Use of a Secondary Current Sensor in Plasma during Electron-Beam Welding with Focus Scanning for Process Control

Трушников, Д. Н.; Krotova, Elena; Koleva, Elena

doi:10.1155/2016/5302681

Cited by 9 publications

(8 citation statements)

References 29 publications

(52 reference statements)

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“…The arc sound sensing system is in most cases simpler than the vision sensing system; however, it needs accurate calibration [ 32 ], and noise from welding machine cannot be ignored, but it can be identified and, in most cases, filtered out without removing significant informational content from the acoustic signal. Other group of monitoring methods utilizes electric signals of welding process that are, in most cases, easy to collect (especially for GMAW process), including the signals of arc current [ 33 ] and arc voltage [ 34 ]. The arc electrical signal is easy to obtain, and the sensing system does not limit the weld accessibility.…”

Section: Introductionmentioning

confidence: 99%

Temperature-Based Prediction of Joint Hardness in TIG Welding of Inconel 600, 625 and 718 Nickel Superalloys

2021

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Welding is an important process in terms of manufacturing components for various types of machines and structures. One of the vital and still unsolved issues is determining the quality and properties welded joint in an online manner. In this paper, a technique for prediction of joint hardness based on the sequence of thermogram acquired during welding process is proposed. First, the correspondence between temperature, welding linear energy and hardness was revealed and confirmed using correlation analysis. Using a linear regression model, relations between temperature and hardness were described. According to obtained results in the joint area, prediction error was as low as 1.25%, while for HAZ it exceeded 15%. Future work on optimizing model and input data for HAZ hardness prediction are planned.

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Section: Introductionmentioning

confidence: 99%

Temperature-Based Prediction of Joint Hardness in TIG Welding of Inconel 600, 625 and 718 Nickel Superalloys

2021

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“…The mathematical model makes it possible to calculate the change in plasma parameters and the magnitude of the waveform of the secondary current in the plasma during excitation of a non-self-sustaining discharge by delivering the positive potential to the collector. Modeling the formation of this waveform is of special interest, since it is actively used when building automated control methods for EBW [21].…”

Section: Description Of the Modelmentioning

confidence: 99%

“…This result is due to the temperature's prevailing influence on the emission. According to [21], when formulating the problem the temperature is assumed to increase in the direction of the bottom of the channel.…”

Section: Influence Of the Penetration Channel's Shape On The Magnitudmentioning

confidence: 99%

Modeling the Influence of the Penetration Channel’s Shape on Plasma Parameters When Handling Highly Concentrated Energy Sources

Трушников

Саломатова

Bezukladnikov

et al. 2017

Advances in Materials Science and Engineering

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In our work to formulate a scientific justification for process control methods when processing materials using concentrated energy sources, we develop a model that can calculate plasma parameters and the magnitude of the secondary waveform of a current from a non-self-sustained discharge in plasma as a function of the geometry of the penetration channel, thermal fields, and the beam's position within the penetration channel. We present the method and a numeric implementation whose first stage involves the use of a two-dimensional model to calculate the statistical probability of the secondary electrons' passage through the penetration channel as a function of the interaction zone's depth. Then, the discovered relationship is used to numerically calculate how the secondary current changes as a distributed beam moves along a three-dimensional penetration channel. We demonstrate that during oscillating electron beam welding the waveform has the greatest magnitude during interaction with the upper areas of the penetration channel and diminishes with increasing penetration channel depth in a way that depends on the penetration channel's shape. When the surface of the penetration channel is approximated with a Gaussian function, the waveform decreases nearly exponentially.

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“…EBW is commonly used for joining metals (including refractory metals) due to the available concentrated thermal energy, up to 10 8 W/cm 2 [27], delivered by the electron beam. During EBW, process artefacts including secondary electrons (SE), backscattered electrons (BSE) and/or electrons in the plasma plume are generated above the welding zone [28]. By capturing some or all of these artefacts, electronic images [27,29] and electron signal-time series plots [28][29][30][31] can be generated post and during EBW.…”

Section: Introductionmentioning

confidence: 99%

“…During EBW, process artefacts including secondary electrons (SE), backscattered electrons (BSE) and/or electrons in the plasma plume are generated above the welding zone [28]. By capturing some or all of these artefacts, electronic images [27,29] and electron signal-time series plots [28][29][30][31] can be generated post and during EBW. Electron beam focus quality [28] and weld-quality attributes including weld-seam quality [27] and keyhole depth [32] are commonly monitored in industry.…”

Section: Introductionmentioning

confidence: 99%

Pilot capability evaluation of a feedback electronic imaging system prototype for in-process monitoring in electron beam additive manufacturing

Wong

Neary

Jones³

et al. 2018

Int J Adv Manuf Technol

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Electron beam additive manufacturing (EBAM) is an additive manufacturing (AM) technique increasingly used by many industrial sectors, including medical and aerospace industries. The application of this technology is still, however, challenged by many technical barriers. One of the major issues is the lack of process monitoring and control system to monitor process repeatability and component quality reproducibility. Various techniques, mainly involving infrared (IR) and optical cameras, have been employed in previous attempts to study the quality of the EBAM process. However, all attempts lack the flexibility to zoom-in and focus on multiple regions of the processing area. In this paper, a digital electronic imaging system prototype and a piece of macroscopic process quality analysis software are presented. The prototype aims to provide flexibility in magnifications and the selection of fields of view (FOV). The software aims to monitor the EBAM process on a layer-by-layer basis. Digital electronic images were generated by detecting both secondary electrons (SE) and backscattered electrons (BSE) originating from interactions between the machine electron beam and the processing area using specially designed hardware. Prototype capability experiments, software verification and demonstration were conducted at room temperature on the top layer of an EBAM test build. Digital images of different magnifications and FOVs were generated. The upper range of the magnification achieved in the experiments was 95 and the demonstration verified the potential ability of the software to be applied in process monitoring. It is believed that the prototype and software have significant potential to be used for in-process EBAM monitoring in many manufacturing sectors. This study is thought to be the necessary precursor for future work which will establish whether the concept is suited to working under in-process EBAM operating conditions.

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Use of a Secondary Current Sensor in Plasma during Electron-Beam Welding with Focus Scanning for Process Control

Cited by 9 publications

References 29 publications

Temperature-Based Prediction of Joint Hardness in TIG Welding of Inconel 600, 625 and 718 Nickel Superalloys

Temperature-Based Prediction of Joint Hardness in TIG Welding of Inconel 600, 625 and 718 Nickel Superalloys

Modeling the Influence of the Penetration Channel’s Shape on Plasma Parameters When Handling Highly Concentrated Energy Sources

Pilot capability evaluation of a feedback electronic imaging system prototype for in-process monitoring in electron beam additive manufacturing

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