Application of sensing technology in intelligent robotic arc welding: A review

F Xu, Y Xu, H Zhang, S Chen - Journal of Manufacturing Processes, 2022 - Elsevier
As a traditional fusion welding method, arc welding occupies most of the total welding
production. However, the current industrial welding robots with the “teaching and playback” …

Deep learning based real-time and in-situ monitoring of weld penetration: Where we are and what are needed revolutionary solutions?

R Yu, Y Cao, H Chen, Q Ye, YM Zhang - Journal of Manufacturing …, 2023 - Elsevier
Abstract Welding Procedure Designed assures the desired weld penetration be produced
under nominal welding conditions. When conditions deviate from the nominal, penetration …

Monitoring and control the Wire Arc Additive Manufacturing process using artificial intelligence techniques: a review

G Mattera, L Nele, D Paolella - Journal of Intelligent Manufacturing, 2024 - Springer
Abstract Wire Arc Additive Manufacturing is a Direct Energy Deposition additive technology
that uses the principle of wire welding to deposit layers of material to create a finished …

Recent progress of sensing and machine learning technologies for process monitoring and defects detection in wire arc additive manufacturing

Y Guo, Y Zhang, Z Pan, W Zhou - Journal of Manufacturing Processes, 2024 - Elsevier
Abstract Wire Arc Additive Manufacturing possesses advantages of high deposition rate and
low cost compared with other metal additive manufacturing processes. However, potential …

[HTML][HTML] Inference of highly time-resolved melt pool visual characteristics and spatially-dependent lack-of-fusion defects in laser powder bed fusion using acoustic and …

H Liu, C Gobert, K Ferguson, B Abranovic, H Chen… - Additive …, 2024 - Elsevier
With a growing demand for high-quality fabrication, the interest in real-time process and
defect monitoring of laser powder bed fusion (LPBF) has increased, leading manufacturers …

Monitoring Wire Arc Additive Manufacturing process of Inconel 718 thin-walled structure using wavelet decomposition and clustering analysis of welding signal

G Mattera, J Polden, N Luigi - Journal of Advanced …, 2024 - jamstjournal.com
Monitoring is a crucial aspect of modern production systems, especially in additive
manufacturing, where instabilities and defects can lead to significant economic losses due to …

Indirect porosity detection and root-cause identification in WAAM

JYII Alcaraz, W Foqué, A Sharma… - Journal of Intelligent …, 2024 - Springer
Due to the complexity of the Wire-arc Additive Manufacturing (WAAM) process, it is prone to
the occurrence of defects in the product. One of the most common defects is porosity, which …

[HTML][HTML] Machine learning-based weld porosity detection using frequency analysis of arc sound in the pulsed gas tungsten arc welding process

S Jang, W Lee, Y Jeong, Y Wang, C Won, J Lee… - Journal of Advanced …, 2024 - Elsevier
Automatic welding equipment has replaced human welders in the nuclear industry for safety
issues and uniform and high welding quality. However, automatic welding equipment cannot …

Monitoring the gas metal arc additive manufacturing process using unsupervised machine learning

G Mattera, J Polden, J Norrish - Welding in the World, 2024 - Springer
The study aimed to assess the performance of several unsupervised machine learning (ML)
techniques in online anomaly (The term “anomaly” is used here to indicate a departure from …

Utilising unsupervised machine learning and IoT for cost-effective anomaly detection in multi-layer wire arc additive manufacturing

G Mattera, EW Yap, J Polden, E Brown, L Nele… - … International Journal of …, 2024 - Springer
Wire arc additive manufacturing (WAAM) is an additive manufacturing process for building
large-sized metal components using gas metal arc welding technology. Detecting defects …