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The design freedom provided by Additive Manufacturing (AM) enables the part consolidation (PC) of sophisticated mechanical assemblies. However, PC has been mainly performed for static components in assemblies with nonmoving parts. In this regard, a new approach to assembly-level Design for Additive Manufacturing (A-DfAM) considering an industrial lifeboat hook assembly with a functionally dynamic system is proposed. The methodology comprises steps starting from inputting the Computer-Aided Design (CAD) files for the 3D printing of the final assembly and evaluation. Throughout the design stages, opportunistic and restrictive natures of DfAM within our methodology direct engineers and designers to manufacture optimized products. In addition, a comparative assessment of the original and final assemblies is also illustrated. Consequently, a significant part-count reduction after PC was achieved, and the prototype of the lifeboat hook components was printed via laser-powder bed fusion (L-PBF). This shows that by incorporating the suggested A-DfAM framework, it can serve as a potential guide to whoever intends to manufacture dynamic assemblies.

Using energy-saving electrochromic (EC) displays in smart devices for augmented reality makes cost-effective, easily producible, and efficiently operable devices for specific applications possible. Prussian blue (PB) is a metal-organic coordinated compound with unique EC properties that limit EC display applications due to the difficulty in PB micro-patterning. This work presents a novel micro-printing strategy for PB patterns using localized crystallization of FeFe(CN)

Approaches for predicting financial markets, including conventional statistical methods and recent deep learning methods, have been investigated in many studies. However, financial time series data (e.g., daily stock market index) contain noises that prevent stable predictive model learning. Using these noised data in predictions results in performance deterioration and time lag. This study proposes padding-based Fourier transform denoising (P-FTD) that eliminates the noise waveform in the frequency domain of financial time series data and solves the problem of data divergence at both ends when restoring to the original time series. Experiments were conducted to predict the closing prices of S&P500, SSE, and KOSPI by applying data, from which noise was removed by P-FTD, to different deep learning models based on time series. Results show that the combination of the deep learning models and the proposed denoising technique not only outperforms the basic models in terms of predictive performance but also mitigates the time lag problem.

Increasing production variability while maintaining operation efficiency remains a critical issue in many manufacturing industries. While the adoption of mixed-model assembly lines enables the production of high product variety, it also makes the system more complex as variety increases. This paper proposes an information entropy-based methodology that quantifies and then minimizes the complexity through product sequencing. The theory feasibility is demonstrated in a series of simulations to showcase the impact of sequencing in controlling the system predictability and complexity. Hence, the framework not only serves as a tool to quantitatively assess the impact of complexity on total system performance but also provides means and insights into how complexity can be mitigated without affecting the overall manufacturing workload.

Although macroscopically-sized MOF crystals have proven of interest for efficient chromatographic separations, information processing, or optoelectronic devices, growing really large crystals has proven problematic and the largest reported monocrystals are only ca. 0.025 cm3 in volume. Here, we report a sequential vapour-diffusion/reseeding procedure that yields monocrystals of γ-cyclodextrin (γ-CD) MOFs with volumes reaching 1 cm3. With such large monoliths at hand, it is possible to physically block their certain faces and thus guide further growth only in desired directions, ultimately producing crystals of low-symmetry shapes and – upon “doping” with different additives at different stages of growth – with spatially varying compositions.

Efforts to enhance thermal efficiency of turbines by increasing the turbine inlet temperature have been further accelerated by the introduction of 3D printing to turbine components as complex cooling geometry can be implemented using this technique. However, as opposed to the properties of materials fabricated by conventional methods, the properties of materials manufactured by 3D printing are not isotropic. In this study, we analyzed the anisotropic thermal conductivity of nickel-based superalloy CM247LC manufactured by selective laser melting (SLM). We found that as the density decreases, so does the thermal conductivity. In addition, the anisotropy in thermal conductivity is more pronounced at lower densities. It was confirmed that the samples manufactured with low energy density have the same electron thermal conductivity with respect to the orientation, but the lattice thermal conductivity was about 16.5% higher in the in-plane direction than in the cross-plane direction. This difference in anisotropic lattice thermal conductivity is proportional to the difference in square root of elastic modulus. We found that ellipsoidal pores contributed to a direction-dependent elastic modulus, resulting in anisotropy in thermal conductivity. The results of this study should be beneficial not only for designing next-generation gas turbines, but also for any system produced by 3D printing.

Over the past decades, additive manufacturing has rapidly advanced due to its advantages in enabling diverse material usage and complex design production. Nevertheless, the technology has limitations in terms of quality, as printed products are sometimes different from their desired designs or are inconsistent due to defects. Warping deformation, a defect involving layer shrinkage induced by the thermal residual stress generated during manufacturing processes, is a major factor in lowering the quality and raising the cost of printed products. This study utilized a variety of thermal time series data and the K-nearest neighbors (KNN) algorithm with dynamic time warping (DTW) to detect and predict the warping deformation in the printed parts using fused deposition modeling (FDM) printers. Multivariate thermal time series data extracted from thermocouples were trained using DTW-based KNN to classify warping deformation. The results showed that the proposed approach can predict warping deformation with an accuracy of over 80% by only using thermal time series data corresponding to 20% of the whole printing process. Additionally, the classification accuracy exhibited the promising potential of the proposed approach in warping prediction and in actual manufacturing processes, so the additional time and cost resulting from defective processes can be reduced.

Additive manufacturing (AM) became widespread through several organizations due to its benefits in providing design freedom, inventory improvement, cost reduction, and supply chain design. Process planning in AM involving various AM technologies is also complicated and scarce. Thus, this study proposed a decision-support tool that integrates production and distribution planning in AM involving material extrusion (ME), stereolithography (SLA), and selective laser sintering (SLS). A multi-objective optimization approach was used to schedule component batches to a network of AM printers. Next, the analytic hierarchy process (AHP) technique was used to analyze trade-offs among conflicting criteria. The developed model was then demonstrated in a decision-support system environment to enhance practitioners&rsquo, applications. Then, the developed model was verified through a case study using automotive and healthcare parts. Finally, an experimental design was conducted to evaluate the complexity of the model and computation time by varying the number of parts, printer types, and distribution locations.

The discovery of ferroelectricity in HfO2-based materials in 2011 provided new research directions and opportunities. In particular, for atomic layer deposited Hf0.5Zr0.5O2 (HZO) films, it is possible to obtain homogenous thin films with satisfactory ferroelectric properties at a low thermal budget process. Based on experiment demonstrations over the past 10 years, it is well known that HZO films show excellent ferroelectricity when sandwiched between TiN top and bottom electrodes. This work reports a comprehensive study on the effect of TiN top and bottom electrodes on the ferroelectric properties of HZO thin films (10 nm). Investigations showed that during HZO crystallization, the TiN bottom electrode promoted ferroelectric phase formation (by oxygen scavenging) and the TiN top electrode inhibited non-ferroelectric phase formation (by stress-induced crystallization). In addition, it was confirmed that the TiN top and bottom electrodes acted as a barrier layer to hydrogen diffusion into the HZO thin film during annealing in a hydrogen-containing atmosphere. These features make the TiN electrodes a useful strategy for improving and preserving the ferroelectric properties of HZO thin films for next-generation memory applications.

This paper presents an agent-based modeling framework for affordance-based driving behaviors during the exit maneuver of driver agents in human-integrated transportation problems. We start our discussion from one novel modeling framework based on the concept of affordance called the affordance-based finite state automata (AFSA) model, which incorporates the human perception of resource availability and action capability. Then, the agent-based simulation illustrates the validity of the AFSA framework for the highway-lane-driver system. Next, the comparative study between real driving data and agent-based simulation outputs is provided using the transition diagram. Finally, we perform a statistical analysis and a correlation study to analyze affordance-based driving behavior of driver agents. The simulation results show that the AFSA model well represents the perception-based human actions and drivers’ characteristics, which are essential for the design viewpoint of control framework of human driver modeling. This paper is also expected to benefit a designed control for autonomous/self-driving car in the future.

In this paper, recent technology for wearables to detect electrolyte of sweat and biocompatible smart materials for additive manufacturing (AM) have been surveyed. The challenges and gaps toward successful realization of functional wearable devices as a kit to test biomarkers were, then, discussed. While wearable devices sensing physical conditions of users are popular in a consumer market, wearable devices to detect chemical components of human sweat have little studied due to several limitations such as contacting human skin and controlling ambient parameters etc. AM technology makes innovative products realized, differing from conventional fabrication and design methodologies due to unlimited design freedom for manufacturing and material selections. We investigate the potentials of AM to be applied for wearable devices, although detecting chemical biofluids requires wider and deeper researches on the development of advanced materials and AM process.

Despite the recent advances in manufacturing automation, the role of human involvement in manufacturing systems is still regarded as a key factor in maintaining higher adaptability and flexibility. In general, however, modeling of human operators in manufacturing system design still considers human as a physical resource represented in statistical terms. In this paper, we propose a human in the loop (HIL) approach to investigate the operator's choice complexity in a mixed model assembly line. The HIL simulation allows humans to become a core component of the simulation, therefore influencing the outcome in a way that is often impossible to reproduce via traditional simulation methods. At the initial stage, we identify the significant features affecting the choice complexity. The selected features are in turn used to build a regression model, in which human reaction time with regard to different degree of choice complexity serves as a response variable used to train and test the model. The proposed method, along with an illustrative case study, not only serves as a tool to quantitatively assess and predict the impact of choice complexity on operator's effectiveness, but also provides an insight into how complexity can be mitigated without affecting the overall manufacturing throughput.

Three-dimensional (3D) printing is a new paradigm in customized manufacturing and allows the fabrication of complex structures that are difficult to realize with other conventional methods. Four-dimensional (4D) printing adds active, responsive functions to 3D-printed components, which can respond to various environmental stimuli. This review introduces recent ideas in 3D and 4D printing of mechanical multistable structures. Three-dimensional printing of multistable structures can enable highly reconfigurable components, which can bring many new breakthroughs to 3D printing. By adopting smart materials in multistable structures, more advanced functionalities and enhanced controllability can also be obtained in 4D printing. This could be useful for various smart and programmable actuators. In this review, we first introduce three representative approaches for 3D printing of multistable structures: strained layers, compliant mechanisms, and mechanical metamaterials. Then, we discuss 4D printing of multistable structures that can help overcome the limitation of conventional 4D printing research. Lastly, we conclude with future prospects.

The characteristics of embedded pulse plasma using 60 MHz radio frequency as the source power and 2 MHz radio frequency as the bias power were investigated for the etching of SiO2 masked with an amorphous carbon layer (ACL) using an Ar/C4F8/O2 gas mixture. Especially, the effects of the different pulse duty ratio of the embedded dual-frequency pulsing between source power and bias power on the characteristics on the plasma and SiO2 etching were investigated. The experiment was conducted by varying the source duty percentage from 90 to 30% while bias duty percentage was fixed at 50%. Among the different duty ratios, the source duty percentage of 60% with the bias duty percentage of 50% exhibited the best results in terms of etch profile and etch selectivity. The change of the etch characteristics by varying the duty ratios between the source power and bias power was believed to be related to the different characteristics of gas dissociation, fluorocarbon passivation, and ion bombardment observed during the different source/bias pulse on/off combinations. In addition, the instantaneous high electron temperature peak observed during each initiation of the source pulse-on period appeared to affect the etch characteristics by significant gas dissociation. The optimum point for the SiO2 etching with the source/bias pulsed dual-frequency capacitively coupled plasma system was obtained by avoiding this instant high electron temperature peak while both the source power and bias power were pulsed almost together, therefore, by an embedded RF pulsing.

Microwave-assisted reduced graphene oxide (MR-GO) layer was applied to hole extraction layer (HEL) of polymer solar cells (PSCs) and was compared with the widely used poly(3,4-ethylenedioxythiophene): poly(styrenesulfonate) (PEDOT:PSS) in bulk hetero-junction (BHJ) solar cells. The power conversion efficiency (PCE) of 3.57% was achieved with the MR-GO layer, which is 21% higher than that of PSCs with the conventional PEDOT:PSS HEL material. This enhancement of PCE is mainly attributed to the increase of short-circuit current density originated from the hydrophobic surface of the MR-GO layer. The hydrophobic graphene oxide surface is believed to improve wetting property and physical contact of active blends. In addition, the MR-GO interfacial layer is found to show the excellent device stability in atmospheric condition. The PCE of conventional PEDOT:PSS based PSCs showed total degradation when the device was exposed to atmospheric condition for 1000 h without any encapsulation, while that of MR-GO based PSC showed over 85% of PCE.

A flexible, and skin-attachable strain gauge sensor based on interconnected nanofibers was recently reported with discernible gauge factor for a flexible and highly sensitive pressure sensor. Here, we present a simple theory that can explain the observed electrical responses by employing the well-established friction law and electrical circuit analysis. The results are in good agreement with the experimental data obtained under physical loads.

Vertically-grown graphene electrodes are developed for flexible electric double-layer capacitors (EDLC). Solid-state electrolytes and large area flexible electrodes are essential parts for wearable applications. In this work, the vertically-grown graphene electrodes were fabricated by a plasma-enhanced chemical vapor deposition process and applied to flexible electrodes of EDLC. The areal capacitance of the capacitor based on vertical graphene is 2.45 mF cm−2, which is much better even with solid electrolytes when compared to other reported vertical graphene capacitors. The capacitance also shows good flexibility and it remains unaltered even after 100 000 times of bending or 180 degree folding. These unique features of the capacitor could be ascribed to a discrete ‘tree-like’ morphology of the vertical graphene, which has not been known before.

In the title complex, [Cu(C13H8NOS)2], the CuIIatom is coordinated by two N atoms and two O atoms from two bidentate benzothiazolphenolate ligands, forming a distorted tetrahedral geometry [dihedral angle between two N—Cu—O planes: 45.1 (2)°]. The dihedral angles between the benzothiazole ring systems and the phenol rings are 4.1 (4) and 5.8 (4)°, indicating an almost planar geometry. Weak intra- and intermolecular C—H...O hydrogen bonds are observed. In the crystal, weak π–π interactions between aromatic and thiazole rings [centroid–centroid distances = 3.626 (3) and 3.873 (3) Å] link the molecules into a two-dimensional supramolecular network along thebcplane.

Photoluminescence (PL) of solution-processed graphene oxide (GO) was tuned from near-ultraviolet to blue region through controlled hydrazine (N2H4) reduction. The peak shifted from 341 nm to 421 nm as hydrazine concentration increased to 0.3 M. Further reduction of the GO increased the density of small sp2 nucleus and enhanced the interconnectivity of localized sp2 sites. It is deduced that hopping and tunneling effects facilitated the migration of excitons to nonradiative recombination sites, resulting in PL quenching and saturation of PL red-shift.

The title compound, C2H3N3OS, is a monoclinic (P21/c) polymorph of the previously reported triclinic structure [Kanget al.(2012).Acta Cryst.E68, o1198]. The asymmetric unit contains two independent molecules which are essentially planar, with r.m.s. deviations of 0.001 and 0.032 Å from the mean plane defined by the seven non-H atoms. In the crystal, N—H...N and N—H...O hydrogen bonds link the molecules into a sheet parallel to (111).

About 30 nm quantum-dot thin films are formed by electrospray deposition (ESD) process and quantum-dot-light-emitting-diodes (QD-LEDs) are demonstrated. Maximum brightness of 23 000 cd m(-2) and current efficiency of 5.9 cd A(-1) are achieved with the ESD process. The ESD process can be a potential solution for large area quantum dot layers with simple and flexible control.

The performance of quantum dot light-emitting diodes (QD-LEDs) was investigated for different hole transport layers with small molecules and polymers: poly(4-butyl-phenyl-diphenyl-amine), poly-N-vinylcarbazole (PVK), N,N'-diphenyl-N,N'-bis(3-methylphenyl)-1,1'-diphenyl-4,4'-diamine, 4,4',4″-tris(N-carbazolyl)-triphenyl-amine (TCTA), and 4,4'-bis(carbazole-9-yl)biphenyl (CBP). The electroluminescence performance of QD-LEDs was considerably improved by adding small molecules (TCTA or CBP) having high hole mobilily to the polymer hole transport material (PVK). The maximal current efficiency of QD-LED-based PVK was improved by 27% upon addition of 20 wt % TCTA due to the hole injection improvement. The lower turn-on voltage, the higher current density, and the higher luminance were achieved by addition of TCTA. The maximal luminance of 40900 cd/m(2) and the highest current efficiency of 14.0 cd/A with the narrow full width at half-maximum (35 nm) were achieved by the best hole transport layer.

In the complex cation of the title salt, [CrCl(C2H6OS)5]Cl2, the CrIIIion is coordinated by one chloride ligand and five O atoms from dimethyl sulfoxide (DMSO) ligands, leading to a slightly distorted octahedral coordination environment [O—Cr—O angles range from 86.69 (16) to 92.87 (16)°]. In the crystal, complex cations are arranged in hexagonally packed rows parallel to [010], with the chloride counter-anions situated in between. The interactions between cations and anions are mainly ionic in nature.

The title compound, C9H9N3O2S, is a monoclinic (C2/c) polymorph of the previously reported triclinic structure [Kang (2013).Acta Cryst.E69, o1327]. The molecule is almost planar with an r.m.s. deviation of 0.069 Å from the mean plane of all non-H atoms. The benzoyl and terminal thiourea fragments adopt atransoidconformation with respect to the central carbonyl O atom. Two intramolecular N—H...O hydrogen bonds are present. In the crystal, N—H...O and N—H...S interactions link the molecules into zigzag chains extending along thec-axis direction.

High quality graphene was obtained though microwave irradiated expansion following a solution process. This method is facile, inexpensive, and produces usable results. Ultrathin, uniform graphene films were fabricated at room temperature by a vacuum filtration method. Combining carbon nanotubes (CNTs) as bridges between graphene flakes allowed the fabrication of high-performance conductive films for flexible applications, with conductivities and optical properties comparable to commercial ITO: 181 Omega sq(-1) at 82.2% transmittance after chemical treatment and doping. With future work, this versatile material could well provide an appropriate transparent electrode for flexible optical electronics.

A high current density obtained in a limited, nanometer-thick region is important for high efficiency polymer solar cells (PSCs). The conversion of incident photons to charge carriers only occurs in confined active layers; therefore, charge-carrier extraction from the active layer within the device by using solar light has an important impact on the current density and the related to power conversion efficiency. In this study, we observed a surprising result, that is, extracting the charge carrier generated in the active layer of a PSC device, with a thickness-controlled PEDOT:PSS bilayer that acted as a hole extraction layer (HEL), yielded a dramatically improved power conversion efficiency in two different model systems (P3HT:PC₆₀BM and PCDTBT:PC₇₀BM). To understand this phenomenon, we conducted optical strength simulation, photocurrent-voltage measurements, incident photon to charge carrier efficiency measurements, ultraviolet photoelectron spectroscopy, and AFM studies. The results revealed that approximately 60 nm was the optimum PEDOT:PSS bilayer HEL thickness in PSCs for producing the maximum power conversion efficiency.