Optimization of Process Control Parameters for WEDM of Al-LM25/Fly Ash/B4C Hybrid Composites Using Evolutionary Algorithms: A Comparative Study

Nagarajan, Lenin; Sivakumar, M.; Selvakumar, G.; Rajamani, D.; Sivalingam, Vinothkumar; Gupta, Munish Kumar; Mikołajczyk, Tadeusz; Pimenov, Danil Yurievich

doi:10.3390/met11071105

Cited by 32 publications

(15 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This increases the quality of the surface by decreasing the SR value. Another reason for the decreasing SR is that with an increase in the T off value, the flushing of debris gains more time and it removes the unwanted eroded particles from the machined zone [ 59 , 60 ]. From Figure 4 , by increasing the Al 2 O 3 amount, a decrease in the SR was identified.…”

Section: Resultsmentioning

confidence: 99%

Multi-Response Optimization of Al2O3 Nanopowder-Mixed Wire Electrical Discharge Machining Process Parameters of Nitinol Shape Memory Alloy

et al. 2022

Self Cite

View full text Add to dashboard Cite

Shape memory alloy (SMA), particularly those having a nickel–titanium combination, can memorize and regain original shape after heating. The superior properties of these alloys, such as better corrosion resistance, inherent shape memory effect, better wear resistance, and adequate superelasticity, as well as biocompatibility, make them a preferable alloy to be used in automotive, aerospace, actuators, robotics, medical, and many other engineering fields. Precise machining of such materials requires inputs of intellectual machining approaches, such as wire electrical discharge machining (WEDM). Machining capabilities of the process can further be enhanced by the addition of Al2O3 nanopowder in the dielectric fluid. Selected input machining process parameters include the following: pulse-on time (Ton), pulse-off time (Toff), and Al2O3 nanopowder concentration. Surface roughness (SR), material removal rate (MRR), and recast layer thickness (RLT) were identified as the response variables. In this study, Taguchi’s three levels L9 approach was used to conduct experimental trials. The analysis of variance (ANOVA) technique was implemented to reaffirm the significance and adequacy of the regression model. Al2O3 nanopowder was found to have the highest contributing effect of 76.13% contribution, Ton was found to be the highest contributing factor for SR and RLT having 91.88% and 88.3% contribution, respectively. Single-objective optimization analysis generated the lowest MRR value of 0.3228 g/min (at Ton of 90 µs, Toff of 5 µs, and powder concentration of 2 g/L), the lowest SR value of 3.13 µm, and the lowest RLT value of 10.24 (both responses at Ton of 30 µs, Toff of 25 µs, and powder concentration of 2 g/L). A specific multi-objective Teaching–Learning-Based Optimization (TLBO) algorithm was implemented to generate optimal points which highlight the non-dominant feasible solutions. The least error between predicted and actual values suggests the effectiveness of both the regression model and the TLBO algorithms. Confirmatory trials have shown an extremely close relation which shows the suitability of both the regression model and the TLBO algorithm for the machining of the nanopowder-mixed WEDM process for Nitinol SMA. A considerable reduction in surface defects owing to the addition of Al2O3 powder was observed in surface morphology analysis.

show abstract

Section: Resultsmentioning

confidence: 99%

Multi-Response Optimization of Al2O3 Nanopowder-Mixed Wire Electrical Discharge Machining Process Parameters of Nitinol Shape Memory Alloy

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…The maximum MRR of 0.662 mg/s is obtained at current of 5 A. An increase in current level from low to high increase the breakdown voltage and raise its spark energy [36,37] as given by the Equation ( 6). Due to higher E s , the high current results in melting and vaporization of more volume of material [41,42].…”

Section: The Effect Of Current and Wire Speed On Mrrmentioning

confidence: 99%

“…Considering these factors, an attempt is made in this work to investigate the effects of wire-EDM process parameters such as current, pulse-on and pulse-off time, wire speed and voltage on MRR and optimize these settings to improve the productivity in terms of MRR. Although, artificial neural networkbased optimization methods such as fuzzy AHP-ARAS [34], genetic algorithm [35], particle swarm, moth-flame and Grass-Hooper optimization [36] methods are more attractive tools of optimization for the experiments with large data sets, for limited experimental trails as in this work, Taguchi and response surface [37] methods are more viable methods. Therefore, this works adopts Taguchi method for optimization of MRR and to develop the regression model to predict the MRR.…”

Section: Introductionmentioning

confidence: 97%

Optimization and Modeling of Material Removal Rate in Wire-EDM of Silicon Particle Reinforced Al6061 Composite

et al. 2021

Self Cite

View full text Add to dashboard Cite

The mechanical, physical and interfacial properties of aluminum alloys are improved by reinforcing the silicon carbide particles (SiCp). Machinability of such alloys by traditional methods is challenging due to higher tool wear and surface roughness. The objective of research is to investigate the machinability of SiCp reinforced Al6061 composite by Wire-Electrical Discharge Machining (wire-EDM). The effect of wire-EDM parameters namely current (I), pulse-on time (Ton), wire-speed (Ws), voltage (Iv) and pulse-off time (Toff) on material removal rate (MRR) is investigated and their settings are optimized for achieving the high MRR. The experiments are designed by using Taguchi L16 orthogonal arrays. The MRR obtained at different experiments are analyzed using statistical tools. It is observed that all the chosen process parameters showed significant influence of on the MRR with contribution of 27.39%, 22.08%, 21.32%, 15.76% and 12.94% by I, Iv, Toff, Ton and Ws, respectively. At optimum settings, the Wire-EDM resulted in MRR of 65.21 mg/min and 62.41 mg/min for samples with 4% and 8% SiCp. The results also indicated reinforcing SiCp upto 8% showed marginally low influence on MRR. Microstructural investigation of the cut surface revealed the presence of craters with wave pattern on its surface. The top surface of the crater is featured by the recast layers connecting adjacent craters. Further, the statistical model is developed using linear regression to predict the MRR (?2—73.65%) and its predicting accuracy is verified by the confirmation trials. The statistical model is useful for predicting the MRR for different settings of the process parameters. The optimized settings can be used to improve the machining productivity by increasing the MRR while machining of Al6061-SiCp (upto 8 wt. %) alloy by wire-EDM industries.

show abstract

“…These optimization techniques work better with smaller data sets. 31 In comparison, metaheuristics techniques like Grass-Hooper optimization, 32 teaching-learning based optimization 33 are more suitable for the more extensive data set.…”

Section: Fadwsmentioning

confidence: 99%

Investigation and multi-objective optimization of monocrystalline silicon wafering using wire electro-discharge machining

Verma

Singh

2022

Proceedings of the Institution of Mechanical Engineers, Part C:

View full text Add to dashboard Cite

Monocrystalline silicon wafers are widely used as the primary material for solar cell production in the photovoltaic industry, owing to their high efficiency and sleeker aesthetic. Wafering is the primary process towards wafer production. The existing wafering processes have limitations of cracking, chipping, and high kerf loss, arising the need for cost-efficient and precise methods to produce wafers. In the present experimental work, an attempt has been made to explore the potential of Wire Electro-discharge Machining (WEDM) for wafer production and to gain an understanding of the effects caused by pulse on time (Ton), pulse off time (Toff), peak current (Ip), wire tension (WT), and wire feed rate (WFR) during the wafering process to achieve the optimal parametric setting for attaining maximum wafering speed and minimum surface roughness. The work material used was P-type monocrystalline silicon. Wafers thickness of 250 μm was produced, avoiding edge chipping. The experimentation was planned according to the face-centered central composite design. The obtained results were statistically analyzed, and response surface methodology (RSM) was used to model the wafering speed and surface roughness. From the analysis of experimental results, it was noticed that at the higher level of Ton and Ip, a maximum wafering speed of 2.574 mm/min was attained. In contrast, the minimum surface roughness of 1.57 μm was achieved at the lower level of Ton and Ip. The most significant process variables that influence the wafering speed and surface roughness are the Ton, Toff, and Ip. Micrographs of the wafer surface reveal the presence of micro craters, but there was no evidence of micro-cracks. The multi-objective optimization of the responses was done using the desirability approach, and the optimized values of wafering speed and surface roughness attained were 0.989 mm/min and 1.57 μm, respectively.

show abstract

Optimization of Process Control Parameters for WEDM of Al-LM25/Fly Ash/B4C Hybrid Composites Using Evolutionary Algorithms: A Comparative Study

Cited by 32 publications

References 28 publications

Multi-Response Optimization of Al2O3 Nanopowder-Mixed Wire Electrical Discharge Machining Process Parameters of Nitinol Shape Memory Alloy

Multi-Response Optimization of Al2O3 Nanopowder-Mixed Wire Electrical Discharge Machining Process Parameters of Nitinol Shape Memory Alloy

Optimization and Modeling of Material Removal Rate in Wire-EDM of Silicon Particle Reinforced Al6061 Composite

Investigation and multi-objective optimization of monocrystalline silicon wafering using wire electro-discharge machining

Contact Info

Product

Resources

About