Enhancement in manufacturing systems using Grey-Fuzzy and LK-SVM approach

• Published in: 2021 IEEE International Conference on Intelligent Systems, Smart and Green Technologies (ICISSGT) pp. 72 - 78
• Main Authors: Latchoumi, T.P., Kalusuraman, G., Banu, J. Faritha, Yookesh, T.L., Ezhilarasi, T.P., Balamurugan, K.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-11-2021
• Subjects: AHC; Casting; Correlation; Linear regression; Machining; Mg/SiC; Solid modeling; Solid particle erosion; Solids; Support Vector Machine; Support vector machines; Taguchi-Fuzzy logic
• DOI: 10.1109/ICISSGT52025.2021.00026

Magnesium with 10% of SiC is prepared through the stir casting process. An integrated Taguchi-Fuzzy model for L27 orthogonal array for Airjet machining parameters is developed. Optimum machining condition predicted by Grey Fuzzy Relational Grade (GFRG) is similar to Grey Relational Grade (GRA) and the optimum machining conditions for low Erosion Rate (ER) and Vickers Hardness Number (VHN) are Impact Angle (IA) = 30o, Erodent Velocity (EV) = 90 m/sec and Discharge Rate (DR) = 4 gms/min. For the experimental observations, the regression equation developed from GFRG has a good correlation. Further, an attempt has been to reduce the mathematical complexity (offline mode) by using the advantages of the Linear Kernel Support Vector Machine (LKSVM) method and Agglomerative Hierarchical Clustering-based (AHC) algorithm, L27 Orthogonal Array (OA) observations are classified into 3 classes. Regression equations developed for ER and VHN particularly for class having more observations (Class 2) alone has found to have excellent correlation with experimental observations.