Advanced Welding Manufacturing: A Brief Analysis and Review of Challenges and Solutions

Zhang, Yu Ming; Yang, Yuqiu; Wei, Zhang; Na, Suck-Joo

doi:10.1115/1.4047947

Cited by 53 publications

(9 citation statements)

References 193 publications

(243 reference statements)

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“…Regarding part quality, many research works emphasize the sensing and tracking of weld seam [13]. The machine vision technologies are adopted in arc seam tracking [14].…”

Section: State-of-the-artmentioning

confidence: 99%

Development of Defect Detection AI Model for Wire + Arc Additive Manufacturing Using High Dynamic Range Images

et al. 2021

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Wire + arc additive manufacturing (WAAM) utilizes a welding arc as a heat source and a metal wire as a feedstock. In recent years, WAAM has attracted significant attention in the manufacturing industry owing to its advantages: (1) high deposition rate, (2) low system setup cost, (3) wide diversity of wire materials, and (4) sustainability for constructing large-sized metal structures. However, owing to the complexity of arc welding in WAAM, more research efforts are required to improve its process repeatability and advance part qualification. This study proposes a methodology to detect defects of the arch welding process in WAAM using images acquired by a high dynamic range camera. The gathered images are preprocessed to emphasize features and used for an artificial intelligence model to classify normal and abnormal statuses of arc welding in WAAM. Owing to the shortage of image datasets for defects, transfer learning technology is adopted. In addition, to understand and check the basis of the model’s feature learning, a gradient-weighted class activation mapping algorithm is applied to select a model that has the correct judgment criteria. Experimental results show that the detection accuracy of the metal transfer region-of-interest (RoI) reached 99%, whereas that of the weld-pool and bead RoI was 96%.

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“…Regarding part quality, many research works emphasize the sensing and tracking of weld seam [13]. The machine vision technologies are adopted in arc seam tracking [14].…”

Section: State-of-the-artmentioning

confidence: 99%

Development of Defect Detection AI Model for Wire + Arc Additive Manufacturing Using High Dynamic Range Images

et al. 2021

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“…Also, sensing devices face some delay in acquiring information from the system and further delay is observed when transferring data between devices and analyzing the information before the control action. Another constraint occurs in the melt pool when laser reflection and conducted heat add complexity to the information acquisition [17]. A literature survey has confirmed this, (e.g., [6,7]) since the majority of validation using this approach is performed for simply shaped structures, focusing on only one parameter.…”

Section: Introductionmentioning

confidence: 99%

Bead Geometry Prediction in Laser-Wire Additive Manufacturing Process Using Machine Learning: Case of Study

et al. 2021

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In Laser Wire Additive Manufacturing (LWAM), the final geometry is produced using the layer-by-layer deposition (beads principle). To achieve good geometrical accuracy in the final product, proper implementation of the bead geometry is essential. For this reason, the paper focuses on this process and proposes a layer geometry (width and height) prediction model to improve deposition accuracy. More specifically, a machine learning regression algorithm is applied on several experimental data to predict the bead geometry across layers. Furthermore, a neural network-based approach was used to study the influence of different deposition parameters, namely laser power, wire-feed rate and travel speed on bead geometry. To validate the effectiveness of the proposed approach, a test split validation strategy was applied to train and validate the machine learning models. The results show a particular evolutionary trend and confirm that the process parameters have a direct influence on the bead geometry, and so, too, on the final part. Several deposition parameters have been found to obtain an accurate prediction model with low errors and good layer deposition. Finally, this study indicates that the machine learning approach can efficiently be used to predict the bead geometry and could help later in designing a proper controller in the LWAM process.

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“…In recent years, some published reviews [17]- [20] introduced the effective applications of various advanced sensing technology (i.e., vision camera, acoustic emissions, ultrasonic testing and eddy current technique) to laser welding detection and summarized the attempts to use artificial-intelligence (AI) technology for welding quality recognition. In addition, the University of Kentucky Welding Research Laboratory [21] systematically analyzed the advanced welding manufacturing as a three-step approaches: i) pre-design that selects process and joint design based on available processes; ii) design that uses models to predict the results from a given set of welding parameters; and iii) real-time sensing and control that overcome the deviations of welding conditions by adjusting the parameters based on in-situ monitoring and adaptive control. As a matter of fact, most of the mentioned studies mainly demonstrated the capability of measuring relevant signatures and investigated the influences of the welding parameters on those measured quantities.…”

Section: Introductionmentioning

confidence: 99%

Progress and perspectives of in-situ optical monitoring in laser beam welding: Sensing, characterization and modeling

Zhang

et al. 2022

Journal of Manufacturing Processes

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Advanced Welding Manufacturing: A Brief Analysis and Review of Challenges and Solutions

Cited by 53 publications

References 193 publications

Development of Defect Detection AI Model for Wire + Arc Additive Manufacturing Using High Dynamic Range Images

Development of Defect Detection AI Model for Wire + Arc Additive Manufacturing Using High Dynamic Range Images

Bead Geometry Prediction in Laser-Wire Additive Manufacturing Process Using Machine Learning: Case of Study

Progress and perspectives of in-situ optical monitoring in laser beam welding: Sensing, characterization and modeling

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