Dataset Open Access
Christos Tachtatzis; Gordon Gourlay; Ivan Andonovic; Omer Panni
<?xml version='1.0' encoding='UTF-8'?> <record xmlns="http://www.loc.gov/MARC21/slim"> <leader>00000nmm##2200000uu#4500</leader> <datafield tag="041" ind1=" " ind2=" "> <subfield code="a">eng</subfield> </datafield> <datafield tag="653" ind1=" " ind2=" "> <subfield code="a">forming, forge, sensors, dynamic measurement, measurement uncertainty, sensor network, digital sensors, MEMS, machine learning</subfield> </datafield> <controlfield tag="005">20200124192625.0</controlfield> <controlfield tag="001">3405265</controlfield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="u">University of Strathclyde</subfield> <subfield code="a">Gordon Gourlay</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="u">University of Strathclyde</subfield> <subfield code="a">Ivan Andonovic</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="u">NPL</subfield> <subfield code="a">Omer Panni</subfield> </datafield> <datafield tag="856" ind1="4" ind2=" "> <subfield code="s">282484853</subfield> <subfield code="z">md5:549e0e2f365c51748373f265dff7b268</subfield> <subfield code="u">https://zenodo.org/record/3405265/files/STRATH radial forge dataset v2.zip</subfield> </datafield> <datafield tag="542" ind1=" " ind2=" "> <subfield code="l">open</subfield> </datafield> <datafield tag="260" ind1=" " ind2=" "> <subfield code="c">2019-09-11</subfield> </datafield> <datafield tag="909" ind1="C" ind2="O"> <subfield code="p">openaire_data</subfield> <subfield code="p">user-met4fof</subfield> <subfield code="o">oai:zenodo.org:3405265</subfield> </datafield> <datafield tag="100" ind1=" " ind2=" "> <subfield code="u">University of Strathclyde</subfield> <subfield code="a">Christos Tachtatzis</subfield> </datafield> <datafield tag="245" ind1=" " ind2=" "> <subfield code="a">Sensor data set radial forging at AFRC testbed v2</subfield> </datafield> <datafield tag="980" ind1=" " ind2=" "> <subfield code="a">user-met4fof</subfield> </datafield> <datafield tag="540" ind1=" " ind2=" "> <subfield code="u">https://creativecommons.org/licenses/by/4.0/legalcode</subfield> <subfield code="a">Creative Commons Attribution 4.0 International</subfield> </datafield> <datafield tag="650" ind1="1" ind2="7"> <subfield code="a">cc-by</subfield> <subfield code="2">opendefinition.org</subfield> </datafield> <datafield tag="520" ind1=" " ind2=" "> <subfield code="a"><p><strong>Sensor data set, radial forging at AFRC testbed</strong></p> <p><strong>General information on the data set</strong></p> <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> <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> <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> <p><strong>Structure of the data</strong></p> <ul> <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> <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> <li>The CMM data is provided in the file &ldquo;CMMData.xlsx&rdquo;.</li> </ul> <p><strong>Further Information</strong></p> <p>For an introduction and tutorial to this data, a set of Jupyter notebooks is available here:</p> <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> <p>These notebooks contain Python code and a documentation of example machine learning tasks and analysis of this data set.</p></subfield> </datafield> <datafield tag="773" ind1=" " ind2=" "> <subfield code="n">doi</subfield> <subfield code="i">isVersionOf</subfield> <subfield code="a">10.5281/zenodo.2573860</subfield> </datafield> <datafield tag="024" ind1=" " ind2=" "> <subfield code="a">10.5281/zenodo.3405265</subfield> <subfield code="2">doi</subfield> </datafield> <datafield tag="980" ind1=" " ind2=" "> <subfield code="a">dataset</subfield> </datafield> </record>
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