Modeling and parametric optimization of electrical discharge machining on casted composite using central composite design

被引:13
作者
Gugulothu, Bhiksha [1 ]
Bharadwaja, K. [2 ]
Vijayakumar, S. [3 ]
Rao, T. V. Janardhana [4 ]
Sri, M. Naga Swapna [5 ]
Anusha, P. [5 ]
Agrawal, Manoj Kumar [6 ]
机构
[1] Bule Hora Univ, Dept Mech Engn, Post Box 144, Bule Hora, Ethiopia
[2] Mallareddy Engn Coll A, Dept Mech Engn, Hyderabad, India
[3] BVC Engn Coll Autonomous, Dept Mech Engn, Odalarevu 533210, Andhra Pradesh, India
[4] BVC Engn Coll Autonomous, Dept Elect & Commun Engn, Odalarevu 533210, Andhra Pradesh, India
[5] PVP Siddhartha Inst Technol, Dept Mech Engn, Vijayawada, India
[6] GLA Univ, Dept Mech Engn, Mathura 281406, UP, India
来源
INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM | 2024年 / 18卷 / 05期
关键词
MRR; ANOVA; EDM process; CCD technique; MMC; MECHANICAL-PROPERTIES; EDM;
D O I
10.1007/s12008-023-01323-7
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
MMCs are broadly used in several industrial applications because of their excellent mechanical properties. Hard materials and composites cannot be machined by typical conventional methods. so nonconventional machining methods such as Electrical discharge machining are mostly used. This work focuses to optimize the process variables of EDM of Al5456/SiC/Flyash hybrid composites. The impact of three most significant variables including pulse-off time, current and pulse-on time are examined. Central Composite Design (CCD) based RSM is applied for experimentation. The development model for material removal rate (MRR) and surface roughness (SR) are investigated by using CCD technique. ANOVA is implemented to identify the most significant variables and their output response of MRR and SR. From the validation result, the maximum values of MRR and SR are identified as 0.783 mm(3)/min and 13.26 mu m, whereas minimum values are 0.249 mm(3)/min and 8.24 mu m. ANOVA results show that current and pulse OFF time are highly significant variables to increase MRR and SR. The development of a regression model and 3D/Contour are made to predict and evaluate the Material Removal Surface and Surface roughness.
引用
收藏
页码:2793 / 2803
页数:11
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