Experimental Investigation and Optimization by Evaluation Based on Distance from Average Solution Approach for Wire Electrical Discharge Machining of Super Duplex Stainless Steels

D, Jafrey Daniel James; Ramakrishnan, H.; Pandiyan, G. Karthik; Bharath, M.; Gokul, P.; Akash, G.; Arun, R.

doi:10.1007/s11665-023-08052-8

Cited by 8 publications

(1 citation statement)

References 21 publications

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“…Optimization of process parameters in any machining process is crucial for achieving the desired precision, surface finish, and material removal rate. Various methods have been employed to optimize these parameters, each with its own strengths and limitations [26,27]. Taguchi's DOE is known for its systematic approach to experimentation, allowing for the simultaneous study of multiple factors with minimal experimental runs.…”

Section: Introductionmentioning

confidence: 99%

Design of experiments integrated with neural networks for optimization and predictive modelling of electrode wear of novel Ti-6Al-4V-SiCp composites during die sinking electric discharge machining

Hegde,

Shetty,

Nayak

et al. 2024

Mater. Res. Express

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Electric Discharge Machining (EDM) is a widely used manufacturing process for shaping hard and electrically conductive materials. This study investigates the effects of various electrode materials such as, Ti-6Al-4V-SiCp, Brass and Copper on the machining performance of AISI 316L Stainless Steel workpieces using EDM. The methodology involved manipulating parameters such as Electrode Material, Discharge Current, Gap Voltage, Spark Gap, Pulse-on Time, and Pulse-off Time. From the extensive experimantation it was observed that the combination of Ti-6Al-4V-SiCp electrode material, 8Amp Discharge Current, 90V Gap Voltage, 75µm Spark Gap, 100µs Pulse-on Time and 15µs Pulse-off Time has resulted in lowest Electrode Wear Rate, Machining Time, and Electrode Surface Roughness Ratio. Ti-6Al-4V-SiCp electrodes possess higher hardness and electrical conductivity compared to Brass and Copper Electrodes leading to higher wear resistance against repeated thermal shocks during electric discharge machining operation. Feed Forward Artificial Neural Network is successfully applied to predict the output characteristics of the experimentation with high accuracy of 98.3% (Electrode Wear Rate), 94.6% (Machining Time) and 93.8% (Electrode Surface Roughness Ratio). Further, microstructure analysis concludes that lowest wear is observed in Ti-6Al-4V-SiCp electrodes compared to Brass and Copper electrodes.

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Section: Introductionmentioning

confidence: 99%