Experimental investigations and its dimensional analysis–based modeling of the UAECDM process

The ultrasonic-assisted electrochemical discharge machining (UAECDM) process uses thermal, chemical, and ultrasonic energy together during the machining process. The energy produced from the discharge is used in the work material to produce micro-feature, but the entire energy is not used solely for...

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Bibliographic Details
Published in:International journal of advanced manufacturing technology 2020-12, Vol.111 (11-12), p.3241-3257
Main Authors: Rathore, Ranjeet Singh, Dvivedi, Akshay
Format: Article
Language:English
Subjects:
CAE) and Design
Computer-Aided Engineering (CAD
Dimensional analysis
Discharge
Engineering
Industrial and Production Engineering
Material removal rate (machining)
Mathematical models
Mechanical Engineering
Media Management
Original Article
Physical properties
Process parameters
Tool wear
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Summary:The ultrasonic-assisted electrochemical discharge machining (UAECDM) process uses thermal, chemical, and ultrasonic energy together during the machining process. The energy produced from the discharge is used in the work material to produce micro-feature, but the entire energy is not used solely for material removal. Hence, it also affects the tool electrode wear (TEW) along with the material removal rate (MRR). An attempt has been made in this article to investigate the effect of process parameters related to the UAECDM process performance. From the experimental results, it was found that the ultrasonic vibrational amplitude influences the MRR and TEW most compared to other process parameters such as pulse on time ( T on ), electrolyte concentration, and applied voltage. In addition, mathematical models are developed using dimensional analysis to predict TEW and MRR. It is based on process parameters affecting TEW and MRR, including the tool electrode and work material’s thermal-physical properties. The results obtained from the mathematical models are quite similar to experimental results, and it has been found that the models can be used for further process’s effects on performance characteristics.
ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-020-06320-8