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Ramkumar, J.
- Use of Resnet Modelling for Tig Weld Feature Digitization And Correlation – A Technique for AI Based Welding System
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Authors
Affiliations
1 University of Cape Town, ZA
2 Indian Institute of Technology, Kanpur, IN
1 University of Cape Town, ZA
2 Indian Institute of Technology, Kanpur, IN
Source
Manufacturing Technology Today, Vol 22, No 1 (2023), Pagination: 25-32Abstract
TIG Welding is being practiced in the manufacturing industry and it demands highly skilled labour. Artificial Intelligence (AI) is developing rapidly as researchers are constantly finding new ways in which intelligent machines can add value to their industry. An AI-based welding system stands to add value by increasing production rates, improving safety, and decreasing the human input required. Weld monitoring is a key activity in the TIG welding process and successful use of AI system will enable failure prediction and the proactive corrective actions. The aim of this project is to explore, test, and compare ResNet modelling based machine learning algorithms and examine their ability to monitor welds. In this project the weld monitoring process includes collecting images of weld joint for weld feature digitization. Also, the study enables predicting whether the weld shows good quality, contamination, burn through, misalignment, lack of fusion, or lack of penetration through a ResNet modelling based image analysis.Keywords
ResNet Modelling, TIG Welding, Image Analysis, AI Based Weld System.
References
- Bacioiu, D., Melton, G., Papaelias, M., Shaw, R. (2019). Automated defect classification of Aluminium 5083 TIG welding using HDR camera and neural networks. Journal of Manufacturing Processes, 45, 603-613. 10.1016/j.jmapro.2019. 07.020
- Das, D., Pratihar, D., Roy, G., Pal, A. (2017). Phenomenological model-based study on electron beam welding process, and input-output modelling using neural networks trained by back-propagation algorithm, genetic algorithms, particle swarm optimization algorithm and bat algorithm. Applied Intelligence, 48(9), 2698-2718. 10.1007/s10489- 017-1101-2
- Fande, A. W., Taiwade, R. V., Raut, L. (2022). Development of activated tungsten inert gas welding and its current status: A review. Materials and Manufacturing Processes, 37(8), 841-876. 10.1080/10426914.2022.2039695
- Gyasi, E., Handroos, H., Kah, P. (2019). Survey on artificial intelligence (AI) applied in welding: A future scenario of the influence of AI on technological, economic, educational and social changes. Procedia Manufacturing, 38, 702-714. 10.1016/ j. promfg.2020.01.095
- Kesse, M., Buah, E., Handroos, H., Ayetor, G. (2020). Development of an artificial intelligence powered TIG welding algorithm for the prediction of bead geometry for TIG welding processes using hybrid deep learning, Metals, 10(4), 451, 2020. 10.3390/met10040451
- Plato.stanford.edu, (2022) Fuzzy Logic (Stanford Encyclopaedia of Philosophy), Plato.stanford.edu, 2022 Available: https://plato.stanford.edu/ entries/ logic-fuzzy/
- Xia, C., Pan, Z., Fei, Z., Zhang, S., Li, H. (2020). Vision based defects detection for Keyhole TIG welding using deep learning with visual explanation. Journal of Manufacturing Processes, 56, 845-855. 10.1016/j.jmapro.2020.05.033
- Influence of process variables on surface roughness of 316L stainless steel parts fabricated via selective laser melting process
Abstract Views :53 |
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Authors
Affiliations
1 National Institute of Technology Delhi, IN
2 CSIR–National Physical Laboratory, New Delhi, IN
3 Indian Institute of Technology Kanpur, Kanpur, IN
1 National Institute of Technology Delhi, IN
2 CSIR–National Physical Laboratory, New Delhi, IN
3 Indian Institute of Technology Kanpur, Kanpur, IN
Source
Manufacturing Technology Today, Vol 22, No 1 (2023), Pagination: 33-38Abstract
Selective laser melting process (SLM) is a metal additive manufacturing technique with excellent design freedom and feasibility. In SLM, a high-energy source is used to melt powder particles into a pattern of successive layers. However, the major challenge associated with the SLM process is that the parts have a high surface roughness (Ra) compared to forming, machining, and rolling processes. In this paper, the core parameters, including scan speed, hatch distance, laser power, and energy density effects discussed as the roughness parameters. The experimental runs were designed based on Taguchi L9 orthogonal array. The results displayed that Ra of samples was largely affected by laser power as compared to scanning speed and hatching spacing. The Ra of samples achieved less at high energy density. In contrast to other surface finishing operations, the polished sample showed the average Ra value of 0.049 μm manufactured at an energy density of 58.83 J/mm3.Keywords
Selective Laser Melting, Process Parameter, Energy Density, 316L SS, Surface Roughness.References
- AlMangour, B., Grzesiak, D., & Yang, J. M. (2017). In-situ formation of novel TiC-particle-reinforced 316L stainless steel bulk-form composites by selective laser melting. Journal of Alloys and Compounds, 706, 409-418.
- Aqilah, D. N., Farazila, Y., Suleiman, D. Y., Amirah, M. A. N., & Izzati, W. B. W. N. (2018). Effects of process parameters on the surface roughness of stainless steel 316L parts produced by selective laser melting. Journal of Testing and Evaluation, 46(4), 1673-1683.
- Brytan, Z. (2017). Comparison of vacuum sintered and selective laser melted steel AISI 316L. Archives of Metallurgy and Materials, 62.
- Calignano, F., Manfredi, D., Ambrosio, E. P., Iuliano, L., & Fino, P. (2013). Influence of process parameters on surface roughness of aluminum parts produced by DMLS. International Journal of Advanced Manufacturing Technology, 67(9), 2743-2751.
- Cherry, J. A., Davies, H. M., Mehmood, S., Lavery, N. P., Brown, S. G. R., & Sienz, J. (2015). Investigation into the effect of process parameters on microstructural and physical properties of 316L stainless steel parts by selective laser melting. International Journal of Advanced Manufacturing Technology, 76(5), 869-879.
- Ghorbani, J., Li, J., & Srivastava, A. K. (2020). Application of optimized laser surface re-melting process on selective laser melted 316L stainless steel inclined parts. Journal of Manufacturing Processes, 56, 726-734.
- Kurzynowski, T., Gruber, K., Stopyra, W., Kuźnicka, B., & Chlebus, E. (2018). Correlation between process parameters, microstructure and properties of 316 L stainless steel processed by selective laser melting. Materials Science and Engineering: A, 718, 64-73.
- Pant, M., Nagdeve, L., Kumar, H., & Moona, G. (2022). A contemporary investigation of metal additive manufacturing techniques. Sādhanā, 47(1), 1-19.
- Prashanth, K. G., Scudino, S., Maity, T., Das, J., & Eckert, J. (2017). Is the energy density a reliable parameter for materials synthesis by selective laser melting. Materials Research Letters, 5(6), 386-390.
- Song, B., Dong, S., Zhang, B., Liao, H., & Coddet, C. (2012). Effects of processing parameters on microstructure and mechanical property of selective laser melted Ti6Al4V. Materials & Design, 35, 120-125.
- Strano, G., Hao, L., Everson, R. M., & Evans, K. E. (2013). Surface roughness analysis, modelling and prediction in selective laser melting. Journal of Materials Processing Technology, 213(4), 589-597.
- Sun, Z., Tan, X., Tor, S. B., & Chua, C. K. (2018). Simultaneously enhanced strength and ductility for 3D-printed stainless steel 316L by selective laser melting. NPG Asia Materials, 10(4), 127-136.
- Thijs, L., Verhaeghe, F., Craeghs, T., Van Humbeeck, J., & Kruth, J. P. (2010). A study of the microstructural evolution during selective laser melting of Ti–6Al–4V. Acta materialia, 58(9), 3303-3312.
- Wang, D., Liu, Y., Yang, Y., & Xiao, D. (2016). Theoretical and experimental study on surface roughness of 316L stainless steel metal parts obtained through selective laser melting. Rapid Prototyping Journal. 22(4), 706-716.
- Yakout, M., Elbestawi, M. A., & Veldhuis, S. C. (2018). On the characterization of stainless steel 316L parts produced by selective laser melting. International Journal of Advanced Manufacturing Technology, 95(5), 1953-1974.
- Numerical and experimental study of micro-convex dimple developed by laser additive manufacturing for surface applications
Abstract Views :59 |
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Authors
Affiliations
1 Indian Institute of Technology Kanpur, IN
1 Indian Institute of Technology Kanpur, IN
Source
Manufacturing Technology Today, Vol 22, No 1 (2023), Pagination: 45-50Abstract
Surface texturing using laser is one such technique exhaustively used for enhancing the surface properties of the components. In this work, a 2D FEM is built to simulate the thermo-fluidic phenomena of surface texturing in the preplaced IN718 powder. Transient heat transfer and fluid flow were used to predict the temperature and velocity fields. Experiments are conducted to develop micro-convex dimple texture on the surface, which usually enhances the surface hydrophobicity and tribological properties. The experimental and numerical results are in good agreement and reveal that with increase in the number of pulses, the height of the micro-convex dimples decreases.Keywords
Additive Manufacturing, Convex Dimple, Texture, Simulation, Melt Pool Oscillations.References
- Bayat, M., Thanki, A., Mohanty, S., Witvrouw, A., Yang, S., Thorborg, J., Tiedje, N. S., & Hattel, J. H. (2019). Keyhole-induced porosities in Laser-based powder bed fusion (L-PBF) of Ti6Al4V: High-fidelity modelling and experimental validation. Additive Manufacturing, 30(July), 100835. https://doi.org/10.1016/j.addma.2019.100835
- Dinda, G. P., Dasgupta, A. K., & Mazumder, J. (2012). Texture control during laser deposition of nickel-based superalloy. Scripta Materialia, 67(5), 503-506.
- Etsion, I. (2005). State of the art in laser surface texturing. Journal of Tribology, 127(1), 248-253.
- Gan, Z., Yu, G., He, X., & Li, S. (2017). Numerical simulation of thermal behavior and multicomponent mass transfer in direct laser deposition of Co-base alloy on steel. International Journal of Heat and Mass Transfer, 104, 28-38.
- Hirano, K., Fabbro, R., & Muller, M. (2011). Experimental determination of temperature threshold for melt surface deformation during laser interaction on iron at atmospheric pressure. Journal of Physics D: Applied Physics, 44(43). https://doi.org/10.1088/0022-3727/44/ 43/435402
- Jiao, L., Chua, Z. Y., Moon, S. K., Song, J., Bi, G., & Zheng, H. (2018). Femtosecond laser produced hydrophobic hierarchical structures on additive manufacturing parts. Nanomaterials, 8(8), 601.
- Knapp, G. L., Raghavan, N., Plotkowski, A., & Debroy, T. (2019). Experiments and simulations on solidification microstructure for Inconel 718 in powder bed fusion electron beam additive manufacturing. Additive Manufacturing, 25, 511-521.
- Mandal, V., Sharma, S., Singh, S. S., & Ramkumar, J. (2022). Laser surface texturing in powder bed fusion: numerical simulation and experimental characterization. Metals and Materials International, 28(1), 181-196.
- Mandal, V., Tripathi, P., Kumar, A., Singh, S. S., & Ramkumar, J. (2022). A study on selective laser melting (SLM) of TiC and B4C reinforced IN718 metal matrix composites (MMCs). Journal of Alloys and Compounds, 901, 163527.
- Mandal, V., Tripathi, P., Sharma, S., Jayabalan, B., Mukherjee, S., Singh, S. S., & Ramkumar, J. (2023). Fabrication of ex-situ TiN reinforced IN718 composites using laser powder bed fusion (L-PBF): Experimental characterization and high-fidelity numerical simulations. Ceramics International.
- Otero, N., Romero, P., Gonzalez, A., & Scano, A. (2012). Surface texturing with laser micro cladding to improve tribological properties. Journal of Laser Micro/Nanoengineering, 7(2).
- Romero, P., Otero, N., González, A., García, G., & Scano, A. (2011). Additive generation of surface microstructures for fluid-dynamic applications by using single-mode fibre laser assisted microcladding. Physics Procedia, 12, 268-277.
- Sarker, A., Tran, N., Rifai, A., Elambasseril, J., Brandt, M., Williams, R., Leary, M., & Fox, K. (2018). Angle defines attachment: Switching the biological response to titanium interfaces by modifying the inclination angle during selective laser melting. Materials & Design, 154, 326-339.
- Sharma, S., Mandal, V., Ramakrishna, S. A., & Ramkumar, J. (2019). Numerical simulation of melt pool oscillations and protuberance in pulsed laser micro melting of SS304 for surface texturing applications. Journal of Manufacturing Processes, 39, 282-294.
- Simonelli, M., Tse, Y. Y., & Tuck, C. (2014). On the texture formation of selective laser melted Ti-6Al-4V. Metallurgical and Materials Transactions A, 45(6), 2863-2872.
- Wang, M., Wu, Y., Lu, S., Chen, T., Zhao, Y., Chen, H., & Tang, Z. (2016). Fabrication and characterization of selective laser melting printed Ti–6Al–4V alloys subjected to heat treatment for customized implants design. Progress in Natural Science: Materials International, 26(6), 671-677.
- Wei, H. L., Mazumder, J., & DebRoy, T. (2015). Evolution of solidification texture during additive manufacturing. Scientific Reports, 5(1), 1-7.
- Zhou, X., Li, K., Zhang, D., Liu, X., Ma, J., Liu, W., & Shen, Z. (2015). Textures formed in a CoCrMo alloy by selective laser melting. Journal of Alloys and Compounds, 631, 153-164.