Thermal model of through flow universal motor by means of lumped parameter network

Biti u stanju predvidjeti porast temperature unutar stroja jednako je važno kao i predvidjeti njegovo djelovanje i radni vijek. Budući da mjerenja temperature i toplinske simulacije računalom mogu zahtijevati puno vremena, putovi topline unutar protočnog univerzalnog motora su opisani jednostavnom toplinskom mrežom skupnog (lumped) parametra. Jednom kad je model izgrađen, njegovi nepoznati koeficijenti konvekcije su usklađeni s alatom genetičkog algoritma u MatLab. Model je primijenjen i uspješno provjeren mjerenjima na dva različita tipa motora usisivača za prašinu. Uzimajući u obzir gibitke rotora kao jednog od ulaza modela, procjene temperature su točnije bez obzira na radni režim stroja.Being able to predict temperature rise inside a machine is as important as predicting its performance and life. Because temperature measurements and computational thermal simulations can be time consuming, thermal paths inside the through-flow universal motor were described by means of simple lumped parameter thermal network. Once the model was built, its unknown convection coefficients were tuned with the genetic algorithm tool in MatLab. The model has been applied and successfully verified with measurements on two different types of a vacuum cleaner motor. Taking account of impeller losses as one of the model inputs makes temperature estimates more accurate regardless of machine’s operational regime

Microstructural analysis of Bulk Molding Compounds and correlation with the flexural strength

In this study, the influence of the glass fiber (GF) content on the microstructure and flexural strength of bulk molding compounds (BMCs) is investigated. Three sets of BMCs with different weight fractions of GF (5/10/12.5 wt%) were commercially prepared and compression molded into test specimens. The microstructure of the composites was analysed by scanning electron microscopy and further quantitatively characterized by Voronoi analysis in order to define the degree of the fiber distribution homogeneity. The experimental results were compared to the modelled microstructures. The results revealed that the fiber distribution in the composite with 5 wt% of GF is considered as the most homogeneous. Through the obtained microstructural descriptors, the fiber weight content and their distribution were correlated to the flexural strength of BMCs. The flexural strength was the highest for the composite with 10 wt% of GF

Microstructure, mechanical and electrical properties of Glass Fiber Reinforced Composites (GFRC)

In this study, the influence of E-glass fiber and mineral filler content on the microstructure, physical, mechanical and dielectric properties of Glass Fiber Reinforced Composites (GFRC) was investigated. Five sets of GFRC, based on polymer resin with varying E-grade glass fibers and CaCO3 mineral filler weight fractions (15/64, 20/59, 25/54, 30/49, 35/44), were commercially prepared. Test specimens were prepared by compression molding. Scanning Electron Microscope images revealed that at higher concentrations, the fibers clustered together, resulting in heterogeneous microstructures. Characterization of the composites showed that glass fiber content and distribution significantly affects the mechanical properties. The flexural strength of the composites decreased with increasing glass fiber content. The dielectric constant ε` decreased with increasing fiber content

Failure modes and life prediction model for high-speed bearings in a through-flow universal motor

The focus of this study was the empirical modelling of the high-speed bearings life and failure modes of a through-flow universal motor. An approach was used facilitating predictions of the life through-range of various conditions. The model estimates bearing life for the survival probability of 50% % L50. It influences parameters such as bearing temperature, speed factor, equivalent load, grease fill amount, type of oil, type of bearing cage, type of seals, tolerance class, and side of the motor, all of which are considered in the model. Initial empirical data consisted of 4672 test populations, involving 38,021 vacuum cleaner motors. Strict filtering requirements of all the available test data resulted in 170 final populations, consisting of 1385 tested and 638 failed bearings, which were used for building a Weibull database and for developing the models. The paper`s key contributions are the empirical models gained with multiple linear regression and the obtained database of tested bearings