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<rdf:RDF xmlns:community="http://www.bibsonomy.org/ontologies/2008/05/community#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:owl="http://www.w3.org/2002/07/owl#" xmlns:admin="http://webns.net/mvcb/" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:syn="http://purl.org/rss/1.0/modules/syndication/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:taxo="http://purl.org/rss/1.0/modules/taxonomy/" xmlns:cc="http://web.resource.org/cc/" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" xmlns:swrc="http://swrc.ontoware.org/ontology#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xmlns="http://purl.org/rss/1.0/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xml:base="https://puma.ub.uni-stuttgart.de/tag/Learning%20Process%20vector"><owl:Ontology rdf:about=""><rdfs:comment>PUMA publications for /tag/Learning%20Process%20vector</rdfs:comment><owl:imports rdf:resource="http://swrc.ontoware.org/ontology/portal"/></owl:Ontology><rdf:Description rdf:about="https://puma.ub.uni-stuttgart.de/bibtex/28c494972705666f29df6c2d8f9c80392/isw-bibliothek"><owl:sameAs rdf:resource="/uri/bibtex/28c494972705666f29df6c2d8f9c80392/isw-bibliothek"/><rdf:type rdf:resource="http://swrc.ontoware.org/ontology#Article"/><owl:sameAs rdf:resource="http://www.sciencedirect.com/science/article/pii/S0736584515001155"/><swrc:date>Mon Mar 21 15:25:16 CET 2016</swrc:date><swrc:journal>Robotics and Computer-Integrated Manufacturing </swrc:journal><swrc:pages> - </swrc:pages><swrc:title>Estimation of stability lobe diagrams in milling with continuous learning algorithms </swrc:title><swrc:year>2015</swrc:year><swrc:keywords>Learning Milling Neural Process Support algorithms machines myown network stability vector xfh xlr xvl </swrc:keywords><swrc:abstract>Abstract The productivity of milling processes is limited by the occurrence of chatter vibrations. The correlation of the maximum stable cutting depth and the spindle speed can be shown in a stability lobe diagram (SLD). The stability is different for different width of cut and can change with the axis positions. Today it is already a great effort to estimate the \{SLD\} only for one position. Many experiments are necessary to measure the \{SLD\} or derive a detailed mathematical model to calculate the SLD. Moreover not only the cutting depth, but also the cutting width should be represented in the SLD. This paper presents a new approach to assess the process stability based on measured acceleration signals. The multidimensional stability lobe diagram (MSLD) are derived during the production using two new continuously learning algorithms. In this paper the application of a continuous learning support vector machine and a continuous neural network is shown. The support vector machine and the neural network are extended to make them capable for continuous learning and time-variant systems. A new trust criterion is introduced, which gives information about the prediction quality of the output for the selected input region. The learned \{MSLDs\} are evaluated against analytically calculated \{MSLDs\} and the learning algorithms can reproduce the analytical results very well. </swrc:abstract><swrc:hasExtraField><swrc:Field swrc:value="0736-5845" swrc:key="issn"/></swrc:hasExtraField><swrc:hasExtraField><swrc:Field swrc:value="http://dx.doi.org/10.1016/j.rcim.2015.10.003" swrc:key="doi"/></swrc:hasExtraField><swrc:author><rdf:Seq><rdf:_1><swrc:Person swrc:name="Jens Friedrich"/></rdf:_1><rdf:_2><swrc:Person swrc:name="Christoph Hinze"/></rdf:_2><rdf:_3><swrc:Person swrc:name="Anton Renner"/></rdf:_3><rdf:_4><swrc:Person swrc:name="Alexander Verl"/></rdf:_4><rdf:_5><swrc:Person swrc:name="Armin Lechler"/></rdf:_5></rdf:Seq></swrc:author></rdf:Description><rdf:Description rdf:about="https://puma.ub.uni-stuttgart.de/bibtex/2575e20fd8faadd0adcf43a69e8eea13b/isw-bibliothek"><owl:sameAs rdf:resource="/uri/bibtex/2575e20fd8faadd0adcf43a69e8eea13b/isw-bibliothek"/><rdf:type rdf:resource="http://swrc.ontoware.org/ontology#InProceedings"/><owl:sameAs rdf:resource="http://dx.doi.org/10.14809/faim.2014.0641"/><swrc:date>Mon Mar 21 15:22:24 CET 2016</swrc:date><swrc:booktitle>Proceedings of the 24th International Conference on Flexible Automation {\&amp;} Intelligent Manufacturing</swrc:booktitle><swrc:publisher><swrc:Organization swrc:name="University of Texas at San Antonio"/></swrc:publisher><swrc:title>Continuous learning support vector machine to estimate stability lobe diagrams in milling</swrc:title><swrc:year>2014</swrc:year><swrc:keywords>Learning Milling Process Support algorithm isw machines myown stbaility vector xfh xlr xvl </swrc:keywords><swrc:hasExtraField><swrc:Field swrc:value="10.14809/faim.2014.0641" swrc:key="doi"/></swrc:hasExtraField><swrc:author><rdf:Seq><rdf:_1><swrc:Person swrc:name="Jens Friedrich"/></rdf:_1><rdf:_2><swrc:Person swrc:name="Henning Hartmann"/></rdf:_2><rdf:_3><swrc:Person swrc:name="Alexander Verl"/></rdf:_3><rdf:_4><swrc:Person swrc:name="Armin Lechler"/></rdf:_4></rdf:Seq></swrc:author></rdf:Description><foaf:Group rdf:about="https://puma.ub.uni-stuttgart.de/tag/Learning%20Process%20vector"><foaf:name>Learning Process vector</foaf:name><description>Community for tag(s) Learning Process vector</description></foaf:Group></rdf:RDF>