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| Sl. No. | Purpose | Algorithms | Input parameters | Ref. (Year) |
|
| 1 | Predict optimum process parameter for minimum wear ratio and maximum MRR | BpNN, particle swarm optimization, simulated annealing, GA | Pulse current, pulse-on time, pulse-off time | [15] (2015) |
| 2 | Investigations of surface integrity and bio-activity performance | TRMGP | Servo voltage pulse off-time pulse on-time | [16] (2019) |
| 3 | Prediction of surface roughness and MRR | Taguchi, GRA ANNs | Pulse on & off time wire feed rate | [17] (2018) |
|
| Cases of Electrochemical Machining processes using Machining Learning Algorithms |
| 1 | Process parameter optimization for MRR and Ra | LS-SVM, MFNN, Taguchi technique, ANOVA | Flow rate, feed voltage | [18] (2012) |
| 2 | Process parameter optimization for maximizing MRR and minimizing radial overcut | TLBO | Electrolyte concentration, electrolyte flow rate, applied voltage, inter-electrode gap, | [19] (2011) |
|
| Cases of laser machining processes using machining learning algorithms |
| 1 | Process monitoring and control | Convolutional neural networks (CNNs) | Beam translation beam rotation | [20] (2019) |
| 2 | Prediction of surface quality, dimensional features, and productivity | NN, decision trees, K-NN, linear regression | Scanning speed, pulse intensity, pulse frequency | [21] (2015) |
|
| Cases of abrasive water jet machining processes using machining learning algorithms |
| 1 | Surface roughness prediction | Extreme machine learning, ANN, GPR | Cutting speed, material thickness, abrasive flow, measurement position | [22] (2016) |
| 2 | Prediction of process parameters | Adaptive neuro-fuzzy inference system | Jet pressure standoff distance number of shots | [23] (2019) |
| 3 | Surface roughness prediction | Feed-forward BpNN, regression model | Traverse speed, water jet pressure, stand-off distance, abrasive grit | [24] (2008) |
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