{
  "annotation_id": "7269c7e5-9fc2-4c33-a2fe-f9a434ade513",
  "date_created": "2026-03-02T18:00:50.492000Z",
  "date_modified": "2026-03-02T18:00:50.492000Z",
  "file_hash": "3366ca6cb9e46d657320fac09ed83e1996d7d3d2945a019a9d82bf48add373f7",
  "private": false,
  "record": {
    "abstract": "Acoustic Emission (AE) data from single point turning machining are analysed\nin this paper in order to gain a greater insight of the signal statistical\nproperties for Tool Condition Monitoring (TCM) applications. A statistical\nanalysis of the time series data amplitude and root mean square (RMS) value at\nvarious tool wear levels are performed, \u0026#65533;nding that ageing features can\nbe revealed in all cases from the observed experimental histograms. In\nparticular, AE data amplitudes are shown to be distributed with a power-law\nbehaviour above a cross-over value. An analytic model for the RMS values\nprobability density function (pdf) is obtained resorting to the Jaynes\u0027 maximum\nentropy principle (MEp); novel technique of constraining the modelling function\nunder few fractional moments, instead of a greater amount of ordinary moments,\nleads to well-tailored functions for experimental histograms.",
    "arxiv_id": "physics/0404128",
    "authors": [
      "F. A. Farrelly",
      "A. Petri",
      "L. Pitolli",
      "G. Pontuale",
      "A. Tagliani",
      "P. L. Novi Inverardi"
    ],
    "categories": [
      "physics.data-an",
      "cond-mat.mtrl-sci",
      "physics.gen-ph"
    ],
    "doi": "10.1121/1.1764831",
    "journal_ref": "J. Acoust. Soc. Am. 116, 981-986 (2004).",
    "title": "Statistical properties of acoustic emission signals from metal cutting   processes",
    "url": "https://arxiv.org/abs/physics/0404128"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "8b2bdb3b-fac4-4cb5-a01a-2a9d803eae01",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}