Zufu Pan

Beibu Gulf University

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Xianping Zeng

Chinese Academy of Sciences

Quan Li

Xinjiang University

Xiaohu Huang

Beibu Gulf University

Zhiqiang Feng

Beibu Gulf University

Hao Yuan

Chongqing Three Gorges University

Xiaohong Xiang

Chongqing University of Posts and Telecommunications

Xin Li

University of Science and Technology of China

Xin Li

Central South University

Xin Li

Wuhan University of Technology

Bingqian Li

Beibu Gulf University

Cooperative Institutions

Beibu Gulf University

Chinese Academy of Sciences

Huazhong University of Science and Technology

Wuhan University

University of Chinese Academy of Sciences

Shanghai Jiao Tong University

Qingdao University of Science and Technology

Czech Academy of Sciences, Institute of Physics

National Engineering Research Center of Electromagnetic Radiation Control Materials

University of Electronic Science and Technology of China

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Defect Identification for Mild Steel in Arc Welding Using Multi-Sensor and Neighborhood Rough Set Approach

Applied Sciences (2024)

Xianping Zeng Zhiqiang Feng Xiaohong Xiang Xin Li Xiaohu Huang

Welding technology plays a vital role in the manufacturing process of ships, automobiles, and aerospace vehicles because it directly impacts their operational safety and reliability. Hence, the development of an accurate system for identifying welding defects in arc welding is crucial to enhancing the quality of welding production. In this study, a defect recognition method combining the Neighborhood Rough Set (NRS) with the Dingo Optimization Algorithm Support Vector Machine (DOA-SVM) in a multisensory framework is proposed. The 195-dimensional decision-making system mentioned above was constructed to integrate multi-source information from molten pool images, welding current, and vibration signals. To optimize the system, it was further refined to a 12-dimensional decision-making setup through outlier processing and feature selection based on the Neighborhood Rough Set. Subsequently, the DOA-SVM is employed for detecting welding defects. Experimental results demonstrate a 98.98% accuracy rate in identifying welding defects using our model. Importantly, this method outperforms comparative techniques in terms of quickly and accurately identifying five common welding defects, thereby affirming its suitability for arc welding. The proposed method not only achieves high accuracy but also simplifies the model structure, enhances detection efficiency, and streamlines network training.

Robot welding

10.3390/app14124978

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Neighborhood Rough Fuzzy Penetration Control Method with Variable Precision Based on GMAW

Applied Sciences (2023)

Xiaohong Xiang Zhiqiang Feng Hao Yuan Xianping Zeng Zufu Pan

Considering the nonlinear, time-varying, and multivariate coupling nature of the welding process, achieving excellent control of the welding process can be challenging. In addition, welding experience varies from person to person, making it difficult to establish a uniform standard. In this work, rough set theory is introduced and applied to arc welding process modeling and quality control to achieve effective online control of weld penetration during welding. A variable precision neighborhood rough-fuzzy method is proposed to enhance the efficiency and adaptability of rough set theory for information processing in the welding process. By designing welding experiments with different gaps and currents, descriptors such as the tail area coefficient and the length-width ratio of the melt pool have been proposed to characterize the melt pool. Rough set theory has been used to extract decision classification rules for welding percolation state information, and clustering analysis, fuzzy logic, and min-max fuzzy methods have been introduced for knowledge modeling. The proposed variable precision neighborhood rough-fuzzy control model is verified via three sets of experiments, and the results show that the model has excellent stability and effectiveness.

Weld pool

10.3390/app13169215

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Prediction of MAG Welding Formation Based on Fuzzy Interpolation Reasoning

Applied Sciences (2023)

Zufu Pan Zhiqiang Feng Hao Yuan Xin Li

Welding is an important production and processing method in industrial production. Due to the uncertainty of its process and the complexity of its mechanism, it is challenging to construct an accurate welding process parameter model to describe it. Fuzzy interpolation reasoning is a valuable tool in the realm of intelligent systems. It helps to reduce the complexity of fuzzy models and can be used for reasoning in fuzzy systems with sparse rule bases. This paper provides an interpolation reasoning method that combines fuzzy set distance and similarity. The proposed method is applied to welding forming predictions, and welding penetration is predicted by the given welding current and speed. The results show that the algorithm has high calculation accuracy.

Interpolation

10.3390/app13137546

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