welding outcomes with a mere 6.9% error in lateral deviation compared to actual welding, demonstrating its potential in optimizing welding parameters and enhancing accuracy and quality. Employing digital twin models to monitor, simulate, and optimize the welding process can reduce risks, save costs, and improve efficiency, providing new perspectives for optimizing robotic friction stir welding processes.

Indexed as

CELdeviation measurementRFSWrobot stiffnesswelding process simulation

Identifiers

PMID38339719
PMCPMC10857107

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.