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Hamid, M. F.
- A Deep Study on Machine Learning Techniques for Tool Condition Monitoring in Turning of Titanium-based Superalloys.
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Authors
Affiliations
1 School of Mechanical Engineering, Ramaiah Institute of Technology, VTU, India., IN
2 School of Mechanical Engineering, REVA University, India., IN
3 Advanced Material Research Cluster, Faculty of Bioengineering and Technology, Universiti Malaysia Kelantan, Jeli,Kelantan, Malaysia., MY
4 School of Mechanical Engineering, Universiti Sains Malaysia, Engineering Campus, Seri Ampangan, 14300 Nibong Tebal, Penang, Malaysia., MY
1 School of Mechanical Engineering, Ramaiah Institute of Technology, VTU, India., IN
2 School of Mechanical Engineering, REVA University, India., IN
3 Advanced Material Research Cluster, Faculty of Bioengineering and Technology, Universiti Malaysia Kelantan, Jeli,Kelantan, Malaysia., MY
4 School of Mechanical Engineering, Universiti Sains Malaysia, Engineering Campus, Seri Ampangan, 14300 Nibong Tebal, Penang, Malaysia., MY
Source
Journal of Mines, Metals and Fuels, Vol 70, No 10A (2022), Pagination: 265-270Abstract
The current state-of-the-art review on tool condition monitoring for turning of titanium-based superalloys is presented in this paper. Titanium (Ti) superalloys are widely utilised in aerospace industry, automobile industry, petrochemical applications. Ti superalloys are also used in fabrication of biomedical components due to their outstanding combination of mechanical properties and strong corrosion resistance at extreme temperatures. But these superalloys are difficult-to-cut because to their low heat conductivity, low elastic modulus, high strength, and strong chemical resistance. Literature review highlights the drastic reduction in tool life of titanium superalloys at highspeed and feed rates throughout the machining process. The review paper focuses on (i) various reasons to deploy tool condition monitoring; and (ii) study of tool condition monitoring methods based on machine learning techniques to identify the ideal parameters for the prevention of catastrophic tool failure.
References
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- R. Corne, C. Nath, M. el Mansori, and T. Kurfess, “Enhancing Spindle Power Data Application with Neural Network for Real-time Tool Wear/Breakage Prediction During Inconel Drilling,” in Procedia Manufacturing, 2016, vol. 5, pp. 1–14. doi: 10.1016/ j.promfg.2016.08.004.
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- Design and Analysis of Bipolar Plate of Polymer Electrolyte Membrane Fuel Cell Assembly used for Automotive Applications
Abstract Views :74 |
PDF Views:0
Authors
Rakshith T M
1,
Pramodkumar S Kataraki
1,
Ayub Ahmed Janvekar
2,
A F Zubair
3,
Isam Qasem
4,
Ahmed A. Hussien
4,
Aulia Ishak
5,
M. Mazlan
6,
M. F. Hamid
7
Affiliations
1 chool of Mechanical Engineering, REVA University, Bengaluru 560064, Karnataka, India., IN
2 School of Mechanical Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu 600127, India., IN
3 Intelligent and Smart Manufacturing Centre, Center for Mechanical Engineering Studies, UniversitiTeknologi MARA,Penang Branch, Malaysia., MY
4 Department of Mechanical Engineering, Al-Huson University College, Al-Balqa Applied Uni-versity, Irbid, Jordan., JO
5 Department of Industrial Engineering, Universitas Sumatera Utara, Medan 20155, Indonesia., ID
6 Advanced Material Research Cluster, Faculty of Bioengineering and Technology, Universiti Malaysia Kelantan, Jeli,Kelantan, Malaysia., MY
7 School of Mechanical Engineering, Universiti Sains Malaysia, Engineering Campus, Seri Ampangan, 14300 NibongTebal,Penang, Malaysia., MY
1 chool of Mechanical Engineering, REVA University, Bengaluru 560064, Karnataka, India., IN
2 School of Mechanical Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu 600127, India., IN
3 Intelligent and Smart Manufacturing Centre, Center for Mechanical Engineering Studies, UniversitiTeknologi MARA,Penang Branch, Malaysia., MY
4 Department of Mechanical Engineering, Al-Huson University College, Al-Balqa Applied Uni-versity, Irbid, Jordan., JO
5 Department of Industrial Engineering, Universitas Sumatera Utara, Medan 20155, Indonesia., ID
6 Advanced Material Research Cluster, Faculty of Bioengineering and Technology, Universiti Malaysia Kelantan, Jeli,Kelantan, Malaysia., MY
7 School of Mechanical Engineering, Universiti Sains Malaysia, Engineering Campus, Seri Ampangan, 14300 NibongTebal,Penang, Malaysia., MY
Source
Journal of Mines, Metals and Fuels, Vol 70, No 10A (2022), Pagination: 306-310Abstract
A polymer electrolyte membrane fuel cell (PEMFC) is defined as a type of fuel cell used to generate voltage and current. A fuel cell produces very small amount of electrical energy about 0.7 volts. So, it is essential to stack the fuel cells in bipolar plate series connection for the production of the large amount of electrical energy to fulfil the requirement. However, it is required to stack them with uniform pressure distribution in order to minimize the chance of BPP, MEA and GDL damage, fuel leakage and contact resistance. The mechanical properties and geometrical attributes of PEMFC stack components were collected with the help of many journal papers and books for the sake of their design and simulation work. In this study, the finite element analysis (FEA) were employed to simulate the bipolar plates meant for the assessment of the uniform stress dissemination.
Keywords
Polymer Electrolyte Membrane Fuel Cell, Bipolar Plate, Membrane Electrode Assembly, Gas Diffusion Layer and Finite Element Analysis.References
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