Crash Performance of Strength Gradient Tube Induced by Selective Laser Patterning.

This paper presents an investigation of the performance of a 22 MnB5 tube after local heat treatment according to a patterning shape under dynamic crash test conditions to propose the patterning shape with the best energy absorption efficiency. Numerical simulations support experimental results to validate the deformation mode during dynamic crash test as well as the strain distribution of the specimen. The helical patterning not only demonstrates the highest axial loading force and energy absorbance in both static and dynamic crash tests, but also can be easily fabricated in a short time. The helical pattern can optimize different pitch sizes according to the thickness and diameter of the cylindrical tube, and it has the highest energy absorption rate with 83.0% in dynamic conditions.

Effects of tetramethyl bisphenol F on thyroid and growth hormone-related endocrine systems in zebrafish larvae.

Trimethyl bisphenol F (TMBPF) has recently been used as a bisphenol A substitute in polymer coatings for metal cans containing beverages or food. This study investigated whether TMBPF disrupts the endocrine system associated with thyroid hormones and growth hormones employing zebrafish embryos and larvae. After 14 days of exposure, body weight was significantly reduced when zebrafish were exposed to a TMBPF concentration greater than 50 µg/L. The triiodothyronine levels were significantly increased, while growth hormone levels were significantly decreased in larvae exposed to 5 µg/L TMBPF. The transcription of genes associated with thyroid hormone production (trα, tpo, tg, and nis), deiodination (deio2), growth hormone production (gh1, ghrh, and ghra), and insulin-like growth factor (igf2a, igf2b, igf2r, igfbp1a, igfbp1b, igfbp2a, igfbp2b, and igfbp5a) was significantly upregulated, whereas the transcription of genes association with thyrotropin-releasing hormone (trh and trhr1) was significantly downregulated. These results suggest that hyperthyroidism, decrease in growth hormone, and regulation of genes involved in the hypothalamus-pituitary-thyroid and growth hormone/insulin-like growth factor might be responsible for the observed growth inhibition in larvae exposed to TMBPF. The bioaccumulation of TMBPF and its effects on the endocrine system after chronic exposure requires further investigation.

Artificial Neural Networks for Predicting Plastic Anisotropy of Sheet Metals Based on Indentation Test.

This paper mainly proposes two kinds of artificial neural network (ANN) models for predicting the plastic anisotropy properties of sheet metal using spherical indentation test, which minimizes measurement time, costs, and simplifies the process of obtaining the anisotropy properties than the conventional tensile test. The proposed ANN models for predicting anisotropic properties can replace the traditional complex dimensionless analysis. Moreover, this paper is not limited to the prediction of yield strength anisotropy but also further accurately predicts the Lankford coefficient in different orientations. We newly construct an FE spherical indentation model, which is suitable for sheet metal in consideration of actual compliance. To obtain a large dataset for training the ANN, the constructed FE model is utilized to simulate pure and alloyed engineering metals with one thousand elastoplastic parameter conditions. We suggest the specific variables of the residual indentation mark as input parameters, also with the indentation load-depth curve. The profile of the residual indentation, including the height and length in different orientations, are used to analyze the anisotropic properties of the material. Experimental validations have been conducted with three different sheet alloys, TRIP1180 steel, zinc alloy, and aluminum alloy 6063-T6, comparing the proposed ANN model and the uniaxial tensile test. In addition, machine vision was used to efficiently analyze the residual indentation marks and automatically measure the indentation profiles in different orientations. The proposed ANN model exhibits remarkable performance in the prediction of the flow curves and Lankford coefficient of different orientations.