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Samuel, G. L.
- Experimental investigation of additive manufacturing of SS 316L using laser direct metal deposition
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Authors
Affiliations
1 Indian Institute of Technology Madras, Chennai, IN
1 Indian Institute of Technology Madras, Chennai, IN
Source
Manufacturing Technology Today, Vol 22, No 1 (2023), Pagination: 51-56Abstract
Laser direct metal deposition (LDMD) is a rapidly emerging additive manufacturing technique offering attractive characteristics like high deposition rates, component repair, and deposition of functionally graded materials. Experimental investigations have been carried out to deposit SS 316L structures at higher deposition rates using LDMD. A continuous fiber laser operating at a wavelength of 1070 nm is used to deposit the structures under different processing parameters like power, scanning speed, and powder feed rate. A power range of 600 W to 1200 W is found to be optimal with speed varying in the range between 10 mm/sec and 25 mm/sec. At low power with higher velocities, a low layer thickness is obtained and vice-versa. With an increase in the power and the decrease in the speed, deposition rates are increased. The findings will help to develop pre-processing, online-processing, and post-processing strategies for LDMD.Keywords
Laser Direct Metal Deposition, SS 316 L, Deposition Rate, Additive Manufacturing.
References
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- Sasikumar, R., Kannan, A. R., Kumar, S. M., Pramod, R., Kumar, N. P., Shanmugam, N. S., & Sivankalai, S. (2022). Wire arc additive manufacturing of functionally graded material with SS 316L and IN625: Microstructural and mechanical perspectives. CIRP Journal of Manufacturing Science and Technology, 38, 230-242.
- Steen, W. M. (2003). Rapid Prototyping and Low-volume Manufacture. In: Laser Material Processing, (279-299). Springer- London.
- Svetlizky, D., Das, M., Zheng, B., Vyatskikh, A. L., Bose, S., Bandyopadhyay, A., Schoenung, J. M., Lavernia, E. J., Eliaz, N. (2021). Directed energy deposition (DED) additive manufacturing: Physical characteristics, defects, challenges and applications. Materials Today, 49, 271-295.
- Syed, W. U. H., Pinkerton, A. J., & Li, L. (2005). A comparative study of wire feeding and powder feeding in direct diode laser deposition for rapid prototyping. Applied Surface Science, 247(1-4), 268-276. Takemura, S., Koike, R., Kakinuma, Y., Sato, Y., & Oda, Y. (2019). Design of powder nozzle for high resource efficiency in directed energy deposition based on computational fluid dynamics simulation. International Journal of Advanced Manufacturing Technology, 105(10), 4107-4121.
- Zadi-Maad, A., Rohib, R., & Irawan, A. (2018). Additive manufacturing for steels: A review. In IOP Conference Series: Materials Science and Engineering, 285(1), 012028.
- Intelligent Prediction of Machine Tool Performance in Micro Turning Using Textured Inserts
Abstract Views :58 |
PDF Views:0
Authors
Affiliations
1 Indian Institute of Technology Madras, Chennai, India., IN
1 Indian Institute of Technology Madras, Chennai, India., IN
Source
Manufacturing Technology Today, Vol 22, No 4 (2023), Pagination: 66-73Abstract
Intelligent machine tools can adapt to modifications in the machining environment while performing operations. An intelligent prediction of machine tool condition is an essential aspect in the manufacturing sector of Industry 4.0. Micro components of titanium alloys have huge applications in aerospace, optical and biomedical industries. In this study, machine learning (ML) based models are developed to forecast the performance of a micro-turning machine tool while working with plain and variously patterned textured micro inserts. The micro-turning experiments are performed on Ti6Al4V alloy and the cutting force, surface roughness and tool flank wear are measured for every machining pass. Supervised ML models are trained in order to predict the cutting force, flank wear and surface roughness with cutting parameters and the type of cutting inserts. In the comparison of developed ML models, Extreme Gradient Boost (XGBoost) performs best in prediction with the accuracy of 98.53% and runs in 40.67 milliseconds.Keywords
Micro Turning, Micro Texturing, Machine Learning Models, Tool Wear, Surface Roughness.References
- Aghazadeh, F., Tahan, A., & Thomas, M. (2018). Tool condition monitoring using spectral subtraction algorithm and artificial intelligence methods in milling process. International Journal of Mechanical Engineering and Robotics Research, 7(1), 30-34.
- Aslantas, K., & Çiçek, A. (2018). High speed turning of Ti6Al4V alloy in micro cutting conditions. Procedia CIRP, 77, 58-61.
- Aslantas, K., Danish, M., Hasçelik, A., Mia, M., Gupta, M., Ginta, T., & Ijaz, H. (2020). investigations on surface roughness and toolwear characteristics in micro-turning of Ti-6Al-4V alloy. Materials, 13(13), 1-20.
- Chakraborty, S., & Bhattacharya, S. (2021). Application of XGBoost Algorithm as a Predictive Tool in a CNC Turning Process. Reports in Mechanical Engineering, 2(2), 190-201.
- Cheng, M., Jiao, L., Shi, X., Wang, X., Yan, P., & Li, Y. (2020). An intelligent prediction model of the tool wear based on machine learning in turning high strength steel. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 234(13), 1580-1597.
- Devaraj, S., Malkapuram, R., & Singaravel, B. (2021). Performance analysis of micro textured cutting insert design parameters on machining of Al-MMC in turning process. International Journal of Lightweight Materials and Manufacture, 4(2), 210-217.
- Gouarir, A., Martínez-Arellano, G., Terrazas, G., Benardos, P., & Ratchev, S. (2018). In-process tool wear prediction system based on machine learning techniques and force analysis. Procedia CIRP, 77(Hpc), 501-504.
- Hartung, P. D., Kramer, B. M., & von Turkovich, B. F. (1982). Tool Wear in Titanium Machining. CIRP Annals - Manufacturing Technology, 31(1), 75-80.
- Jagadesh, T., & Samuel, G. L. (2014). Investigations into Cutting Forces and Surface Roughness in Micro Turning of Titanium Alloy Using Coated Carbide Tool. Procedia Materials Science, 5, 2450-2457.
- Lin, Y., Wu, K., Shih, W., Hsu, P., & Hung, J. (2020). Prediction of surface roughness based on cutting parameters and machining vibration in end milling using regression method and artificial neural network. Applied Sciences, 10(11), 3941.
- Liu, C., Vengayil, H., Zhong, R. Y., & Xu, X. (2018). A systematic development method for cyber-physical machine tools. Journal of Manufacturing Systems, 48, 13-24.
- Pramanik, A. (2014). Problems and solutions in machining of titanium alloys. International Journal of Advanced Manufacturing Technology, 70(5-8), 919-928.
- Rajesh Babu, T., & Samuel, G. L. (2022). Prediction of Machining Quality and Tool Wear in Micro-Turning Machine Using Machine Learning Models. Advances in Micro and Nano Manufacturing and Surface Engineering(1-12).
- Ribeiro, F. S. F., Lopes, J. C., Bianchi, E. C., & de Angelo Sanchez, L. E. (2020). Applications of texturization techniques on cutting tools surfaces-a survey. The International Journal of Advanced Manufacturing Technology, 109(3-4), 1117-1135. https://doi.org/10.1007/s00170-020-05669-0
- Silva, R., Dionisio, A., Leitao, P., & Barata, J. (2018). IDARTS - Towards intelligent data analysis and real-time supervision for. Computers in Industry, 101(October 2017), 138-146.
- Vasumathy, D., & Meena, A. (2017). Influence of micro scale textured tools on tribological properties at tool-chip interface in turning AISI 316 austenitic stainless steel. Wear, 376-377, 1747-1758.
- Wang, P., Gao, R. X., & Yan, R. (2017). A deep learning-based approach to material removal rate prediction in polishing. CIRP Annals - Manufacturing Technology, 66(1), 429-432.