Product Quality Classification Based on Machine Learning in the Quality Control System of the Laser Metal Deposition Process
DOI:
https://doi.org/10.37253/telcomatics.v10i2.11432Keywords:
Laser Metal Deposition, Random Forest, Decision Tree, Support Vector Machine, Quality ControlAbstract
Industry 4.0 revolutionizes modern manufacturing by enabling the active integration of smart
sensors and machine learning to optimize product quality control systems. This research focuses
on classifying product quality in the Laser Metal Deposition (LMD) process by applying three
machine learning algorithms, namely Decision Tree (DT), Random Forest (RF), and Support
Vector Machine (SVM). The dataset consists of four numerical sensor variables, including
Optical Sensor, Laser Power, Pressure, and Temperature, with Defect Label as the binary target
variable. The Synthetic Minority Oversampling Technique (SMOTE) is used to balance the class
distribution. Correlation analysis reveals weak linear relationships among all variables,
suggesting the presence of complex non-linear interactions. The Random Forest model produces
the best performance with accuracy of 0.88, recall of 0.79, and AUC of 0.80, outperforming
Decision Tree and SVM. These findings indicate that ensemble-based methods effectively
capture complex patterns within sensor data and offer reliable predictions for quality control in
metal manufacturing industries, particularly within Laser Metal Deposition processes.
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