September 11, 2019 Dataset Open Access

Christos Tachtatzis; Gordon Gourlay; Ivan Andonovic; Omer Panni

DCAT Export

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    <dct:title>Sensor data set radial forging at AFRC testbed v2</dct:title>
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    <dcat:keyword>forming, forge, sensors, dynamic measurement, measurement uncertainty, sensor network, digital sensors, MEMS, machine learning</dcat:keyword>
    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2019-09-11</dct:issued>
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    <dct:description>&lt;p&gt;&lt;strong&gt;Sensor data set, radial forging at AFRC testbed&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;&lt;strong&gt;General information on the data set&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;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 &amp;deg;C.&lt;/p&gt; &lt;p&gt;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.&lt;/p&gt; &lt;p&gt;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.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Structure of the data&lt;/strong&gt;&lt;/p&gt; &lt;ul&gt; &lt;li&gt;The sensor readings for the forging of the parts are provided in 81 csv files in the folder &amp;ldquo;Scope Traces&amp;rdquo;, named &amp;ldquo;Scope0001.csv&amp;rdquo; to &amp;ldquo;Scope0081.csv&amp;rdquo;. Each file contains the readings (columns) against time (rows). The first column displays the clock times (in milliseconds).&lt;/li&gt; &lt;li&gt;A commentary on the sensors is provided in the file &amp;ldquo;ForgedPartDataStructureSummaryv3.xlsx&amp;rdquo; &lt;strong&gt;(NOTE: Some columns do not have sensor descriptions as this information is not available).&lt;/strong&gt;&lt;/li&gt; &lt;li&gt;The CMM data is provided in the file &amp;ldquo;CMMData.xlsx&amp;rdquo;.&lt;/li&gt; &lt;/ul&gt; &lt;p&gt;&lt;strong&gt;Further Information&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;For an introduction and tutorial to this data, a set of Jupyter notebooks is available here:&lt;/p&gt; &lt;p&gt;&lt;a href="https://github.com/harislulic/Strathcylde_AFRC_machine_learning_tutorials/releases/tag/v2.0"&gt;https://github.com/harislulic/Strathcylde_AFRC_machine_learning_tutorials/releases/tag/v2.0&lt;/a&gt;&lt;/p&gt; &lt;p&gt;These notebooks contain Python code and a documentation of example machine learning tasks and analysis of this data set.&lt;/p&gt;</dct:description>
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