Automatic Assessment of Tool Wear Condition During Milling of Laminated MDF Board Using the EVA-02 Classifier

The main objective of this study was to conduct initial evaluations of the performance of a classifier utilizing the EVA-02 model, which was designed to identify tool wear states by analyzing scanned images of machined, melamine-faced MDF workpieces produced under varying tool degradation levels. Tool wear was quantified using the flank wear indicator VB and grouped into four wear classes (Class I: VB = 0–0.1 mm, Class II: VB = 0.1–0.2 mm, Class III: VB = 0.2–0.3 mm, Class IV: VB = 0.3–0.4 mm). The classifier automatically analyzed the delamination level observed on the edges of the workpieces to predict the corresponding tool wear class. To increase the number of training samples and capture local manifestations of edge damage, each edge image was divided into 28 overlapping image windows of size 250 × 250 pixels. A few different data aggregation strategies were investigated. The most accurately recognized tool wear class was Class IV (Precision 89%; Recall 93% and F1-score 91%—rounded to whole numbers). However, the classification metrics for the remaining classes were considerably lower. For instance, the F1-score for each of them fell well below 80%. Most notably, the recall for class III was particularly poor, reaching a mere 65%. The overall accuracy, averaged across all four classes, was 79%. This result is comparable to previous research reports and can be considered promising, but it does not represent a major breakthrough.