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Sensor data set radial forging at AFRC testbed v2

Christos Tachtatzis; Gordon Gourlay; Ivan Andonovic; Omer Panni


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{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.3405265", 
  "language": "eng", 
  "title": "Sensor data set radial forging at AFRC testbed v2", 
  "issued": {
    "date-parts": [
      [
        2019, 
        9, 
        11
      ]
    ]
  }, 
  "abstract": "<p><strong>Sensor data set, radial forging at AFRC testbed</strong></p>\n\n<p><strong>General information on the data set</strong></p>\n\n<p>Radial forging is widely used in industry to manufacture components for a broad range of sectors including automotive, medical, aerospace, rail and industrial. The Advanced Forming Research Centre (AFRC) at the University of Strathclyde, Glasgow, houses a GFM SKK10/R radial forge that has been used as a testbed for this project. Using two pairs of hammers operating at 1200 strokes/min, and providing a maximum forging force per hammer of 150 tons, the radial forge is capable of processing a range of metals, including steel, titanium and inconel. Both hollow and solid material can be formed with the added benefit of creating internal features on hollow parts using a mandrel. Parts can be formed at a range of temperatures from ambient temperature to 1200 &deg;C.</p>\n\n<p>For the provided data set, a total of 81 parts were forged over one day of operation. A machine failure occurred during the forging of part number 70, and this part was re-run once the malfunction had been fixed. Each forged part was then measured using a CMM to provide dimensional output relative to a target specification and tolerances. The CMM records 18 dimensional measurements.</p>\n\n<p>The aim of the measurement setup is to predict the quality (in terms of dimensional properties) of the forged part from the sensor measurements during the forging process.</p>\n\n<p><strong>Structure of the data</strong></p>\n\n<ul>\n\t<li>The sensor readings for the forging of the parts are provided in 81 csv files in the folder &ldquo;Scope Traces&rdquo;, named &ldquo;Scope0001.csv&rdquo; to &ldquo;Scope0081.csv&rdquo;. Each file contains the readings (columns) against time (rows). The first column displays the clock times (in milliseconds).</li>\n\t<li>A commentary on the sensors is provided in the file &ldquo;ForgedPartDataStructureSummaryv3.xlsx&rdquo; <strong>(NOTE: Some columns do not have sensor descriptions as this information is not available).</strong></li>\n\t<li>The CMM data is provided in the file &ldquo;CMMData.xlsx&rdquo;.</li>\n</ul>\n\n<p><strong>Further Information</strong></p>\n\n<p>For an introduction and tutorial to this data, a set of Jupyter notebooks is available here:</p>\n\n<p><a href=\"https://github.com/harislulic/Strathcylde_AFRC_machine_learning_tutorials/releases/tag/v2.0\">https://github.com/harislulic/Strathcylde_AFRC_machine_learning_tutorials/releases/tag/v2.0</a></p>\n\n<p>These notebooks contain Python code and a documentation of example machine learning tasks and analysis of this data set.</p>", 
  "author": [
    {
      "family": "Christos Tachtatzis"
    }, 
    {
      "family": "Gordon Gourlay"
    }, 
    {
      "family": "Ivan Andonovic"
    }, 
    {
      "family": "Omer Panni"
    }
  ], 
  "version": "1.0", 
  "type": "dataset", 
  "id": "3405265"
}
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