"Avtomatychne Zvaryuvannya" (Automatic Welding), #5, 2024, pp. 68-74
Automation of the manufacturing process of a drilling tool fragment using additive deposition method
A.S. Novodranov, O.M. Savytskyi
E.O. Paton Electric Welding Institute of the NAS of Ukraine
11 Kazymyr Malevych Str., 03150, Kyiv, Ukraine.
E-mail: artur19940731@gmail.com
Nowadays, 3D printing technology is actively developing and is used in the manufacture of parts for various industries. This
paper outlines the advantages, limitations, and characteristics of surfacing complex-shaped parts using pulsed arc and constant
power arc methods. Four robotic surfacing approaches were investigated, including three variations of the pulsed arc mode,
which provided high-quality deposited metal and a fine-grained structure. The deposited metal was tested for impact toughness,
and all samples demonstrated high impact toughness regardless of the surfacing method. However, the additive surfacing of
metal parts may lead to surface defects such as pores and cracks in the deposited layers. The use of a robotic system based on
an anthropomorphic robot with an integrated machine vision system enhances both the quality and productivity of the surfacing
process. The analysis of macro- and microstructures of the deposited metal specimens confirmed the effectiveness of the defect
detection system. As a result of the study, an experimental drilling auger was successfully manufactured via robotic surfacing
using a constant power arc. 13 Ref., 4 Tabl., 6 Fig.
Keywords: 3D technology, WAAM deposition, anthropomorphic robot, machine vision system, defect recognition, constant
power arc, pulsed arc
Received: 25.04.2025
Received in revised form: 19.06.2025
Accepted: 08.10.2025
References
1. Sorokin, V.G. (1989) Data base of steels and alloys. Moscow, Mashinostroenie [in Russian].
2. Paton, B.E. (1974) Technology of the fusion electric welding. Moscow, Mashinostroenie [in Russian].
3. Sterenbogen, J.A., Petrov, P.F. (1979) The influence of the crystallization temperature range on the tendency of steels to form crystallization cracks during welding. Avtomaticheskaya Svarka, 7, 10-13 [in Russian].
4. Nazarchuk, A.T. (1997) The influence of portion-discrete formation of seams on the thermal cycle of arc fusion welding. Avtomaticheskaya Svarka, 5, 13-17 [in Russian].
5. Nazarchuk, A.T. (2000) Improvement of process of consumable electrode welding with an intermittent arc. The Paton Welding J., 7, 28-31.
6. Nazarchuk, A.T., Snisar, V.V., Demchenko, E.L. (2003) Portioned heat input as a method to control structure of the weld and HAZ metal. The Paton Welding J., 12, 34-37.
7. ASTM-International, ASTM Standard F2792- 12 (2012) Standard Terminology for Additive Manufacturing Technologies.
8. Anuj V. Dongaonkar, Rajesh M. Metkar (2018) Reconstruction of Damaged Parts by Integration Reverse Engineering (RE) and Rapid Prototyping (RP). 3D Printing and Additive Manufacturing Technologies, 159-171.
https://doi.org/10.1007/978-981-13-0305-0_149. Shah, A., Aliyev, R., Zeidler, H., Krinke, S. (2023) A review of the recent developments and challenges in wire arc additive manufacturing (WAAM) process. J. Manuf. Mater. Process, 7, 97.
https://doi.org/10.3390/jmmp703009710. Rodrigues, T.A., Duarte, V., Miranda, R.M., Santos, T.G., Oliveira, J.P. (2019) Current status and perspectives on wire and arc additive manufacturing (WAAM). Materials, 12, 1121.
https://doi.org/10.3390/ma1207112111. Shapovalov, E.V., Novodranov, A.S., Vashchenko, V.M., Savytskyi, O.M., Klishchar, F.S. (2024) Robotic complex for multilayer surfacing by periodic arc and control of surface defects of the deposited metal. Avtomatychne Zvariuvannya, 6, 43-50 [in Ukrainian].
https://doi.org/10.37434/as2024.06.0712. Shapovalov, E.V., Novodranov, A.S., Vashchenko, V.M., Savytskyi, O.M., Topchev, D.D. (2025) Specifications of using intermittent action and constant power arcs in 3D welding technologies. Avtomatychne Zvariuvannya, 2, 43-50 [in Ukrainian].
https://doi.org/10.37434/as2025.02.0613. Shapovalov, E.V., Novodranov, A.S. (2025) Applying neural network technologies for quality control in robotic surfacing. In: Proc. of the VIIth Intern. Conf. on Welding and Related Technologies, Yaremche, Ukraine, 7-10 October 2024, 154- 157.
https://doi.org/10.1201/9781003518518-31
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