PUMA publications for /tag/Learning%20ProcessTue Jul 02 11:28:25 CEST 2024jan2018 National Conference of the United States Association for Small Business and Entrepreneurship, USASBE 2018 ; Conference date: 10-01-2018 Through 14-01-2018The Learning Process in Technology Entrepreneurship Education – Insights from an Engineering Degree2018Entrepreneurship Entrepreneurship, Learning Process Technology education, Fri Feb 02 08:07:58 CET 2024Procedia CIRP216–221Tool condition monitoring in drilling processes using anomaly detection approaches based on control internal data1212024analysis anomaly condition detection learning machine machining manufacturing monitoring networks neural process series time tool Fri Mar 20 16:22:37 CET 2020Load-inducing factors in instructional design: Process-related advances in theory and assessment2019assessment cognition instruction learning load modeling myown process theory Mon Mar 21 15:25:16 CET 2016Robotics and Computer-Integrated Manufacturing - Estimation of stability lobe diagrams in milling with continuous learning algorithms 2015Learning Milling Neural Process Support algorithms machines myown network stability vector xfh xlr xvl 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. Mon Mar 21 15:22:24 CET 2016Proceedings of the 24th International Conference on Flexible Automation {\&} Intelligent ManufacturingContinuous learning support vector machine to estimate stability lobe diagrams in milling2014Learning Milling Process Support algorithm isw machines myown stbaility vector xfh xlr xvl Learning ProcessCommunity for tag(s) Learning Process