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Parametric Optimization of Electro Discharge Process during Machining of Aluminum/Boron Carbide/Graphite Composite

Journal Article
05-15-01-0007
ISSN: 1946-3979, e-ISSN: 1946-3987
Published September 27, 2021 by SAE International in United States
Parametric Optimization of Electro Discharge Process during Machining
                    of Aluminum/Boron Carbide/Graphite Composite
Sector:
Citation: Rizwee, M., Rao, P., and Ahmad, M., "Parametric Optimization of Electro Discharge Process during Machining of Aluminum/Boron Carbide/Graphite Composite," SAE Int. J. Mater. Manf. 15(1):81-89, 2022, https://doi.org/10.4271/05-15-01-0007.
Language: English

Abstract:

The efficiency of the traditional machining process becomes limited because of the mechanical properties and complexity of the geometric shape of the processed materials. This difficulty is resolved through the nonconventional machining process. Electric Discharge Machining (EDM) process is one of the popular nonconventional machining processes among all nonconventional machining processes for processing such materials. The main objective of the present research work is to evaluate the effect of percentage weight fraction of reinforcement and process parameters on machining responses during EDM of aluminum (Al) 7075-reinforced boron carbide (B4C) and graphite metal matrix composite (MMC) and optimization of the result. Servo voltage (SVO), pulse-on time (T ON), pulse current (I), and different weight percentages of B4C and graphite reinforcement in aluminum metal matrix composite (AMMC) are selected as a process variable to study the process responses in terms of tool wear rate (TWR) and radial overcut (ROC). The design of the experiment has been performed through the Taguchi analysis and linear regression mathematical model to develop the mathematical relation between process and response parameter. Validation of experimentation work has been done through the Analysis of Variance (ANOVA). Experimentation results show the role of important process parameters that affect mostly TWR and ROC and imply the combined level of input parameters that optimize the process responses.