التفاصيل البيبلوغرافية
| العنوان: |
Neural Network (NN)-Based RSM-PSO Multiresponse Parametric Optimization of the Electro Chemical Discharge Micromachining Process During Microchannel Cutting on Silica Glass. |
| المؤلفون: |
Bellubbi, Sadashiv, Mallick, Bijan, Hameed, Azzam Sabah, Dutta, Pijush, Sarkar, Manoj Kumar, Nanjundaswamy, Sathisha |
| المصدر: |
Journal of Advanced Manufacturing Systems; Dec2022, Vol. 21 Issue 4, p869-897, 29p |
| مصطلحات موضوعية: |
FUSED silica, MICROMACHINING, MARINE debris, PARTICLE swarm optimization, TUNGSTEN carbide, ELECTRIC machines, CARBONACEOUS aerosols, ELECTRIC metal-cutting |
| مستخلص: |
The production of miniature parts by the electrochemical discharge micromachining process (μ -ECDM) draws the most of attractions into the industrial field. Parametric influences on machining depth (MD), material removal rate (MRR), and overcut (OC) have been propounded using a mixed electrolyte (NaOH:KOH- 1:1) varying concentrations (wt.%), applied voltage (V), pulse on time (μ s), and stand-off distance (SOD) during microchannel cutting on silica glass (SiO 2 + NaSiO 3 ). Analysis of variances has been analyzed to test the adequacy of the developed mathematical model and multiresponse optimization has been performed to find out maximum MD with higher material removal at lower OC using desirability function analysis as well as neural network (NN)-based Particle Swarm Optimization (PSO). The SEM analysis has been done to find unexpected debris. MD has been improved with better surface quality using a mixed electrolyte at straight polarity using a tungsten carbide (WC) cylindrical tool along with X , Y , and Z axis movement by computer-aided subsystem and combining with the automated spring feed mechanism. PSO-ANN provides better parametric optimization results for micromachining by the ECDM process. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
