Fabrication of bulk piezoelectric and dielectric BaTiO3 ceramics using paste extrusion 3D printing technique

A simple and facile method was developed to fabricate functional bulk barium titanate (BaTiO₃, BT) ceramics using the paste extrusion 3D printing technique. The BT ceramic is a lead-free ferroelectric material widely used for various applications in sensors, energy storage, and harvesting. There are several traditional methods (eg, tape casting) to process bulk BT ceramics but they have disadvantages such as difficult handing without shape deformation, demolding, complex geometric shapes, expansive molds, etc. In this research, we utilized the paste extrusion 3D printing technique to overcome the traditional issues and developed printable ceramic suspensions containing BT ceramic powder, polyvinylidene fluoride (PVDF), N,N-dimethylformamide (DMF) through simple mixing method and chemical formulation. This PVDF solution erformed multiple roles of binder, plasticizer, and dispersant for excellent manufacturability while providing high volume percent and density of the final bulk ceramic. Based on empirical data, it was found that the maximum binder ratio with good viscosity and retention for desired geometry is 1:8.8, while the maximum BT content is 35.45 vol% (77.01 wt%) in order to achieve maximum density of 3.93 g/cm³ (65.3%) for 3D printed BT ceramic. Among different sintering temperatures, it was observed that the sintered BT ceramic at 1400°C had highest grain growth and tetragonality which affected high performing piezoelectric and dielectric properties, 200 pC/N and 4730 at 10³ Hz respectively. This paste extrusion 3D printing technique and simple synthesis method for ceramic suspensions are expected to enable rapid massive production, customization, design flexibility of the bulk piezoelectric and dielectric devices for next generation technology.     

The rough set based approach to generic routing problems: case of reverse logistics supplier selection

In recent years, Reverse Logistics (RL) has been touted as one of the strategies of improving organization performance and generating a competitive advantage. In RL, the generic routing problem has become a focus since it provides a great flexibility in modeling, e.g., selection of suppliers by using a node as a supplier candidate in a network. To date, complicated networks make decision makers hard to search a desired routine. In addition, the traditional network defines and resolves such a problem only at one soot. The solution cannot be acquired from multiple perspectives like minimal cost, minimal delivery time, maximal reliability, and optimal “3Rs”—reduce, reuse, and recycle. In this study, rough set theory is applied to reduce complexity of the RL data sets and induct decision rules. Through incorporating the decision rules, the generic label correcting algorithm is used to solve generic routing problems by integrating various operators and comparators in the GLC algorithm. Consequently, the desired RL suppliers are selected.     

Design support systems: A case study of modular design of the set-top box from design knowledge externalization perspective

In this paper, the knowledge externalization of modular product design is introduced, which discovers and shares the design rules to increase the effectiveness/efficiency of the design process. This solution approach involves modeling modular products, formulating their design knowledge, and discovering implicit knowledge into design rules based on the rough set theory. The design rules are generated for improving heat dissipation issues of the set-top box (STB), which is perhaps the most attractive device for the home. A web based knowledge management of product design (KMPD) system is also implemented to provide adapted design rules and share design knowledge in distributed environment. The methodology discussed in the paper departs from the previous developments in design knowledge externalization as well as rough set theory application, thus creating a variable approach for product design.     

Minimizing the normalized sum of square for workload deviations on m parallel processors

In many organizations, it is desirable to distribute workload as equally as possible among a group of employees or machines. This paper proposes a performance measure, that we call the Normalized Sum of Square for Workload Deviations (NSSWD), and studies the problem of how to schedule a set of n jobs on m parallel identical processors in order to minimize the NSSWD. The NSSWD criterion is relevant where uniformity of wear to machines or of workload to employees is desirable. An algorithm, called Workload Balancing (WB), is proposed for solving this problem. Moreover, we perform a simulation experiment to evaluate WB against several well-known heuristics in the literature. Lastly, we discuss the computational results obtained from the simulation experiment.     

Rule induction based on an incremental rough set

The incremental technique is a way to solve the issue of added-in data without re-implementing the original algorithm in a dynamic database. There are numerous studies of incremental rough set based approaches. However, these approaches are applied to traditional rough set based rule induction, which may generate redundant rules without focus, and they do not verify the classification of a decision table. In addition, these previous incremental approaches are not efficient in a large database. In this paper, an incremental rule-extraction algorithm based on the previous rule-extraction algorithm is proposed to resolve there aforementioned issues. Applying this algorithm, while a new object is added to an information system, it is unnecessary to re-compute rule sets from the very beginning. The proposed approach updates rule sets by partially modifying the original rule sets, which increases the efficiency. This is especially useful while extracting rules in a large database.     

Optimal Placement of Intermittent Renewable Energy Resources and Energy Storage System in Smart Power Distribution Networks

This paper presents an efficient strategy to optimally allocate renewable energy sources (RES), primarily wind and solar photovoltaic (PV), and energy storage system (ESS) in electrical distribution networks with a goal of minimizing costs and active power losses. The proposed planning strategy is used to determine the number of possible and optimal locations for the hybrid RES–ESS system. This paper further presents a control scheme to optimally dispatch the output of ESS, which increases the effective utilization of RES by reducing their day-ahead forecast errors in order to minimize the deviation between forecasted and actual values. In our proposed strategy, the location that produces the least overall power losses complying with the system constraints is considered as the optimal one. In order to evaluate the effectiveness of the proposed method, this paper considers a case study of a 16-bus test system that comprises of several loads, wind farm, PV, and ESS. The test results demonstrate that by determining the optimal bus location for the RES–ESS system, overall power losses as well as peak load can be significantly reduced.     

An effective capacitance model for computing the electronic properties of charged defects in crystals

By examining how a defect within a crystalline material responds to small changes in its charge state, the electronic properties of an ionized defect can be modeled by an effective work function and capacitance. Such an approach leads to a correction formula to the total energy of a charged periodic system and allows a comparison between the electronic band structure of the ionized defect to its corresponding neutral one. The correction formula can be related to the potential alignment method and Makov–Payne correction widely adopted in charged periodic systems. The new approach suggests both an alternative interpretation and improvements to the popular Makov–Payne and potential alignment scheme. P-doped Si, which has a shallow donor level, and an isolated vacancy in crystalline Si, which has a deep defect level within the Si energy gap, are chosen as prototypical systems to demonstrate our method.     

Magic structures of h-passivated 110 silicon nanowires

We report a genetic algorithm approach combined with ab initio calculations to determine the structure of hydrogenated 110 Si nanowires. As the number of atoms per length increases, we find that the cross section of the nanowire evolves from chains of six-atom rings to fused pairs of such chains to hexagons bounded by {001} and {111} facets. Our calculations predict that hexagonal wires become stable starting at about 1.2 nm diameter, which is consistent with recent experimental reports of nanowires with diameters of about 3 nm.