• Journal of Electrical Engineering and Technology
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• 제13권3호
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• pp.1202-1211
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• 2018

In this paper, induction machine operation efficiency and torque is improved using Machine Learning based Gene Optimization (ML-GO) Technique is introduced. Optimized Genetic Algorithm (OGA) is used to select the optimal induction machine data. In OGA, selection, crossover and mutation process is carried out to find the optimal electrical machine data for induction machine design. Initially, many number of induction machine data are given as input for OGA. Then, fitness value is calculated for all induction machine data to find whether the criterion is satisfied or not through fitness function (i.e., objective function such as starting to full load torque ratio, rotor current, power factor and maximum flux density of stator and rotor teeth). When the criterion is not satisfied, annealed selection approach in OGA is used to move the selection criteria from exploration to exploitation to attain the optimal solution (i.e., efficient machine data). After the selection process, two point crossovers is carried out to select two crossover points within a chromosomes (i.e., design variables) and then swaps two parent's chromosomes for producing two new offspring. Finally, Adaptive Levy Mutation is used in OGA to select any value in random manner and gets mutated to obtain the optimal value. This process gets iterated till finding the optimal value for induction machine design. Experimental evaluation of ML-GO technique is carried out with performance metrics such as torque, rotor current, induction machine operation efficiency and rotor power factor compared to the state-of-the-art works.

• 한국주조공학회지
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• 제27권4호
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• pp.167-172
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• 2007

In the present study, the microstructural evolution and mechanical properties of $Al_{71.6}Ge_{28.4}$ eutectic alloy have been investigated. Stable (fcc ${\alpha}$-Al and diamond cubic ${\beta}$-Ge) and various metastable crystalline (monoclinic, rhombohedral) phases were produced by competitive phase selection during non-equilibrium processing methods i.e. melt spinning and injection casting. The as-injection casted samples containing metastable-equilibrium eutectic (${\alpha}$-Al + monoclinic) structure showed much higher strength than samples with equilibrium eutectic (${\alpha}-Al+{\beta}-Ge$) structure but plasticity disappointingly diminished. In order to endow the enhanced ductility without significant strength drop, the alloys was heat-treated at transition temperature from metastable phase to stable phase. The annealed specimen displayed the phase transformed microstructural evolution and enhanced macroscopic plasticity.

• Steel and Composite Structures
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• 제22권4호
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• pp.875-888
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• 2016

The proper selection of materials for the intended use of the structural member is of particular interest. The paper deals with determining both the mechanical properties at different temperatures and the behavior in tensile creep as well as fatigue testing of tensile stressed specimens made of low alloy 42CrMo4 steel delivered as annealed and cold drawn. This steel is usually used in engineering practice in design of statically and dynamically stressed components. Displayed engineering stress - strain diagrams indicate the mechanical properties, creep curves indicate the material creep behavior while experimental investigations of fatigue may ensure the fatigue limit determination for considered stress ratio. Also, hardness testing provides an insight into material resistance to plastic deformation. Experimentally obtained results regarding material properties were: tensile strength (735 MPa / $20^{\circ}C$, 105 MPa / $680^{\circ}C$), yield strength (593 MPa / $20^{\circ}C$, 76 MPa / $680^{\circ}C$). Fatigue limit in the amount of 532.26 MPa, as maximum stress at stress ratio R = 0.25 at ambient temperature was calculated on the basis of experimentally obtained results. Regarding the creep resistance it is visible that this steel can be treated as creep resistant at high temperatures (including $580^{\circ}C$) when applied stress is of low level (till 0.2 of yield stress).