Critical aspects of sinter-hardening of prealloyed Cr–Mo steel

In recent years, growing demand for greater mechanical properties of PM steel components with competitive fabrication cost has led to significant innovations in different fields of powder metallurgy. Recent research has been focused on reaching higher performance with lower cost. To this end, the possibility of combining the conventional sintering and post-sintering processes for a particular powder composition has been introduced. Sinter-hardening is a result of the research conducted along this line. Elimination of any secondary operation such as quench-hardening by incorporating it in the sintering process (i.e. sinter-hardening) is of great interest, as it will lead to lower processing costs and equal, if not higher mechanical performance. However, to ensure the desired mechanical properties of the final component and robustness of the performance, critical aspects of the sinter-hardening process should be rigorously studied.Hence with specific attention to a Cr–Mo steel powder (FL-5305), this study deals with the influence of density on cooling rate, the effect of different sintering temperatures (e.g. 1120 °C and 1250 °C) on austenite grain size and consequently, hardenability. The microstructure development in sinter-hardened FL-5305 material has been analyzed and predicted by means of the available literature for solid steel and also using the commercial software (JMatPro 5.0) for materials assessment based on thermodynamic and kinetics modeling. Finally, inaccurate carbon control and its adverse impact on excessive formation of cementite have been addressed.     

Virtual Machine Consolidation in Cloud Computing Systems: Challenges and Future Trends

Wireless Personal Communications - Cloud Computing Systems (CCSs) provides a computing capability through the Internet. It enables organizations or individuals to have a computing power without...     

The Role of Elastic and Plastic Anisotropy of Snin Recrystallization and Damage Evolution DuringThermal Cycling in SAC305 Solder Joints

Because failures in lead-free solder joints occur at locations other than the most highly shear-strained regions, reliability prediction is challenging. To gain physical understanding of this phenomenon, physically based understanding of how elastic and plastic deformation anisotropy affect microstructural evolution during thermomechanical cycling is necessary. Upon solidification, SAC305 (Sn-3.0Ag-0.5Cu) solder joints are usually single or tricrystals. The evolution of microstructures and properties is characterized statistically using optical and orientation imaging microscopy. In situ synchrotron x-ray measurements during thermal cycling are used to examine how crystal orientation and thermal cycling history change strain history. Extensive characterization of a low-stress plastic ball grid array (PBGA) package design at different stages of cycling history is compared with preliminary experiments using higher-stress package designs. With time and thermal history, microstructural evolution occurs mostly from continuous recrystallization and particle coarsening that is unique to each joint, because of the specific interaction between local thermal and displacement boundary conditions and the strong anisotropic elastic, plastic, expansion, and diffusional properties of Sn crystals. The rate of development of recrystallized microstructures is a strong function of strain and aging. Cracks form at recrystallized (random) boundaries, and then percolate through recrystallized regions. Complications arising from electromigration and corrosion are also considered.     

Steady-state analysis of quantized distributed incremental LMS algorithm without Gaussian restriction

In this paper, we analyze the steady-state performance of the distributed incremental least mean-square (DILMS) algorithm when it is implemented in finite-precision arithmetic. Our analysis in this paper does not consider any distribution of input data. We first formulate the update equation for quantized DILMS algorithm, and then we use a spatial-temporal energy conservation argument to derive theoretical expressions that evaluate the steady-state performance of individual nodes in the network. We consider mean-square error, excess mean-square error, and mean-square deviation as the performance criteria. Simulation results are generated by using two types of signals, Gaussian and non-Gaussian distributed signals. As the simulation results show, there is a good match between the theory and simulation.     

New pulse mode neuro-fuzzy hardware architecture and application to image denoising

This paper focuses on a new digital architecture of pulse mode neuro-fuzzy system (PMNFS) with on-chip learning ability. The main purpose goal is to make use of the outstanding features of neuro-fuzzy in function approximation, and implement a reconfigurable architecture with on-chip learning on a field-programmable gate array (FPGA) platform. Details of the whole design with on-chip learning solutions are given. As an application illustrating the efficiency and scalability of the proposed PMNFS, we have considered the approximation of image denoising, which is a very important step in image processing. Experimental results show great efficiency of the proposed method, outperforming other denoising techniques. It was also demonstrated that such a system is strongly adaptive and gives good restored images independently of the kind of noises. Owing to learning, such feature cannot be met with conventional denoising techniques. Design synthesis results on a virtex II PRO FPGA platform are presented. Comparisons with conventional techniques as well as neural ones show higher performances of the designed PMNFS.     

A Novel Refined Track Initiation Algorithm for Group Targets Based on Group Model

Traditional refined track initiation methods for group targets have mistakes or loss of tracks when tracking irregular motions, for the reason that they rely on a stable relative position of group members. To solve the problem, a group dynamic model was introduced for proposing a new initiation algorithm and its whole framework. We made a self-adaptive improvement of the group separation on various group radii. After the pre-association of these groups, a state equation derived from the model was used for predictions of group members. Then a relational matrix was defined for refined data associations. Finally tracks were validated by logic-based method. Particular scenarios and Monte Carlo simulations showed that, compared with algorithms based on relative position, this algorithm has better performance on the adaptability to changes of a group structure and the correctness of initiation.     

Organic field-effect transistor circuits using atomic layer deposited gate dielectrics patterned by reverse stamping

We report on a reverse stamping method to produce via-holes in circuits comprising acene-based top-gate organic field-effect transistors (OFETs) having a CYTOP/Al₂O₃ (by atomic layer deposition) bilayer gate dielectric. This method relies on the weak adhesive force that exists between a small molecule acene film and a polymer to enable easy delamination of the bilayer gate dielectric by using a PDMS stamp. We demonstrate the effectiveness of this method by fabricating simple circuits using top-gate triisopropylsilylethynyl pentacene (TIPS-pentacene)/poly (triarylamine) (PTAA) OFETs.     

Adaptive feedback linearization control of antilock braking systems using neural networks

Antilock braking systems are designed to control the wheel slip, such that the braking force is maximized and steerability is maintained during braking. However, the control of antilock braking systems is a challenging problem due to nonlinear braking dynamics and the uncertain and time-varying nature of the parameters. This paper presents an adaptive neural network-based hybrid controller for antilock braking systems. The hybrid controller is based on the well-known feedback linearization, combined with two feedforward neural networks that are proposed so as to learn the nonlinearities of the antilock braking system associated with feedback linearization controller. The adaptation law is derived based on the structure of the controller, using steepest descent gradient approach and backpropagation algorithm to adjust the networks weights. The weight adaptation is online and the stability of the proposed controller in the sense of Lyapunov is studied. Simulations are conducted to show the effectiveness of the proposed controller under various road conditions and parameter uncertainties.