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.

自主制造使系统能够区分工具状况中的轻微，正常和完全故障。K-means聚类已成为在无人监督的情况下发现类的最常用算法。尽管如此，该算法对每次系统更新时给出各种解决方案的初始质心都很敏感。常规无监督K均值重新定位为半监督Fixum K均值。它嵌入了一种新的策略，可以重新捕获K值，并且可以使用新的初始种子计算来启动系统，直到收敛为止。切削力\（{F} _ {c} \），推力\（{F} _ {t} \）和垂直切削力\（{F} _ {cn} \）的力分量从Neo-MoMac切削力测量设备中提取。分析阈值表示自然排序的输入向量，因为Z旋转系数（^R Z）对应于完成切削次数的强相关性（R ² = 0.8511）。采用新的初始质心计算的聚类系统仅通过一次分配就成功确定了三个区域，并通过八个给定的数据集实现了最佳距离平方。它与在每次运行中返回不同聚类结构的标准K均值相冲突，而K均值++重复多次以实现最小目标功能。在此过程中，F-Km提供的鲁棒性和一致性聚类结果优于标准K-means的85％，并且收敛速度比K-means + +快四倍。轮廓值平均分数是所得聚类分布得很好的0.8504（最高分数是0.9207）。聚类系统已识别出可在大约VB  = 0.213  mm处停止切削的工具在工具状态进入异常相的故障区域之前（VB  <0.250  mm）.s拟议的系统有效地聚集了在合理的磨损阶段范围内进行的切削测试获得的数据。精度和鲁棒性分析已证明F-km在聚类分配输出和可复制性方面得分达到100％。相比之下，K均值的精度得分为76.3％，鲁棒性为5％至33％。相比于F-km，K均值+ +的鲁棒性和时间复杂度的机会得分为33％。发现F-Km比标准K-means和K-means ++更准确，省时且健壮。因此，该方法可以可靠地用于观察工具磨损状态识别，而无需训练并消除传统的直接工具磨损。