A Hybrid Approach for Predicting Critical Machining Conditions in Titanium Alloy Slot Milling Using Feature Selection and Binary Whale Optimization Algorithm

Monitoring the machining process is crucial for providing cost-effective, high-quality production and preventing unwanted accidents. This study aims to predict critical machining conditions related to surface roughness and tool breakage in titanium alloy slot milling. The Siemens SINUMERIK EDGE (SE)...

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Vydané v:Machines (Basel) Ročník 11; číslo 8; s. 835
Hlavní autori: Rahmani, Amirsajjad, Hojati, Faramarz, Hadad, Mohammadjafar, Azarhoushang, Bahman
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.08.2023
Predmet:
Algorithms
Analysis
Artificial intelligence
binary whale optimization algorithm
Cetacea
feature selection
Fourier transforms
Machine tools
Machining
Machinists' tools
Mathematical optimization
milling
Model testing
Neural networks
Optimization
Sensors
Slot milling
Specialty metals industry
Support vector machines
Surface roughness
Ti6Al4V
Titanium alloys
Titanium base alloys
tool monitoring
Workpieces
ISSN:2075-1702, 2075-1702
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Shrnutí:Monitoring the machining process is crucial for providing cost-effective, high-quality production and preventing unwanted accidents. This study aims to predict critical machining conditions related to surface roughness and tool breakage in titanium alloy slot milling. The Siemens SINUMERIK EDGE (SE) Box system collects signals from the spindle and axes of a CNC machine tool. In this study, features were extracted from signals in time, frequency, and time–frequency domains. The t-test and the binary whale optimization algorithm (BWOA) were applied to choose the best features and train the support vector machine (SVM) model with validation and training data. The SVM hyperparameters were optimized simultaneously with feature selection, and the model was tested with test data. The proposed model accurately predicted critical machining conditions for unbalanced datasets. The classification model indicates an average recall, precision, and accuracy of 80%, 86%, and 95%, respectively, when predicting workpiece quality and tool breakage.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2075-1702
2075-1702
DOI:10.3390/machines11080835