Robotic Welding, Intelligence and Automation

Tarn, Tzyh-Jong; Chen, Shanben; Zhou, Changjiu

doi:10.1007/978-3-540-73374-4

Cited by 33 publications

(2 citation statements)

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“…Addressing this challenge, Intelligent Welding Manufacturing Technology (IWMT) has emerged as a foreseeable trend, heavily relying on intelligentized robotic welding (Tarn and Chen, 2014). As state, "Intelligent welding involves utilizing computers to mimic, enhance, and/or replace human operators in sensing, learning, decision-making, monitoring, and control processes."…”

Section: Introductionmentioning

confidence: 99%

“…However, automated welding using robots promises higher precision, reduced cycle times, enhanced worker safety, and decreased material waste (Gyasi et al, 2019;Reisgen et al, 2019). Several methodologies contribute to the realization of IWMT, including Virtual and Digital Welding Manufacturing and Technology (V&DWMT), Intelligentized Robotic Welding Technology (IRWT), and Flexible and Agile Welding Manufacturing and Technology (F&AWMT) (Tarn and Chen, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Automatic weld joint type recognition in intelligent welding using image features and machine learning algorithms

Sonwane,

Chiddarwar

2023

AIEDAM

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Welding is the most basic and widely used manufacturing process. Intelligent robotic welding is an area that has received much consideration owing to the widespread use of robots in welding operations. With the dawn of Industry 4.0, machine learning is substantially developing to alleviate issues around applying robotic welding intelligently. Identifying the correct weld joint type is essential for intelligent robotic welding. It affects the quality of the weldment and impacts the per-unit cost. The robot controller must change different welding parameters per joint type to attain the desired weld quality. This article presents an approach that uses image features like edges, corners, and blobs to identify different weld joint types using machine learning algorithms. Feature extractors perform the task of feature extraction. The feature extractor choice is crucial for accurate weld joint identification. The present study compares the performance of five feature extractors, namely (1) Histogram of gradients, (2) Local binary pattern, (3) ReLU3 layer, (4) ReLU4 layer, and (5) Pooling layer of ResNet18 Neural network applied to classifiers like Support Vector machines, K-Nearest Neighbor and Decision trees. We trained and tested the proposed model using the Kaggle Weld joint dataset (for Butt and Fillet Joints) and our in-house dataset (for Vee, lap, and corner joints). The experimental findings show that out of the 15 models, the pre-trained ResNet18 feature extractor with an Support Vector Machines classifier has excellent performance with a threefold recognition accuracy of 98.74% for the mentioned dataset with a computation time of 31 ms per image.

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Section: Introductionmentioning

confidence: 99%