Processing math: 100%
Skip main navigation

Cookies Notification

We use cookies on this site to enhance your user experience. By continuing to browse the site, you consent to the use of our cookies. Learn More
×

System Upgrade on Tue, May 28th, 2024 at 2am (EDT)

Existing users will be able to log into the site and access content. However, E-commerce and registration of new users may not be available for up to 12 hours.
For online purchase, please visit us again. Contact us at customercare@wspc.com for any enquiries.

SEARCH GUIDE  Download Search Tip PDF File

Bestsellers

Classical and Computational Solid Mechanics
Classical and Computational Solid Mechanics

2nd Edition
by Y C Fung, Pin Tong and Xiaohong Chen
Introduction to Micromechanics and Nanomechanics
Introduction to Micromechanics and Nanomechanics

2nd Edition
by Shaofan Li and Gang Wang
Practical Railway Engineering
Practical Railway Engineering

2nd Edition
by Clifford F Bonnett

 

  • articleNo Access

    Neural Network (NN)-Based RSM-PSO Multiresponse Parametric Optimization of the Electro Chemical Discharge Micromachining Process During Microchannel Cutting on Silica Glass

    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 (μs), and stand-off distance (SOD) during microchannel cutting on silica glass (SiO2+NaSiO3). 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.

  • chapterNo Access

    Contrastive Analysis of Surface Wetting Characteristics of Stainless Steel, Platinum Sheet and Monocrystalline Silicon

    This paper inspects wetting rules of normal acid, alkanol and alkane on surfaces of stainless steel, platinum sheet and monocrystalline silicon and contrasts the influence on wetting effect by polarity difference. Experimental results show that for alkane not containing functional groups, the increasing of carbon number is beneficial to the enhancement of wetting effect; optimum wetting reagents for different surface present obvious selectivity, and for alkane, the wetting effect of stainless steel, platinum sheet and monocrystalline silicon is from good to bag in sequence. For organic alcohol and organic acid, the sequence is monocrystalline silicon, platinum sheet and stainless steel. Organic alcohol and organic acid are monocrystalline silicon, platinum sheet and stainless steel. Liquid solid interface polarity contrast shows that certain wetting difference is beneficial to the enhancement of wetting effect.