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2024 | OriginalPaper | Buchkapitel

Improving Production Rate by Analyzing Wire-Electrical Discharge Machining Parameters and Developing a Prediction Model

verfasst von : S. Suresh, S. Ramesh, Elango Natarajan, Chun Kit Ang, Kanesan Muthusamy, D. Velmurugan

Erschienen in: Artificial Intelligence for Sustainable Energy

Verlag: Springer Nature Singapore

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Abstract

This research aims to investigate the machinability of a SiC-reinforced Al6061-T6 composite by wire-cut electric discharge machining (Wire-EDM) and to develop a predictive model using artificial neural network (ANN). The machine parameters such as current (I), pulse-on time (T_on), pulse-off time (T_off), and wire feed (EF) are examined and optimized for a high material removal rate (MRR). The experiments are designed using Taguchi L₁₆ orthogonal array, and experiments are conducted at room temperature. The MRR obtained at different experiments is analyzed using statistical tools. An ANN model was then developed, and its performance was evaluated by comparing prediction results with experimental results. Visual graphs were used to show the combined impact of Wire-EDM factors on machinability performance. The suggested model reduces the time needed to set the process parameter values, improving production rate and process effectiveness.

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Titel: Improving Production Rate by Analyzing Wire-Electrical Discharge Machining Parameters and Developing a Prediction Model
verfasst von: S. Suresh
S. Ramesh
Elango Natarajan
Chun Kit Ang
Kanesan Muthusamy
D. Velmurugan
Verlag: Springer Nature Singapore
Buch: Artificial Intelligence for Sustainable Energy
Print ISBN: 978-981-9998-32-6

Electronic ISBN: 978-981-9998-33-3

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-981-99-9833-3_24