pith. sign in

Integrity report for Artificial Intelligence based tool wear and defect prediction for special purpose milling machinery using low-cost acceleration sensor retrofits

A machine-verified record of the checks Pith has run against this paper: detector runs, findings, signed bundle events, and canonical identifiers.

arXiv:2202.03068 · pith:2022:JXDSLSOC3DBXUVO3UIVPYYX5I4

0Critical
0Advisory
0Detectors run
Last checked

Paper page arXiv integrity.json bundle.json

Detector runs

Findings

No public integrity findings for this paper.

Signed record

The machine-readable record for this paper lives at /pith/JXDSLSOC3DBXUVO3UIVPYYX5I4/integrity.json. Pith Number bundles also include signed pith.integrity.v1 events where a Pith Number exists.