FASTENER CLUSTER (K-MEANS)

Groups fasteners into k clusters using k-means with k-means++ initialization on selected numeric features (default: peak_axial_kN, peak_shear_kN, fos_min). Returns per-fastener cluster labels + per-cluster summary (count, centroid in real units, sample IDs). Useful for identifying ‘families’ of similarly-loaded joints in large datasets (e.g. cluster the BiW bolts that all see similar crash load profiles, separate from the few outliers driving failures). Standalone in-PHP implementation — no external ML dependencies. Pipe master_table from lsdyna_fastener_failure_analysis or lsdyna_joint_map_batch_analysis directly into this worker.

When to use

Tagged: cluster, fastener, fos, grouping, k-means, kmeans, ml, outlier.

Inputs

Label ID Type Default Required Description
Master Table master_table dataset   Per-fastener row dataset from a previous fastener-analysis worker.
Number of Clusters (k) num_clusters integer 4   Target cluster count (2-20 typical).
Features (CSV) features string peak_axial_kN,peak_shear_kN,fos_min   Comma-separated numeric column names from master_table to cluster on.
Max Iterations max_iterations integer 50   k-means iteration cap (converges early on most datasets).
Random Seed random_seed integer 42   Seed for reproducible k-means++ initialization.

Outputs

Label ID Type Description
Cluster Labels labels dataset Per-fastener: id + cluster integer (0 .. k-1).
Cluster Summary cluster_summary dataset Per-cluster: count, real-unit centroid for each feature, z-score centroid, up to 10 sample IDs.
Meta meta dataset Run metadata — k, features used, iterations, points used/skipped, feature mean/std stats.

Disciplines

  • engineering.fastener.analysis
  • ml.unsupervised.clustering

Auto-generated from platform schema. Worker id: fastener_cluster. Schema hash: 39417225968e. Hand-curated docs in workerexamples/ override this page when present.