Dataset Open Access
Christos Tachtatzis; Gordon Gourlay; Ivan Andonovic; Omer Panni
<?xml version='1.0' encoding='utf-8'?> <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:adms="http://www.w3.org/ns/adms#" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dct="http://purl.org/dc/terms/" xmlns:dctype="http://purl.org/dc/dcmitype/" xmlns:dcat="http://www.w3.org/ns/dcat#" xmlns:duv="http://www.w3.org/ns/duv#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:frapo="http://purl.org/cerif/frapo/" xmlns:geo="http://www.w3.org/2003/01/geo/wgs84_pos#" xmlns:gsp="http://www.opengis.net/ont/geosparql#" xmlns:locn="http://www.w3.org/ns/locn#" xmlns:org="http://www.w3.org/ns/org#" xmlns:owl="http://www.w3.org/2002/07/owl#" xmlns:prov="http://www.w3.org/ns/prov#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xmlns:schema="http://schema.org/" xmlns:skos="http://www.w3.org/2004/02/skos/core#" xmlns:vcard="http://www.w3.org/2006/vcard/ns#" xmlns:wdrs="http://www.w3.org/2007/05/powder-s#"> <rdf:Description rdf:about="https://doi.org/10.5281/zenodo.3405265"> <rdf:type rdf:resource="http://www.w3.org/ns/dcat#Dataset"/> <dct:type rdf:resource="http://purl.org/dc/dcmitype/Dataset"/> <dct:identifier rdf:datatype="http://www.w3.org/2001/XMLSchema#anyURI">https://doi.org/10.5281/zenodo.3405265</dct:identifier> <foaf:page rdf:resource="https://doi.org/10.5281/zenodo.3405265"/> <dct:creator> <rdf:Description> <rdf:type rdf:resource="http://xmlns.com/foaf/0.1/Agent"/> <foaf:name>Christos Tachtatzis</foaf:name> <org:memberOf> <foaf:Organization> <foaf:name>University of Strathclyde</foaf:name> </foaf:Organization> </org:memberOf> </rdf:Description> </dct:creator> <dct:creator> <rdf:Description> <rdf:type rdf:resource="http://xmlns.com/foaf/0.1/Agent"/> <foaf:name>Gordon Gourlay</foaf:name> <org:memberOf> <foaf:Organization> <foaf:name>University of Strathclyde</foaf:name> </foaf:Organization> </org:memberOf> </rdf:Description> </dct:creator> <dct:creator> <rdf:Description> <rdf:type rdf:resource="http://xmlns.com/foaf/0.1/Agent"/> <foaf:name>Ivan Andonovic</foaf:name> <org:memberOf> <foaf:Organization> <foaf:name>University of Strathclyde</foaf:name> </foaf:Organization> </org:memberOf> </rdf:Description> </dct:creator> <dct:creator> <rdf:Description> <rdf:type rdf:resource="http://xmlns.com/foaf/0.1/Agent"/> <foaf:name>Omer Panni</foaf:name> <org:memberOf> <foaf:Organization> <foaf:name>NPL</foaf:name> </foaf:Organization> </org:memberOf> </rdf:Description> </dct:creator> <dct:title>Sensor data set radial forging at AFRC testbed v2</dct:title> <dct:publisher> <foaf:Agent> <foaf:name>Zenodo</foaf:name> </foaf:Agent> </dct:publisher> <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2019</dct:issued> <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> <dct:language rdf:resource="http://publications.europa.eu/resource/authority/language/ENG"/> <owl:sameAs rdf:resource="https://zenodo.org/record/3405265"/> <adms:identifier> <adms:Identifier> <skos:notation rdf:datatype="http://www.w3.org/2001/XMLSchema#anyURI">https://zenodo.org/record/3405265</skos:notation> <adms:schemeAgency>url</adms:schemeAgency> </adms:Identifier> </adms:identifier> <dct:isVersionOf rdf:resource="https://doi.org/10.5281/zenodo.2573860"/> <dct:isPartOf rdf:resource="https://zenodo.org/communities/met4fof"/> <owl:versionInfo>1.0</owl:versionInfo> <dct:description><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></dct:description> <dct:accessRights rdf:resource="http://publications.europa.eu/resource/authority/access-right/PUBLIC"/> <dct:accessRights> <dct:RightsStatement rdf:about="info:eu-repo/semantics/openAccess"> <rdfs:label>Open Access</rdfs:label> </dct:RightsStatement> </dct:accessRights> <dcat:distribution> <dcat:Distribution> <dct:license rdf:resource="https://creativecommons.org/licenses/by/4.0/legalcode"/> <dcat:accessURL rdf:resource="https://doi.org/10.5281/zenodo.3405265"/> </dcat:Distribution> </dcat:distribution> <dcat:distribution> <dcat:Distribution> <dcat:accessURL>https://doi.org/10.5281/zenodo.3405265</dcat:accessURL> <dcat:byteSize>282484853</dcat:byteSize> <dcat:downloadURL>https://zenodo.org/record/3405265/files/STRATH radial forge dataset v2.zip</dcat:downloadURL> <dcat:mediaType>application/zip</dcat:mediaType> </dcat:Distribution> </dcat:distribution> </rdf:Description> </rdf:RDF>
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