Enhancing Clustering Algorithm with Initial Centroids in Tool Wear Region Recognition

Autonomous manufacturing allows the system to distinguish between a mild, normal and total failure in tool condition. K-means clustering has become the most applied algorithm in discovering classes in an unsupervised scenario. Nevertheless, the algorithm is sensitive to the initial centroids giving various solution every time the system updating. Regular unsupervised K-means is refocused as semi-supervised Fixum K-means. It is embedded with a new tactic to recapture the K value and new initial seedings computation to kick off the system until it converges. Force components of cutting force $${F}_{c}$$ , thrust force $${F}_{t}$$ and perpendicular cutting force $${F}_{cn}$$ were extracted from Neo-MoMac cutting force measurement device. The analysis threshold represents a natural-sorted input vector as Z-rot coefficient (RZ) corresponds to the number of cutting accomplish a strong correlation (R2 = 0.8511) over wear evolution. The clustering system adopted a new calculation of initial centroids has successfully determined the three regions for only a single assignment and achieving the optimal distance squared through eight given data sets. It is conflicting with the standard K-means that return different clustering structure in each run, while K-means +  + replicates several times to achieve minimum objective function. During the course, F-Km delivered robust and consistence clustering results of 85% accuracy over standard K-means and four times converges faster than K-means +  + . The silhouette value average score is 0.8504 (highest score is 0.9207) of how well-distributed the resulting clusters. The clustering system has identified the tool to stop cutting at approximate VB = 0.213 mm before the tool condition enters the failure region of abnormal phase (VB < 0.250 mm).s The proposed system functioned effectively in clustering the data obtained from cutting tests performed within a reasonable range of wear stages. Precision and robustness analysis have proved F-km to score 100% attainment for clustering assignment output and replicability. In contrast, K-means scored 76.3% for precision and ranging from 5 to 33% for robustness. Whereas, K-means +  + scored 33% for robustness and a higher chance of time complexity compared to F-km. F-Km is found to be more accurate, time savvy and robust than standard K-means and K-means +  + . Therefore, the method can be reliably used for observing tool wear state recognition without training and equivocate traditional direct tool wear.