The Guidelines of Material Design and Process Control on Hybrid Fiber Metal Laminate for Aircraft Structures

Fiber metal laminate (FML) is a hybrid material system that consists of thin metal sheets bonded into a laminate with intermediate thin fiber reinforced composite layers. The aerospace industry has recently increased their use of FMLs due to the considerable weight reduction and consequent benefits for critical load-carrying locations in commercial aircraft, such as upper fuselage skin panels. All FML materials and their processes should be qualified through enough tests and fabrication trials to demonstrate reproducible and reliable design criteria. In particular, proper surface treatment technologies are prerequisite for achieving long-term service capability through the adhesive bonding process. This chapter introduces a brief overview of design concept, material properties and process control methodologies to provide detailed background information with engineering practices and to help ensure stringent quality controls and substantiation of structure integrity. The guidelines and information found in this chapter are meant to be a documentation of current knowledge and an application of sound engineering principles to the FML part development for aerospace usage

trans-Dichloridobis(2,2-dimethyl­prop­ane-1,3-diamine-κ2 N,N′)chromium(III) perchlorate

In the title salt, [CrCl2(C5H14N2)2]ClO4, the Cr atom is in a trans-CrCl2N4 octa­hedral environment comprising the four N atoms of two chelating 2,2-dimethyl­propane-1,3-diamine ligands and two Cl atoms. The two six-membered CrC3N2 rings in the cation adopt anti chair–chair conformations with respect to each other. The perchlorate anion is disordered over two positions in respect of the Cl and an O atom in a 6:4 ratio. N—H⋯O hydrogen bonds link the cations and anions into a layer structure

Comparison of Adsorptive Removal of Total Nitrogen (T-N) and Total Phosphorous (T-P) in Aqueous Solution using Granular Activated Charcoal (GAC)

The present study is to explore the possibility of utilizing granular activated charcoal (GAC) for the removal of total phosphorous (T-P) and total nitrogen (T-N) in aqueous solution. Batch adsorption studies were carried out to determine the influences of various factors like initial concentration, contact time and temperature. The adsorption data showed that GAC has a similar adsorption capacity for both T-N and T-P. The adsorption degree of T-N and T-P on GAC was highly concentration dependent. It was found that the adsorption capacity of GAC is quite favorable at a low concentration. At concentrations of 1.0 mg L-1 of T-P and 2.0 mg L-1 of T-N, approximately 97 % of adsorption was achieved by GAC. The equilibrium data were fitted well to the Langmuir isotherm model. The pseudo-second-order kinetic model appeared to be the better-fitting model because it has higher R2 compared with the pseudo-first-order and intra-particle kinetic model. The theoretical adsorption equilibrium qe,cal from pseudo-second-order kinetic model were relatively similar to the experimental adsorption equilibrium qe,exp. To evaluate the effect of thermodynamic parameters at different temperatures, the change in free energy ΔG, the enthalpy ΔH and the entropy ΔS were estimated. Except for adsorption of T-P at 278 K, the ΔG values obtained were all negative at the investigated temperatures. It indicates that the present adsorption system occurs spontaneously. The adsorption process of T-N by GAC was exothermic in nature, whereas T-P showed endothermic behavior. In addition, the positive values of ΔS imply that there was the increase in the randomness of adsorption of T-N and T-P at GAC-solution interface. Â

Utilizing ChatGPT in clinical research related to anesthesiology: a comprehensive review of opportunities and limitations

Chat generative pre-trained transformer (ChatGPT) is a chatbot developed by OpenAI that answers questions in a human-like manner. ChatGPT is a GPT language model that understands and responds to natural language created using a transformer, which is a new artificial neural network algorithm first introduced by Google in 2017. ChatGPT can be used to identify research topics and proofread English writing and R scripts to improve work efficiency and optimize time. Attempts to actively utilize generative artificial intelligence (AI) are expected to continue in clinical settings. However, ChatGPT still has many limitations for widespread use in clinical research, owing to AI hallucination symptoms and its training data constraints. Researchers recommend avoiding scientific writing using ChatGPT in many traditional journals because of the current lack of originality guidelines and plagiarism of content generated by ChatGPT. Further regulations and discussions on these topics are expected in the future

Automated Brittle Fracture Rate Estimator for Steel Property Evaluation Using Deep Learning After Drop-Weight Tear Test

This study proposes an automated brittle fracture rate (BFR) estimator using deep learning. As the demand for line-pipes increases in various industries, the need for BFR estimation through dropweight tear test (DWTT) increases to evaluate steel's property. Conventional BFR or ductile fracture rate (DFR) estimation methods require an expensive 3D scanner. Alternatively, a rule-based approach is used with a single charge-coupled device (CCD) camera. However, it is sensitive to the hyper-parameter. To solve these problems, we propose an approach based on deep learning that has recently been successful in the fields of computer vision and image processing. The method proposed in this study is the first to use deep learning approach for BFR estimation. The proposed method consists of a VGG-based U-Net (VU-Net) which is inspired by U-Net and fully convolutional network (FCN). VU-Net includes a deep encoder and a decoder. The encoder is adopted from VGG19 and transferred with a pre-trained model with ImageNet. In addition, the structure of the decoder is the same as that of the encoder, and the decoder uses the feature maps of the encoder through concatenation operation to compensate for the reduced spatial information. To analyze the proposed VU-Net, we experimented with different depths of networks and various transfer learning approaches. In terms of accuracy used in real industrial application, we compared the proposed VU-Net with U-Net and FCN to evaluate the performance. The experiments showed that VU-Net was the accuracy of approximately 94.9 %, and was better than the other two, which had the accuracies of about 91.8 % and 93.7 %, respectively.11Ysciescopu