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Influence of the milling strategy on the durability of forging tools
Ivo Pahole, Dejan Studenčnik, Karl Gotlih, Mirko Ficko, Jože Balič, 2011, original scientific article

Abstract: The quality of a tool's surface has a direct influence on the number of well-produced parts. For the machining of an active tool surface, two technological processes are used: electrical discharge machining and high-speed milling. These two processes are used when machining new tools and for the repairing of used forging tools. In both cases, the material has already been thermally treated, so it has to be used for hard milling. Practical experience shows that the milling strategy has a big influence on the durability of a forging tool. This paper shows the influence of the CNC machining direction during high-speed milling on the durability of the engraving within the forging tool. In some cases the correct milling strategy can increase the durability of the forging tool by about one third.
Keywords: orodja za kovanje, kakovost površine, visokohitrostno rezkanje, CNC-rezkanje, forging tools, surface quality, high speed cutting, CNC milling
Published in DKUM: 10.07.2015; Views: 1165; Downloads: 109
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Real-time motor unit identification from high-density surface EMG
Vojko Glaser, Aleš Holobar, Damjan Zazula, 2013, original scientific article

Abstract: This study addresses online decomposition of high-density surface electromyograms (EMG) in real-time. The proposed method is based on previouslypublished Convolution Kernel Compensation (CKC) technique and sharesthe same decomposition paradigm, i.e. compensation of motor unit action potentials and direct identification of motor unit (MU) discharges. In contrast to previously published version of CKC, which operates in batch mode and requires ~ 10 s of EMG signal, the real-time implementation begins with batch processing of ~ 3 s of the EMG signal in the initialization stage and continues on with iterative updating of the estimators of MU discharges as blocks of new EMG samples become available. Its detailed comparison to previously validated batch version of CKC and asymptotically Bayesian optimal Linear Minimum Mean Square Error (LMMSE) estimator demonstrates high agreementin identified MU discharges among all three techniques. In the case of synthetic surface EMG with 20 dB signal-to-noise ratio, MU discharges were identified with average sensitivity of 98 %. In the case of experimental EMG, real-time CKC fully converged after initial 5 s of EMG recordings and real-time and batch CKC agreed on 90 % of MU discharges, on average. The real-time CKC identified slightly fewer MUs than its batch version (experimental EMG, 4 MUs versus 5 MUs identified by batch CKC, on average), but required only 0.6 s of processing time on regular personal computer for each second of multichannel surface EMG.
Keywords: discharge pattern, high-density EMG, surface EMG, motor unit, real time decomposition
Published in DKUM: 25.05.2015; Views: 1316; Downloads: 0

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