4.5 Article

Machinability analysis on wire electrical discharge machining of stir casted AA2024/Al2O3/BN hybrid composite for aerospace applications

Journal

MATERIALS AND MANUFACTURING PROCESSES
Volume 36, Issue 6, Pages 730-743

Publisher

TAYLOR & FRANCIS INC
DOI: 10.1080/10426914.2020.1854466

Keywords

Hybrid; aluminum; MMC; composites; machinability; material removal; orientation tolerance; ANFIS

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Metal Matrix Composite (MMC) materials have enhanced characteristics and are considered as an alternate material for engineering applications. Wire Electrical Discharge Machining (WEDM) is effective for machining harder materials like MMC. Analysis of WEDM machinability of Hybrid MMC and development of Grey-ANFIS model for predicting performance measures were conducted. Comparing experimental outcomes with predicted values proved the competency of the developed model.
Metal Matrix Composite (MMC) materials are having enhanced characteristics and considered as an alternate material for various engineering applications. Due to reinforcement added, the materials become harder which results in poor machining performance by conventional machining techniques. Wire Electrical Discharge Machining (WEDM) is an effective method for making complex shapes in harder material that are electrically conductive. In this present exploration, an analysis has been performed on machinability of WEDM of Hybrid MMC (AA2024+Al2O3+BN) which is prepared by stir cast process and development of artificial intelligent decision-making tool for WEDM process. The ascendancy of variables namely duration of pulse on, pulse off and applied current in contrast to preferred performance parameters such as material removal rate, surface roughness, dimensional deviation, and orientation tolerance error were investigated. Grey theory has been engaged for attaining gray relational coefficient values and considered as an input information to develop the Grey-ANFIS model for predicting the desired performance measures. An analysis has been performed by comparing the experimentation outcomes and predicted values. Also the performance of developed Grey-ANFIS model is analyzed to disclose the competency and it is proved that the model is capable of predicting the desired performance measures in an effective manner.

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