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Ramesh Babu, N.
- Thermo-mechanical modelling for prediction of residual stresses in laser powder bed fusion fabricated Ti6Al4V components
Authors
1 Indian Institute of Technology Madras, Chennai, IN
Source
Manufacturing Technology Today, Vol 22, No 1 (2023), Pagination: 63-68Abstract
Additive Manufacturing, mainly the LPBF process, can be used to fabricate valuable components for aerospace applications, including parts for Launch Vehicles. This would significantly reduce the number of parts in the assembly, time, and cost. However, the rapid heating and cooling cycles in an LPBF process result in residual stresses in the fabricated components, limiting their usage. To achieve dimensional accuracy and to prevent premature fatigue failure of components, it is essential to get a reasonable estimation of the residual stresses induced in the component. The development of Thermo-Mechanical models for their estimation is of utmost importance since numerous process variables influence the amount of residual stress created during the LPBF process, and experimental measurement of residual stresses is time-consuming and expensive. In the present study, an attempt has been made to study the effect of preheating on the residual stresses using the developed Thermo-Mechanical model.Keywords
Laser Powder Bed Fusion (LPBF), Ti6Al4V, Residual Stress, Preheating, Thermo-Mechanical, Temperature Gradient.References
- Alderson, N. A. (2012). Thermal Modeling and Simulation of Electron Beam Melting for Rapid Prototyping on Ti6Al4V Alloys, [Doctoral dissertation, North Carolina State University].
- Ali, H., Ghadbeigi, H. & Mumtaz, K. (2018). Residual stress development in selective laser-melted Ti6Al4V: a parametric thermal modelling approach. International Journal of Advanced Manufacturing Technology, 97, 2621-2633.
- Boivineau, M., Cagran, C., Doytier, D. et al. (2006).Thermophysical Properties of Solid and Liquid Ti-6Al-4V (TA6V) Alloy. International Journal of Thermophysics, 27(2), 507-529. https://doi.org/10.1007/PL00021868
- Dunbar, A. J., Denlinger, E. R., Gouge, M.F., & Michaleris, P. (2016). Experimental validation of finite element modeling for laser powder bed fusion deformation. Additive Manufacturing, 12, 108-120.
- Gouge, M. F., Michaleris, P., Denlinger, E. R., & Irwin, J. E. (2016). The Finite Element Method for the Thermo-Mechanical Modeling of Additive Manufacturing Processes. Thermo-Mechanical Modeling of Additive Manufacturing, (19-38). Elsevier BV.
- Liu, S., Zhu, H., Peng, G., Yin, J., & Zeng, X. (2018). Microstructure Prediction of Selective Laser Melting AlSi10Mg Using Finite Element Analysis. Materials & Design, 142, 319-328.
- Parry, L., Ashcroft, I. A., & Wildman, R. D. (2016). Understanding the effect of laser scan strategy on residual stress in selective laser melting through thermo-mechanical simulation. Additive Manufacturing, 12(A), 1-15.
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- Safdar, S., Pinkerton, A.J., Li, L., Sheikh, M.A., & Withers, P.J. (2013). An anisotropic enhanced thermal conductivity approach for modelling laser melt pools for Ni-base super alloys. Applied Mathematical Modelling, 37(3), 1187-1195.
- Approach for Determining the Availability of Machine Tools Based on Skill Level of Operator and Service Personnel
Authors
1 Indian Institute of Technology Madras, Chennai, IN
Source
Manufacturing Technology Today, Vol 22, No 2 (2023), Pagination: 67 - 72Abstract
Machine tools (MTs) availability is a critical measure for production scheduling. On the other hand, the machine tool availability depends on its reliability (mean time between failure, MTBF) and maintainability (mean time to repair, MTTR), which depends on multiple factors and uncertainties. One can consider the skill level of operators and service personnel that influence MTBF and MTTR of machine tools, which in turn can affect operating cost. The studies on the effect of the skill level of operators and service personnel on MT availability are limited. Generally, MT availability should be higher to maintain reasonable operating costs. In the current work, a multi-objective optimization (MOO) problem was formulated to maximize the MT availability and minimize the MT life cycle cost, considering the skill levels of the MT operators and service personnel. The results of this study help the management identify the skill level of operators and service engineers for maximum MT availability.
Keywords
Availability, Multi-Objective Optimization, Skill Level, Genetic AlgorithmReferences
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- Houshyar, A. (2004). Reliability and maintainability of machinery and equipment, part 1: Accessibility and assessing machine tool R&M performance. International Journal of Modelling and Simulation, 24(4), 201-210. https://doi.org/ 10.1080/02286203.2004.11442304
- Houshyar, A. (2005). Reliability and maintainability of machinery and equipment, part 2: Benchmarking, life-cycle cost, and predictive maintenance. International Journal of Modelling and Simulation, 25(1), 1-11. https://doi.org/10. 1080/02286203.2005.11442313
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- Wan, S., Li, D., Gao, J., & Li, J. (2019). A knowledge based machine tool maintenance planning system using case-based reasoning techniques. Robotics and Computer-Integrated Manufacturing, 58, (January), 80-96. https:// doi.org/10.1016/j.rcim.2019.01.012
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