Failure Mode and Ductility of Dual Phase Steel with Edge Crack

AbstractDual phase steels having a microstructure consisting of a ferrite matrix, in which particles of martensite are dispersed, have received a great deal of attention due to their useful combination of high strength, high work hardening rate and ductility, all of which are favorable properties for forming processes. In the present work, various microstructure-level finite element models are generated based on the actual microstructure of DP590 steel, to capture the mechanical behavior and fracture mode. The failure mode of DP steels is predicted using the plastic strain localization theory, mainly resulting from the material microstructure-level inhomogeneity as well as the initial geometrical imperfection. Besides the simulation, tensile test specimens of dog bone type with different edge cracks were prepared on an internally designed blanking tool, and the corresponding deformation processes were recorded via digital image correlation system. It is found that the overall ductility of the DP590 steel strongly depends on the ductility of the ferrite matrix, and pre-existing edge cracks reduce the overall ductility of the steel and change the failure mode

CAT-LLM: Prompting Large Language Models with Text Style Definition for Chinese Article-style Transfer

Text style transfer is increasingly prominent in online entertainment and social media. However, existing research mainly concentrates on style transfer within individual English sentences, while ignoring the complexity of long Chinese texts, which limits the wider applicability of style transfer in digital media realm. To bridge this gap, we propose a Chinese Article-style Transfer framework (CAT-LLM), leveraging the capabilities of Large Language Models (LLMs). CAT-LLM incorporates a bespoke, pluggable Text Style Definition (TSD) module aimed at comprehensively analyzing text features in articles, prompting LLMs to efficiently transfer Chinese article-style. The TSD module integrates a series of machine learning algorithms to analyze article-style from both words and sentences levels, thereby aiding LLMs thoroughly grasp the target style without compromising the integrity of the original text. In addition, this module supports dynamic expansion of internal style trees, showcasing robust compatibility and allowing flexible optimization in subsequent research. Moreover, we select five Chinese articles with distinct styles and create five parallel datasets using ChatGPT, enhancing the models' performance evaluation accuracy and establishing a novel paradigm for evaluating subsequent research on article-style transfer. Extensive experimental results affirm that CAT-LLM outperforms current research in terms of transfer accuracy and content preservation, and has remarkable applicability to various types of LLMs.Comment: 9 page

Thermal-Mechanical Properties of Polyurethane-Clay Shape Memory Polymer Nanocomposites

Shape memory nanocomposites of polyurethane (PU)-clay were fabricated by melt mixing of PU and nano-clay. Based on nano-indentation and microhardness tests, the strength of the nanocomposites increased dramatically as a function of clay content, which is attributed to the enhanced nanoclay–polymer interactions. Thermal mechanical experiments demonstrated good mechanical and shape memory effects of the nanocomposites. Full shape memory recovery was displayed by both the pure PU and PU-clay nanocomposites.