Development of torch position control and welding condition control technology for all‐position, multi‐layer GTA welding. Development of fully automatic GTA welding system for pipes (2nd Report)

Imanaga, Shoji; Haneda, Mitsuaki; Saito, Nobuo; Kobayashi, Masahiro; Hino, E.

doi:10.1080/09507110009549194

Cited by 2 publications

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“…The nuclear industry in Japan made important progress regarding metal transfer mode description and control in TIG and PTA welding, particularly for pipe welding. Imanaga et al (2000) and Taguchi et al (1979aTaguchi et al ( , 1979b) used a small DC power supply to pass a control signal of constant current between the wire and the work piece, and measured the voltage drop to indicate when contact with the melt pool occurred. The average contact frequency (CF) was also calculated over a certain period and it was reported that it has a low value in TM1 and TM3 but is maximum in TM2 (depending on the processing conditions).…”

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confidence: 99%

Metal transfer modes in plasma Wire + Arc additive manufacture

Ríos

Colegrove

Williams

2019

Journal of Materials Processing Technology

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The stability of Plasma transferred arc Wire + Arc Additive Manufacture is critically dependent on the transfer mode of the wire into the melt pool which can either be in permanent contact (TM1), intermittent (TM2) or fleeting (TM3) and it's governed by the position of the wire. Analysis of the voltage waveforms in conjunction with video imaging was used to understand the preferred mode for process control and how these transfer modes are affected by convex and concave melt pools shapes. The study indicated that stable transfer can be more easily achieved with the convex weld pool, because it minimises the extent of the undesirable TM3 where droplet transfer occurs in an erratic fashion. Furthermore, even though the operational range of TM2 is limited, a control algorithm could be more easily developed for this transfer mode. Finally, video imaging showed how the melt pool oscillation interacts with droplet detachment under some operational conditions.

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confidence: 99%

Metal transfer modes in plasma Wire + Arc additive manufacture

Ríos

Colegrove

Williams

2019

Journal of Materials Processing Technology

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“…Therefore, the arc voltage related signals can be used as an efficient medium to identify the droplet transfer mode. Imanaga et al, (2000) reported research about monitoring arc voltage to identify the transfer mode in the gas tungsten arc (GTA) welding process. An extra DC power supply was used in their study, to support the constant current between the end of the filler wire and the substrate.…”

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confidence: 99%

Optimal droplet transfer mode maintenance for wire + arc additive manufacturing (WAAM) based on deep learning

et al. 2022

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In the last decade, wire + arc additive manufacturing (WAAM), which is one of the most promising metal additive manufacturing technologies, has been attracting high interest from both academia and industry. WAAM systems are increasingly employed in the industry and academia, but there are still several challenges and barriers to process stability control. The process stability is highly dependent on how the molten feed wire is added into the melt pool, which is known as the droplet transfer mode. To ensure a stable WAAM deposition process, it is essential to maintain the transfer mode in a suitable stable status. Without an effective transfer mode control method, the operators need to determine and control the transfer mode based on their experience using manual adjustment, which is difficult to achieve in a long period of production process. In this paper, a deep learning-based technology was proposed for the control of the droplet transfer mode based on the data collected from the WAAM process. A long short term memory neural network was applied as the core transfer mode classification model. A time-series data, arc voltage, was collected and statistical and frequency features were extracted, which included 11 relevant features, as the inputs of the classification model. Then, the distance between the melted wire and the melt pool was adjusted based on the determined transfer mode to keep a suitable stability of the process. A case study was used to evaluate the proposed approach and to show its merit. The proposed approach was compared to three commonly used machine learning algorithms, k-nearest neighbours, support vector machine, and decision tree. The proposed method obtained the highest accuracy in determining the transfer mode, which was over 91%. The performance of the proposed approach was also evaluated by the single-pass and oscillated wall building. The proposed deep learning based approach improved the process stability in real-time, which resulted in better deposition qualities, in terms of geometry size and processing cleanliness compared to without control. Furthermore, this data-driven method could be applied to other WAAM processes and materials.

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Development of torch position control and welding condition control technology for all‐position, multi‐layer GTA welding. Development of fully automatic GTA welding system for pipes (2nd Report)

Cited by 2 publications

References 1 publication

Metal transfer modes in plasma Wire + Arc additive manufacture

Metal transfer modes in plasma Wire + Arc additive manufacture

Optimal droplet transfer mode maintenance for wire + arc additive manufacturing (WAAM) based on deep learning

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