Implementation of robotic electrochemical machining in freeform surface machining with material removal rate prediction using different machine learning algorithms

Robots are used as machine tools via some advanced properties such as versatility, adaptation and mobility with other control systems. These advantages can be the solution to overcome the encountered problems in electrochemical machining (ECM). In this manner, the usability of a new machining method...

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Bibliographic Details
Published in:Proceedings of the Institution of Mechanical Engineers. Part C, Journal of mechanical engineering science Journal of mechanical engineering science, 2024-05, Vol.238 (9), p.3835-3849
Main Authors: Cebi, Abdulkadir, Demirtas, Hasan, Kaleli, Alirıza
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Electric potential
Electrochemical machining
Electrolytes
Feed rate
Free form
Gaussian process
Machine learning
Machine tools
Material removal rate (machining)
Mathematical models
Nickel base alloys
Parameters
Regression analysis
Robot learning
Superalloys
Surface roughness
Voltage
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Summary:Robots are used as machine tools via some advanced properties such as versatility, adaptation and mobility with other control systems. These advantages can be the solution to overcome the encountered problems in electrochemical machining (ECM). In this manner, the usability of a new machining method that is Robotic electrochemical machining (RECM) in freeform surface machining is investigated. Firstly, ECM parameters and their effects on the material removal rate (MRR) is discussed and compared with the literature. Experimental results showed that MRR variation for RECM acts similar characteristics with conventional ECM processes. The increase in voltage, electrolyte conductivity and feed rate increase MRR. The highest effect on MRR for both materials is voltage, electrolyte conductivity, and feed rate, respectively. Secondly, three different machine learning models are designed as Gaussian Process Regression (GPR), regression tree and deep neural network (DNN) models and the relationships between their actual and predicted values are discussed. The performance of the DNN model is better than other models with a correlation of determination values equal for Inconel 718 and AISI 1040. Furthermore, the DNN model with four input parameters outperforms developed models such as GPR and DTR with optimized hyperparameters regarding the R2, MSE, RMSE, and MAE. The change in height in the Z-axis and the surface roughness of the machined surfaces are also measured. The results show that the decrease in the Z axis is homogeneous, that is, uniform machining occurs and also the surface roughness has improved. Therefore, RECM can be a solution to machine free-form surfaces for different materials with high predictability.
ISSN:0954-4062
2041-2983
DOI:10.1177/09544062231208302