Enhancing the Mechanical Properties of Cu-Al-Ni Shape Memory Alloys Locally Reinforced by Alumina through the Powder Bed Fusion Process.

A classical problem with Cu-based shape memory alloys (SMAs) is brittle fracture at triple junctions. This alloy possesses a martensite structure at room temperature and usually comprises elongated variants. Previous studies have shown that introducing reinforcement into the matrix can refine grains and break martensite variants. Grain refinement diminishes brittle fracture at triple junctions, whereas breaking the martensite variants can negatively affect the shape memory effect (SME), owing to martensite stabilization. Furthermore, the additive element may coarsen the grains under certain circumstances if the material has a lower thermal conductivity than the matrix, even when a small amount is distributed in the composite. Powder bed fusion is a favorable approach that allows the creation of intricate structures. In this study, Cu-Al-Ni SMA samples were locally reinforced with alumina (Al2O3), which has excellent biocompatibility and inherent hardness. The reinforcement layer was composed of 0.3 and 0.9 wt% Al2O3 mixed with a Cu-Al-Ni matrix, deposited around the neutral plane within the built parts. Two different thicknesses of the deposited layers were investigated, revealing that the failure mode during compression was strongly influenced by the thickness and reinforcement content. The optimized failure mode led to an increase in fracture strain, and therefore, a better SME of the sample, which was locally reinforced by 0.3 wt% alumina under a thicker reinforcement layer.

Novel Approach toward the Forming Process of CFRP Reinforcement with a Hot Stamped Part by Prepreg Compression Molding.

The use of carbon fiber-reinforced plastics (CFRP) is markedly increasing, particularly for the manufacturing of automotive parts, to achieve better mechanical properties and a light weight. However, it is difficult to manufacture multi-material products because of the problems due to the adhesive between CFRP and steel. The prepreg compression molding (PCM) of laminated CFRP can reduce the production time and increase the flexibility of the manufacturing process. In this study, a new manufacturing process is proposed for CFRP reinforcement on a hot stamped B-pillar using PCM. A finite element (FE) simulation of the hot stamping process is conducted to predict the dimensions of the B-pillar. The feasibility of PCM manufacturing is explored by the simulation of the thermoforming of a CFRP set on a shaped B-pillar. The temperature conditions of the CFRP and B-pillar for the PCM are determined by considering the heat transfer between the CFRP and steel. Finally, the PCM of the B-pillar consisting of steel and CFRP was performed to compare with the analytical results for verification. The evaluation of the B-pillar was conducted by the observation of the cross-section for the B-pillar and interlayer by scanning electron microscopy (SEM). As a result, a steel/CFRP B-pillar assembly could be efficiently manufactured using the PCM process without an additional adhesive process.

Design of Multi-Stage Roll Die Forming Process for Drum Clutch with Artificial Neural Network.

The multi-stage roll die forming (RDF) process is a plastic forming process that can manufacture a transmission part with a complex shape, such as a drum clutch, by using a die set with rotational rolls. However, it is difficult to satisfy dimensional accuracy because of spring-back and unfilling. The purpose of this study is to design a multi-stage RDF process for the manufacturing of a drum clutch to improve dimensional accuracy using an artificial neural network (ANN). Finite element (FE) simulation of the multi-stage RDF process is performed to predict the dimensional accuracy according to various clearances for each stage. Moreover, the ANN is used to determine the relationship between the clearance and dimensional accuracy of the drum clutch to reduce the number of FE simulation. The results of the FE simulation and ANN are used to determine the optimal clearance for each stage of the RDF process. Finally, the drum clutch is fabricated using the determined conditions. The experimental results are in good agreement with the results of FE simulation from the aspect of outer diameter, inner diameter, thickness of outer tooth, thickness of inner tooth, and face thickness of tooth.

Simultaneous measurement of the surface shape and thickness for an optical flat with a wavelength-tuning Fizeau interferometer with suppression of drift error.

Two types of phase-shifting algorithms were developed for simultaneous measurement of the surface and thickness variation of an optical flat. During wavelength tuning, phase-shift nonlinearity can cause a spatially nonuniform error and spatially uniform DC drift error. A 19-sample algorithm was developed that eliminates the effect of the spatially uniform error by expanding the 17-sample algorithm with characteristic polynomial theory. The 19-sample algorithm was then altered to measure the surface shape of the optical flat by rotation of the characteristic diagram. The surface shape and thickness variation were measured with these two algorithms and a wavelength-tuning Fizeau interferometer.

Acquisition of Dynamic Material Properties in the Electrohydraulic Forming Process Using Artificial Neural Network.

Electrohydraulic forming is a high-velocity forming process that deforms sheet metals with velocities above 100 m/s and strain rates more than 100 s-1. This experiment was conducted in a closed space because of safety concerns related to the high-velocity conditions; therefore, we were not able to examine the deformation process of the sheet metal. To observe the electrohydraulic forming process in detail, we performed virtual numerical simulations using accurate material properties. Therefore, in this paper, we obtained the material property of a sheet metal from a numerical estimation by using a surrogate model based on the reduced order model and the artificial neural network. The Cowper-Symonds constitutive equation was selected for the Al 6061-T6 sheet metal, and two strain rate parameters were adopted as the unknown parameters. From the two sampling techniques, the training and test samples were extracted from the specific ranges of two unknown parameters, and a numerical simulation was performed for these samples by using the LS-DYNA program. The z-axis displacements of the deformed sheet metal were obtained from the results of the numerical simulation, and two basis vectors were extracted by using principal component analysis. In addition, to predict the weighting coefficients of the two basis vectors at the defined range of parameters, we used the artificial neural network technique as a surrogate model. By comparing the surrogate model and the experimental results and calculating the root mean square error value, we estimated the optimal parameter for Al 6061-T6. Finally, the reliability of the obtained material parameters was proved by comparing the experimental results, the surrogate model, and LS-DYNA.

Mercury distribution characteristics in primary manganese smelting plants.

The mercury (Hg) distribution characteristics were investigated in three primary manganese smelting plants in Korea for the assessment of anthropogenic Hg released. Input and output materials were sampled from each process, and Hg concentrations in the samples were analyzed. Among the input materials, the most mercury was found in the manganese ore (83.1-99.7%) and mercury was mainly released through fly ash or off gas, depending on the condition of off gas cleaning system. As off gas temperature decreases, proportion and concentration of emitted gaseous elemental mercury (Hg0) in off gas decreases. Based on mass balance study from these three plants and national manganese production data, the total amount of mercury released from those Korean plants was estimated to 644 kg/yr. About half of it was emitted into the air while the rest was released to waste as fly ash. With the results of this investigation, national inventory for Hg emission and release could be updated for the response to Minamata Convention on Mercury.

Emission of PCDD/Fs and dioxin-like PCBs from metallurgy industries in S. Korea.

The metallurgy industry and municipal waste incinerators are considered the main sources of polychlorinated dibenzo-p-dioxin and dibenzofurans (PCDD/Fs) in many countries. This study investigated the emission factors and total emissions of PCDD/Fs and dioxin-like polychlorinated biphenyls (PCBs) emitted from metallurgy industries (including ferrous and nonferrous foundries) in Korea. The toxic equivalency (TEQ) emission factor of PCDD/Fs was the highest for secondary copper production, at 24451 ng I-TEQ/ton. The total estimated emissions of PCDD/Fs from these sources were 35.259 g I-TEQ/yr, comprising 0.088 g I-TEQ/yr from ferrous foundries, 31.713 g I-TEQ/yr from copper production, 1.716 g I-TEQ/yr from lead production, 0.111 g I-TEQ/yr from zinc production, and 1.631 g I-TEQ/yr from aluminum production. The total estimated annual amounts of dioxin-like PCBs emitted from these sources were 13.260 g WHO-TEQ/yr, comprising 0.014 g WHO-TEQ/yr from ferrous foundries, 12.675 g WHO-TEQ/yr from copper production, 0.170 g WHO-TEQ/yr from lead production, 0.017 g WHO-TEQ/yr from zinc production, and 0.384 g WHO-TEQ/yr from aluminum production. The highest emission factor was found for secondary copper smelting, at 9770 ng WHO-TEQ/ton.