4.4 Article

Recognition of GTAW weld penetration based on the lightweight model and transfer learning

Related references

Note: Only part of the references are listed.
Article Robotics

How to Accurately Monitor the Weld Penetration From Dynamic Weld Pool Serial Images Using CNN-LSTM Deep Learning Model?

Rui Yu et al.

Summary: This article introduces how to use a deep learning network to solve the challenging problem of accurately monitoring the penetration of a fully penetrated weld pool. By using serial weld pool images and a CNN-LSTM model, sufficient information can be extracted and accurate predictions can be made.

IEEE ROBOTICS AND AUTOMATION LETTERS (2022)

Article Engineering, Manufacturing

End-to-end prediction of weld penetration: A deep learning and transfer learning based method

Wenhua Jiao et al.

Summary: This paper proposes an end-to-end deep learning approach to predict the weld penetration status from top-side images during welding, achieving a classification accuracy of 92.70%. A transfer learning approach based on ResNet is also developed to increase accuracy and training speed, with prediction accuracy improving to 96.35%.

JOURNAL OF MANUFACTURING PROCESSES (2021)

Article Engineering, Manufacturing

Burn-through prediction and weld depth estimation by deep learning model monitoring the molten pool in gas metal arc welding with gap fluctuation

Kazufumi Nomura et al.

Summary: A deep learning model was used to predict welding quality in single bevel GMAW with gap fluctuation, successfully predicting burn-through and penetration depth. The model achieved over 95% accuracy in estimating penetration depth for different sample shapes.

JOURNAL OF MANUFACTURING PROCESSES (2021)

Article Engineering, Manufacturing

Real-time recognition of arc weld pool using image segmentation network

Rui Yu et al.

Summary: This study aims to automatically track the weld pool boundary using a U-Net deep learning network, achieving accurate detection under various welding conditions. Generating representative data through multiple welding experiments ensures the reliability and robustness of the trained network.

JOURNAL OF MANUFACTURING PROCESSES (2021)

Article Engineering, Manufacturing

Vision sensing and feedback control of weld penetration in helium arc welding process

Guodong Peng et al.

Summary: A directional light-assisted vision sensing scheme is proposed in this paper to monitor welding penetration and achieve closed-loop control for consistent full penetration and constant backside bead width. The system utilizes a multiple-input single-output model predictive controller combined with an anti-windup integrator to eliminate steady-state errors caused by nonlinearity in welding. Experimental results show the developed control system effectively maintains a consistent backside bead width with rapid response and robustness against varying heat transfer conditions.

JOURNAL OF MANUFACTURING PROCESSES (2021)

Article Materials Science, Multidisciplinary

Dynamic estimation of joint penetration by deep learning from weld pool image

Yongchao Cheng et al.

Summary: This work introduces a novel approach to estimate root-pass penetration by measuring the backside bead width, using a convolutional neural network model to recognize weld penetration with satisfactory accuracy after training, validation, and testing.

SCIENCE AND TECHNOLOGY OF WELDING AND JOINING (2021)

Article Engineering, Industrial

Real-time penetration state monitoring using convolutional neural network for laser welding of tailor rolled blanks

Zhehao Zhang et al.

JOURNAL OF MANUFACTURING SYSTEMS (2020)

Article Engineering, Manufacturing

Vision based defects detection for Keyhole TIG welding using deep learning with visual explanation

Chunyang Xia et al.

JOURNAL OF MANUFACTURING PROCESSES (2020)

Article Metallurgy & Metallurgical Engineering

Deep Learning-Based Detection of Penetration from Weld Pool Reflection Images

C. Li et al.

WELDING JOURNAL (2020)

Article Automation & Control Systems

Online Monitoring and Model-Free Adaptive Control of Weld Penetration in VPPAW Based on Extreme Learning Machine

Di Wu et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2019)

Article Engineering, Manufacturing

Machine learning of weld joint penetration from weld pool surface using support vector regression

Rong Liang et al.

JOURNAL OF MANUFACTURING PROCESSES (2019)

Article Engineering, Manufacturing

Weld image deep learning-based on-line defects detection using convolutional neural networks for Al alloy in robotic arc welding

Zhifen Zhang et al.

JOURNAL OF MANUFACTURING PROCESSES (2019)

Article Engineering, Manufacturing

Automated defect classification of Aluminium 5083 TIG welding using HDR camera and neural networks

Daniel Bacioiv et al.

JOURNAL OF MANUFACTURING PROCESSES (2019)

Article Materials Science, Characterization & Testing

Automated defect classification of SS304 TIG welding process using visible spectrum camera and machine learning

Daniel Bacioiu et al.

NDT & E INTERNATIONAL (2019)

Article Engineering, Industrial

Automated control of welding penetration based on audio sensing technology

Na Lv et al.

JOURNAL OF MATERIALS PROCESSING TECHNOLOGY (2017)

Article Computer Science, Information Systems

Image Processing of Aluminum Alloy Weld Pool for Robotic VPPAW Based on Visual Sensing

Chun Jiang et al.

IEEE ACCESS (2017)

Article Engineering, Manufacturing

Visual sensing of the weld pool geometry from the topside view in keyhole plasma arc welding

X. F. Liu et al.

JOURNAL OF MANUFACTURING PROCESSES (2017)

Article Engineering, Industrial

Weld penetration sensing in pulsed gas tungsten arc welding based on arc voltage

Zhang Shiqi et al.

JOURNAL OF MATERIALS PROCESSING TECHNOLOGY (2016)

Article Engineering, Manufacturing

Frequency characteristics of weld pool oscillation in pulsed gas tungsten arc welding

Yu Shi et al.

JOURNAL OF MANUFACTURING PROCESSES (2016)

Review Multidisciplinary Sciences

Deep learning

Yann LeCun et al.

NATURE (2015)

Article Automation & Control Systems

Model-Based Predictive Control of Weld Penetration in Gas Tungsten Arc Welding

Yu Kang Liu et al.

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY (2014)

Article Automation & Control Systems

Control of 3D weld pool surface

YuKang Liu et al.

CONTROL ENGINEERING PRACTICE (2013)

Article Engineering, Multidisciplinary

Analytical real-time measurement of a three-dimensional weld pool surface

WeiJie Zhang et al.

MEASUREMENT SCIENCE AND TECHNOLOGY (2013)

Article Computer Science, Artificial Intelligence

A Survey on Transfer Learning

Sinno Jialin Pan et al.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2010)

Article Automation & Control Systems

Visual sensing and penetration control in aluminum alloy pulsed GTA welding

Chongjian Fan et al.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2009)

Article Engineering, Industrial

Infrared sensing techniques for penetration depth control of the submerged arc welding process

HC Wikle et al.

JOURNAL OF MATERIALS PROCESSING TECHNOLOGY (2001)