Research on Alloy Design and Process Optimization of Al–Mg–Zn-Cu-Based Aluminum Alloy Sheets for Automobiles with Secured Formability and Bake-Hardenability

In this study, the compositional design of high-formability, high-bake-hardening Al–Mg–Zn-Cu-based aluminum alloys was carried out, and process conditions were established to secure mechanical properties under harsh conditions for Al–Mg–Zn-Cu-based alloys. Using JMatPro13.0 for precipitation phase simulation, the optimal pre-aging temperature and time of the design composition were selected. Through the introduction of pre-aging, it was confirmed that no over-aging phenomena occurred, even after bake-hardening, and it was confirmed that it could have mechanical properties similar to those of test specimens subjected to traditional heat treatment. Through DSC (Differential Scanning Calorimetry) and TEM (Transmission Electron Microscope) analyses, it was found that pre-aging provided sufficient thermal stability to the GP (Guinier–Preston) zone and facilitated transformation to the η’-phase. In addition, it was confirmed that, even under bake-hardening conditions, coarsening of the precipitation phase was prevented and number density was increased, thereby contributing to improvements in the mechanical properties. The designed alloy plate was evaluated as having excellent anisotropy properties through n-value and r¯-value calculations, and it was confirmed that a similar level of formability was secured through FLC (Forming Limit Curve) comparison with commercial plates

Exploration of Effective Attention Strategies for Neural Automatic Post-editing with Transformer

Automatic post-editing (APE) is the study of correcting translation errors in the output of an unknown machine translation (MT) system and has been considered as a method of improving translation quality without any modification to conventional MT systems. Recently, several variants of Transformer that take both the MT output and its corresponding source sentence as inputs have been proposed for APE; and models introducing an additional attention layer into the encoder to jointly encode the MT output with its source sentence recorded a high-rank in the WMT19 APE shared task. We examine the effectiveness of such joint-encoding strategy in a controlled environment and compare four types of decoder multi-source attention strategies that have been introduced into previous APE models. The experimental results indicate that the joint-encoding strategy is effective and that taking the final encoded representation of the source sentence is the more proper strategy than taking such representation within the same encoder stack. Furthermore, among the multi-source attention strategies combined with the joint-encoding, the strategy that applies attention to the concatenated input representation and the strategy that adds up the individual attention to each input improve the quality of APE results over the strategy using the joint-encoding only.11Nsciescopu