Experimental Investigation on a Novel Hybrid Composite Developed by Laser Engineering Net Shaping: Optimization and Ranking Analysis
Source abstract
Abstract
Recent developments in composites lead to the growth of advanced composites like titanium matrix composite (TMC) as it possesses excellent properties like lightweight and high temperature corrosion resistance, without compromising their strength and hardness. It is used widely in contemporary industries like automobile, biomedical and aerospace. Prime goals lead to the development of a novel material by laser engineering net shaping (LENS), experimental and microstructural analysis, and ranking analysis using a novel optimization algorithm. Laser power (P) and scan speed (V) provide deeper contribution when compared to energy input/area (E). The microstructural characterization illustrates an excellent bonding inside the Ti-matrix. A hybrid mathematical model is proposed on the optimality analysis of laser process parameters to laser performance measures like cooling rate (CR) and hardness (H) and the optimality set is identified. The novelty lies in the comparative and ranking analysis of the experimental runs of the laser responses of the developed novel composite by a novel optimization algorithm like desirable gray relational analysis (DGRA). Rank 1 of desirability 0.843 is obtained and contrasted with rank 2 of desirability 0.833 using DGRA when excellent melting is observed with miniature surface cracks, which can be compared with solo desirability of 0.838, with an improvement of 0.6%. To detect the contributing effects and significance parametric characteristics, ANOVA is incorporated. Optimized solution is additionally improved by 12.939% of CR and 0.253% of H.
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Additive Manufacturing Materials and Processes · Laser Material Processing Techniques · Laser and Thermal Forming Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (40)
Show source-provided references
1. M. Younas, S.H.I. Jaffery, M. Khan et al., Multi-objective Optimization for Sustainable Turning Ti6Al4V Alloy Using Grey Relational Analysis (GRA) Based on Analytic Hierarchy Process (AHP), Int. J. Adv. Manuf. Technol., 2019, 105, p 1175–1188.10.1007/s00170-019-04299-5 ↗
2. M.A. Khan, S.H.I. Jaffery, M. Khan et al., Multi-objective Optimization of Turning Titanium-Based Alloy Ti-6Al-4V Under Dry, Wet, and Cryogenic Conditions Using Gray Relational Analysis (GRA), Int. J. Adv. Manuf. Technol., 2020, 106, p 3897–3911.10.1007/s00170-019-04913-6 ↗
3. R.N. Yadav, A Hybrid Approach of Taguchi-Response Surface Methodology for Modeling and Optimization of Duplex Turning Process, Measurement, 2017, 100, p 131–138. https://doi.org/10.1016/j.measurement.2016.12.06010.1016/j.measurement.2016.12.060 ↗
4. K. Ananthakumar, D. Rajamani, E. Balasubramanian and D.J. Paulo, Measurement and Optimization of Multi-response Characteristics in Plasma Arc Cutting of Monel 400™ Using RSM and TOPSIS, Measurement, 2019, 135, p 725–737. https://doi.org/10.1016/j.measurement.2018.12.01010.1016/j.measurement.2018.12.010 ↗
5. N. Li, J.Y. Sheikh-Ahmad, A. El-Sinawi and V. Krishnaraj, Multi-objective Optimization of the Trimming Operation of CFRPs Using Sensor-Fused Neural Networks and TOPSIS, Measurement, 2019, 132, p 252–262. https://doi.org/10.1016/j.measurement.2018.09.05710.1016/j.measurement.2018.09.057 ↗
6. T. Yu, L. Yang, Yu. Zhao, J. Sun and B. Li, Experimental Research and Multi-response Multi-parameter Optimization of Laser Experimental Research and Multi-response Multi-parameter Optimization of Laser Cladding Fe313, Opt. Laser Technol., 2018, 108, p 321–332. https://doi.org/10.1016/j.optlastec.2018.06.03010.1016/j.optlastec.2018.06.030 ↗
7. J. Jin, S. Zhou, Y. Zhao, Q. Zhang, X. Wang, W. Li, D. Chen and L. Zhang, Refined Microstructure and Enhanced Wear Resistance of Titanium Matrix Composites Produced by Selective Laser Melting, Opt. Laser Technol., 2021, 134, p 106644. https://doi.org/10.1016/j.optlastec.2020.10664410.1016/j.optlastec.2020.106644 ↗
8. D. Cheng, J. Zhang, T. Shi, G. Li, J. Shi, L. Lu and G. Fu, Microstructure and mechanical properties of Additive Manufactured Ti-6Al-4V Components by Annular Laser Metal Deposition in a Semi-Open Environment, Opt. Laser Technol., 2021, 135, p 106640. https://doi.org/10.1016/j.optlastec.2020.10664010.1016/j.optlastec.2020.106640 ↗
9. T. Chen, W. Wu, W. Li and D. Liu, Laser Cladding of Nanoparticle TiC Ceramic Powder: Effects of Process Parameters on the Quality Characteristics of the Coatings and Its Prediction Model, Opt. Laser Technol., 2019, 116, p 345–355. https://doi.org/10.1016/j.optlastec.2019.03.04810.1016/j.optlastec.2019.03.048 ↗
10. I. Shivakoti, G. Kibria and B.B. Pradhan, Predictive Model and Parametric Analysis of Laser Marking Process on Gallium Nitride Material Using Diode Pumped Nd:YAG Laser, Opt. Laser Technol., 2019, 115, p 58–70. https://doi.org/10.1016/j.optlastec.2019.01.03510.1016/j.optlastec.2019.01.035 ↗
11. S. Datta, M.S. Raza, A.K. Das, P. Saha and D.K. Pratihar, Experimental Investigations and Parametric Optimization of Laser Beam Welding of NiTinol Sheets by Metaheuristic Techniques and Desirability Function Analysis, Opt. Laser Technol., 2020, 124, p 105982. https://doi.org/10.1016/j.optlastec.2019.10598210.1016/j.optlastec.2019.105982 ↗
12. Z. Gao, X. Shao, P. Jiang, L. Cao, Q. Zhou, C. Yue and Y. Liu, Parameters Optimization of Hybrid Fiber Laser-Arc Butt Welding on 316L Stainless Steel Using Kriging Model and GA, Opt. Laser Technol., 2016, 83, p 153–162. https://doi.org/10.1016/j.optlastec.2016.04.00110.1016/j.optlastec.2016.04.001 ↗
13. C. Kumar, M. Das, C.P. Paul and B. Singh, Experimental Investigation and Metallographic Characterization of Fiber Laser Beam Welding of Ti-6Al-4V Alloy Using Response Surface Method, Opt. Lasers Eng., 2017, 95, p 52–68. https://doi.org/10.1016/j.optlaseng.2017.03.01310.1016/j.optlaseng.2017.03.013 ↗
14. X. Wang, H. Chen, H. Liu, P. Li, Z. Yan, C. Huang, Z. Zhao and Y. Gu, Simulation and Optimization of Continuous Laser Transmission Welding Between PET and Titanium Through FEM, RSM, GA and experiments, Opt. Lasers Eng., 2013, 51(11), p 1245–1254. https://doi.org/10.1016/j.optlaseng.2013.04.02110.1016/j.optlaseng.2013.04.021 ↗
15. Y. Rong, Z. Zhang, G. Zhang, C. Yue, Y. Gu, Y. Huang, C. Wang and X. Shao, Parameters Optimization of Laser Brazing in Crimping Butt Using Taguchi and BPNN-GA, Opt. Lasers Eng., 2015, 67, p 94–104. https://doi.org/10.1016/j.optlaseng.2014.10.00910.1016/j.optlaseng.2014.10.009 ↗
16. A. Weisheit, S. Rittinghaus, A. Dutta and J.D. Majumdar, Studies on the Effect of Composition and Pre-heating on Microstructure and Mechanical Properties of Direct Laser Clad Titanium Aluminide, Opt Lasers Eng., 2020, 131, p 106041. https://doi.org/10.1016/j.optlaseng.2020.10604110.1016/j.optlaseng.2020.106041 ↗
17. H.Z. Niu, S.L. Xiao, F.T. Kong, C.J. Zhang and Y.Y. Chen, Microstructure Characterization and Mechanical Properties of TiB2/TiAl In Situ Composite by Induction Skull Melting Process, Mater. Sci. Eng. A., 2012, 532, p 522–527. https://doi.org/10.1016/j.msea.2011.11.01710.1016/j.msea.2011.11.017 ↗
18. H. Attar, S. Ehtemam-Haghighi, D. Kent, X. Wu and M.S. Dargusch, Comparative Study of Commercially Pure Titanium Produced by Laser Engineered Net Shaping, Selective Laser Melting and Casting Processes, Mater. Sci. Eng. A., 2017, 705, p 385–393. https://doi.org/10.1016/j.msea.2017.08.10310.1016/j.msea.2017.08.103 ↗
19. G.J. Marshall, W.J. Young, S.M. Thompson, N. Shamsaei, S.R. Daniewicz and S. Shao, Understanding the Microstructure Formation of Ti-6Al-4V During Direct Laser Deposition via In-Situ Thermal Monitoring, J. Occup. Med., 2016, 68(3), p 778–790. https://doi.org/10.1007/s11837-015-1767-z10.1007/s11837-015-1767-z ↗
20. V.K. Balla, M. Das, A. Mohammad and A.M. Al-Ahmari, Additive Manufacturing of γ-TiAl: Processing, Microstructure, and Properties, Adv. Eng. Mater., 2016, 18(7), p 1208–1215. https://doi.org/10.1002/adem.20150058810.1002/adem.201500588 ↗
21. C. Qiu, G.A. Ravi, C. Dance, A. Ranson, S. Dilworth and M.M. Attallah, Fabrication of Large Ti-6Al-4V Structures by Direct Laser Deposition, J. Alloy. Comp., 2015, 629, p 351–361. https://doi.org/10.1016/j.jallcom.2014.12.23410.1016/j.jallcom.2014.12.234 ↗
22. A.J. Sterling, B. Torries, N. Shamsaei, S.M. Thompson and D.W. Seely, Fatigue Behavior and Failure Mechanisms of Direct Laser Deposited Ti-6Al-4V, Mater. Sci. Eng. A., 2016, 655, p 100–112. https://doi.org/10.1016/j.msea.2015.12.02610.1016/j.msea.2015.12.026 ↗
23. Y. Hu, F. Ning, H. Wang, W. Cong and B. Zhao, Laser Engineered Net Shaping of Quasi Continuous Network Microstructural TiB Reinforced Titanium Matrix Bulk Composites: Microstructure and Wear Performance, Opt. Laser. Technol., 2018, 99, p 174–183. https://doi.org/10.1016/j.optlastec.2017.08.03210.1016/j.optlastec.2017.08.032 ↗
24. Y. Hu, W. Cong, X. Wang, Y. Li, F. Ning and H. Wang, Laser Deposition-Additive Manufacturing of TiB-Ti Composites with Novel Three-Dimensional Quasi-Continuous Network Microstructure: Effects on Strengthening and Toughening, Compos. B Eng., 2018, 133, p 91–100. https://doi.org/10.1016/j.compositesb.2017.09.01910.1016/j.compositesb.2017.09.019 ↗
25. H. Attar, M. Bönisch, M. Calin, L.C. Zhang, K. Zhuravleva, A. Funk, S. Scudino, C. Yang and J. Eckert, Comparative Study of Microstructures and Mechanical Properties of In Situ Ti–TiB Composites Produced by Selective Laser Melting, Powder Metallurgy, and Casting Technologies, J. Mater. Res., 2014, 29(17), p 1941–1950. https://doi.org/10.1557/jmr.2014.12210.1557/jmr.2014.122 ↗
26. S. Bose and T. Nandi, Microstructural Characterization and Measurement of Laser Responses of Lens Developed Novel Titanium Matrix Composite, Eur. Phys. J. Plus., 2021, 136, p 978. https://doi.org/10.1140/epjp/s13360-021-01951-610.1140/epjp/s13360-021-01951-6 ↗
27. S. Bose and T. Nandi, Experimental Investigation of WEDM on Titanium Hybrid Composite Reinforced with Boron Powder: A Novel Approach, Eur. Phys. J. Plus., 2020, 135, p 914. https://doi.org/10.1140/epjp/s13360-020-00904-910.1140/epjp/s13360-020-00904-9 ↗
28. S. Bose and T. Nandi, Novel Approach in Experimental and Statistical Investigations on Titanium Matrix Composite, Bull. Mater. Sci., 2021, 44(1), p 46. https://doi.org/10.1007/s12034-020-02330-010.1007/s12034-020-02330-0 ↗
29. M.H. Abidi, A.M. Al-Ahmari, U. Umer and M.S. Rasheed, Multi-objective Optimization of Micro-Electrical Discharge Machining of Nickel-Titanium-Based Shape Memory Alloy Using MOGA-II, Measurement, 2018, 125, p 336–349. https://doi.org/10.1016/j.measurement.2018.04.09610.1016/j.measurement.2018.04.096 ↗
30. S.P.L. Kumar, Measurement and Uncertainty Analysis of Surface Roughness and Material Removal Rate in Micro Turning Operation and Process Parameters Optimization, Measurement, 2019, 140, p 538–547. https://doi.org/10.1016/j.measurement.2019.04.02910.1016/j.measurement.2019.04.029 ↗
31. S.P.L. Kumar, Experimental Investigations and Empirical Modeling for Optimization of Surface Roughness and Machining Time Parameters in Micro end Milling Using Genetic Algorithm, Measurement, 2018, 124, p 386–394. https://doi.org/10.1016/j.measurement.2018.04.05610.1016/j.measurement.2018.04.056 ↗
32. C.C. Nwobi-Okoye and C.U. Uzochukwu, RSM and ANN Modeling for Production of Al 6351/ Egg Shell Reinforced Composite: Multi Objective Optimization Using Genetic Algorithm, Mater. Today Commun., 2020, 22, p 100674. https://doi.org/10.1016/j.mtcomm.2019.10067410.1016/j.mtcomm.2019.100674 ↗
33. M. Iqbal, U.A. Naeem, A. Ahmad, U. Ghani and T. Farid, Relating Groundwater Levels with Meteorological Parameters Using ANN Technique, Measurement, 2020, 164, p 108163. https://doi.org/10.1016/j.measurement.2020.10816310.1016/j.measurement.2020.108163 ↗
34. N. Mondal, S. Mandal and M.C. Mandal, FPA Based Optimization of Drilling Burr Using Regression Analysis and ANN Model, Measurement, 2020, 152(108163), p 107327. https://doi.org/10.1016/j.measurement.2019.10732710.1016/j.measurement.2019.107327 ↗
35. V.R. Adineh, C. Aghanajafi, G.H. Dehghan and S. Jelvani, Optimization of the Operational Parameters in a Fast Axial Flow CWCO2 Laser Using Artificial Neural Networks and Genetic Algorithms, Opt. Laser Technol., 2008, 40(8), p 1000–1007. https://doi.org/10.1016/j.optlastec.2008.03.00310.1016/j.optlastec.2008.03.003 ↗
36. G.D. Goh, S.L. Sing and W.Y. Yeong, A Review on Machine Learning in 3D Printing: Applications, Potential, and Challenges, Artif. Intell. Rev., 2021, 54, p 63–94. https://doi.org/10.1007/s10462-020-09876-910.1007/s10462-020-09876-9 ↗
37. X. Guan and Y.F. Zhao, Modeling of the Laser Powder–Based Directed Energy Deposition Process for Additive Manufacturing: A Review, Int. J. Adv. Manuf. Technol., 2020, 107, p 1959–1982. https://doi.org/10.1007/s00170-020-05027-010.1007/s00170-020-05027-0 ↗
38. J.H.K. Tan, S.L. Sing and W.Y. Yeong, Microstructure Modelling for Metallic Additive Manufacturing: A Review, Virtual Phys. Prototyp., 2020, 15(1), p 87–105. https://doi.org/10.1080/17452759.2019.167734510.1080/17452759.2019.1677345 ↗
39. A. Singh, S. Kapil and M. Das, A Comprehensive Review of the Methods and Mechanisms for Powder Feedstock Handling in Directed Energy Deposition, Addit. Manuf., 2020, 35, p 101388. https://doi.org/10.1016/j.addma.2020.10138810.1016/j.addma.2020.101388 ↗
40. S.L. Sing, S. Huang, G.D. Goh, G.L. Goh, C.F. Tey, J.H.K. Tan and W.Y. Yeong, Emerging Metallic Systems for Additive Manufacturing: In-situ Alloying and Multi-metal Processing in Laser Powder Bed Fusion, Prog. Mater. Sci., 2021, 119, p 100795. https://doi.org/10.1016/j.pmatsci.2021.10079510.1016/j.pmatsci.2021.100795 ↗