Fuzzy based modeling and optimization of EDMed response of Zircaloy-2
Source abstract
Abstract
In this work, fuzzy model was developed that predicts response parameters and surface properties of an electrical discharge machined Zircaloy-2. Taguchi L 18 mixed design was used to perform the experiments using different process parameters (polarity, pulse-on-time, pulse-off-time, tool electrode material, and peak current). Material removal rate (MRR) and tool wear rate (TWR) were chosen as machining response parameters, whereas number of particles (NoP) and the percentage particle area (PPA) for surface properties of EDMed surface. Digital image processing tool was used to evaluate the surface properties. Fuzzy-Sugeno (FS)-model was developed to predict MRR, TWR, NoP, and PPA. Model accuracy was found to be 94% for MRR and TWR, and 92% for NoP and PPA. Maximum MRR 1.53 × 10 −3 mm 3 /min found when machining was performed using graphite tool with negative polarity. Fuzzy Sugeno-GRA method was successfully implemented to predict optimal response corresponding to high value of GRG.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Machining and Optimization Techniques · Non-Destructive Testing Techniques · Advanced machining processes and optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Mechanical Engineering, Bundelkhand Institute of Engineering and Technology Jhansi, Jhansi, India
Department of Mechanical Engineering, Institute of Aeronautical Engineering, Hyderabad, Telangana
Department of Civil Engineering, GLA University, Mathura, India
Department of Mathematics, Deshbandhu college, University of Delhi, Delhi, India
Division of Research and Development, Lovely Professional University, Phagwara, India
Department of Physics, College of Natural science, Dambi Dollo University, Dambi Dollo, Ethiopia
References (39)
Show source-provided references
1. 10.1016/S0890-6955(03)00162-710.1016/S0890-6955(03)00162-7 ↗
2. DiBitonto DD · J Appl Phys · 1989 · 66 · 409510.1063/1.343994 ↗
3. 10.1063/1.33561810.1063/1.335618 ↗
4. Gostimirovic M · J Mech Sci Technol · 2012 · 26 · 17310.1007/s12206-011-0922-x ↗
5. 10.1080/10426914.2014.93095610.1080/10426914.2014.930956 ↗
6. 10.1016/j.cja.2015.12.01610.1016/j.cja.2015.12.016 ↗
7. 10.1080/10426914.2022.204929910.1080/10426914.2022.2049299 ↗
8. Muthuramalingam T · Mater Manuf Process · 2014 · 29 · 137410.1080/10426914.2014.930956 ↗
9. 10.1080/10426914.2015.107041210.1080/10426914.2015.1070412 ↗
10. Malhotra P · Adv. Mat. Process. Technol · 2021 · 7 · 27
11. 10.3390/mi1110092610.3390/mi11100926 ↗
12. Chow CK · J Nucl Mater · 1988 · 257 · 3510.1016/S0022-3115(98)00436-X ↗
13. Bobkov NV · IOP Conf Series · 2017 · 858 · 01200510.1088/1742-6596/858/1/012005 ↗
14. 10.1177/095440892097673910.1177/0954408920976739 ↗
15. 10.3844/ajeassp.2018.318.32610.3844/ajeassp.2018.318.326 ↗
16. 10.6028/NIST.SP.1200-2210.6028/NIST.SP.1200-22 ↗
17. Rishi Rana N · Int J Eng Res Technol · 2015 · 4 · 247
18. Ehsan KM, Hossain MR, Manzur T, et al. Particle size analysis by image processing technique. In: 1st Bangladesh civil engineering summit, Dhanka: Bangladesh, 23–27November 2016, pp. 124–130.
19. Nandi AK · Mechatronics · 2009 · 19 · 21810.1016/j.mechatronics.2008.08.004 ↗
20. Voskoglou MG · J Math · 2018 · 50 · 85
21. 10.1007/s00170020003410.1007/s001700200034 ↗
22. 10.1016/j.matpr.2020.02.23110.1016/j.matpr.2020.02.231 ↗
23. Bhiradi I · Adv Mat Process Technol · 2020 · 6 · 133
24. Krishnamoorthy A · Measurement · 2012 · 45 · 128610.1016/j.measurement.2012.01.008 ↗
25. 10.1007/s00170-017-0073-z10.1007/s00170-017-0073-z ↗
26. 10.1016/j.jmsy.2012.09.00210.1016/j.jmsy.2012.09.002 ↗
27. 10.3390/met1001009210.3390/met10010092 ↗
28. 10.1007/s00521-020-04909-410.1007/s00521-020-04909-4 ↗
29. 10.1080/2374068X.2020.185539810.1080/2374068X.2020.1855398 ↗
30. 10.1080/10426914.2021.190582910.1080/10426914.2021.1905829 ↗
31. 10.1155/2022/631679910.1155/2022/6316799 ↗
32. Gupta DK · Proc IME E J Process Mech Eng · 2022 · 236 · 217610.1177/09544089221085144 ↗
33. Chow SH · Int J Adv Manuf Technol · 2014 · 71 · 115110.1007/s00170-013-5556-y ↗
34. 10.1016/j.jmatprotec.2004.04.34610.1016/j.jmatprotec.2004.04.346 ↗
35. 10.1080/2374068X.2020.186049810.1080/2374068X.2020.1860498 ↗
36. Schneider CA · Nat. meth · 2012 · 9 · 57110.1038/nmeth.2089 ↗
37. Barreto JCG · Quim Nova · 2019 · 42 · 447
38. Deng J · Syst Control Lett · 1982 · 1 · 28810.1016/S0167-6911(82)80025-X ↗
39. 10.1007/s40092-018-0264-810.1007/s40092-018-0264-8 ↗