Micro-Facial Expression Recognition in Video Based on Optimal Convolutional Neural Network (MFEOCNN) Algorithm
S.D. Lalitha1, K.K. Thyagharajan2

1S.D. Lalitha, Ph.D. Student, Anna University Chennai.
2Dr. K.K. Thyagharajan, R.M.D Engineering College, Chennai.
Manuscript received on September 22, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 2832-2841 | Volume-9 Issue-1, October 2019 | Retrieval Number: A9802109119/2019©BEIESP | DOI: 10.35940/ijeat.A9802.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: This work elaborates the experimental work on Material Removal Rate (MRR) and Surface Roughness (SR) output conditions of Wire-Electrical Discharge Machining (WEDM) and finding the optimal input conditions through Taguchi method coupled with Grey relational analysis for lower SR and higher MRR during profile cutting of high strength D3 tool steel, which is the need of the hour in industries. The machining factors considered for investigation which influences MRR and SR were: cutting speed, pulse on-time and off-time, input current, wire tension and feed and servo feed and voltage. A L18 orthogonal array was considered for mixed-level experimental design through Taguchi’s approach and for multi-criteria optimization Grey Relational Analysis was applied. Outcome shows that SR increases with the increase of pulse on-time and decreases with increase in pulse off-time and MRR increases as the pulse on-time increases due to longer spark duration. Both SR and MRR are well within the control limits and servo voltage is the most influential parameter contributing by 48.48%, followed by wire feed rate, input current and servo feed rate with an R2 value of 95.85%, identified through Analysis of Variance (ANOVA). With obtained optimum conditions, a validation experiment was conducted to authenticate the results, which indicates a worthy agreement with predicted output characteristics.
Keywords: D3 tool steel, Material Removal Rate, Taguchi’s Methodology, Grey Relational Analysis.