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| dc.contributor.author | YITAYAL, BELEW SIYOUM | |
| dc.date.accessioned | 2022-12-31T08:27:44Z | |
| dc.date.available | 2022-12-31T08:27:44Z | |
| dc.date.issued | 2022-09-20 | |
| dc.identifier.uri | http://ir.bdu.edu.et/handle/123456789/14822 | |
| dc.description.abstract | Bioimplant materials such as stainless steel 316L are gaining widespread attention because of their importance in leg bone fracture repair. Thus, the rate of failure and loss of implants is undesirably high and leaves space for improvements. Thus, the developing acceptable surface roughness, recast layer thickness without affecting production rate was intended. However, because of the versatile properties of stainless steel 316L, non-conventional machining methods were implemented to machine the material. Therefore, in the current study, minimum surface roughness and recast layer of implant was obtained through machining using wire electric discharge machine. Taguchi’s L18 orthogonal array was implemented to perform the experimental design. The experiment was conducted using molybdenum wire as an electrode and water as a dielectric fluid. After machining the sample, the SEM and Zeta 20 were carried out to study response outputs. The minimum surface roughness was 1.276 µm, and the minimum recast layer thickness of 9.5 µm. The ANN modeling was used to analyze the performance of the experimental results, and a performance value of SR 99.99%, RLT 99.99%, and CS 99.97% confidence levels were obtained, indicating that the ANN model shows a good fit and a strong correlation between selected parameters. The multi-objective Jaya algorithm was implemented for optimization, and the optimal sets of design variables were found as a pulse on time 5µs, pulse off time 5 µs, servo gap voltage 58 V, peak current 2 A, and wire feed rate 10 mm/s. From the confirmatory experiments, the percentages of error for CS, Ra, and RLT were 0.001%, 2.95%, and 2.79%, respectively, which is less than the acceptance limit of 3%. The surface morphology of the machined sample obtained at lower discharge energy showed a reduction in microcracks, micropores, and globules in comparison with the machined surface obtained at a high discharge energy level. The optimum process parameter can be applied on real material of implant and the minimum implant surface roughness and recast layer thickness was applied for real implants in medical filed. Keywords: Bio implant material, Stainless steel 316L, Wire EDM parameters, Surface Roughness, artificial neural network, Multi-objective Jaya Algoris | en_US |
| dc.language.iso | en_US | en_US |
| dc.subject | MECHANICAL AND INDUSTRIAL ENGINEERING | en_US |
| dc.title | Parameter optimization of wire electric discharge machining for stainless steel 316L bioimplant using an artificial neural network based on multi-objective Jaya algorithm (Case Study in Bahir Dar Felege Hiwot Referral Hospital, Bahir Dar Ethiopia) | en_US |
| dc.type | Thesis | en_US |
