Dual Bypass Gas Metal Arc Welding Process and Control

GMAW (Gas Metal Arc Welding) is one of the most important arc welding processes being adopted in modern manufacturing industry due to its advantages in productivity, energy efficiency and automation. By monitoring and improving some of the important properties of GMAW such as production rate, metal transfer and base metal heat input, researchers could bring the process efficiency and stability to a new level. In recent years, some innovative modifications of GMAW such as Twins, Tandem and laser-MIG hybrid welding have been adopted into many industrial applications for better productivity. In this dissertation, a novel GMAW called DB-GMAW (Dual Bypass Gas Metal Arc Welding) using two GTAW torches and one GMAW torch to construct a welding system, is proposed and developed. In DB-GMAW, two GTAW torches perform the bypass system which decouples the total welding current into base metal current and bypass current after the melt down of filler wire. Compared to conventional GMAW, DB-GMAW has many advantages in droplet formation, base metal heat input and penetration achievement due to its unique characteristics in welding arc and current flow. In the first place of the research, experimental system of DB-GMAW is constructed. Then, sufficient experiments under different parameters are performed to provide us a good understanding of the behaviors and characteristics of this novel GMAW process. Observation about metal transfer formation and base metal heat input is studied to verify its theoretical analysis. Full penetration of work piece via DB-GMAW is achieved based on a series of parameter testing experiments. Moreover, image processing techniques are applied to DB-GMAW to monitor the welding process and construct a feedback system for control. Considering the importance of maintaining stable full penetration during many welding applications, a nonlinear model of DB-GMAW full penetration is developed in this dissertation. To do that, we use machine vision techniques to monitor the welding profile of the work piece. A control algorithm based on the nonlinear model using adaptive control technique is also designed. The achievement of this dissertation provides a fundamental knowledge of a novel welding process: DB-GMAW, and a good guidance for further studies about DBGMAW

Recent decadal change in the North Atlantic subtropical underwater associated with the poleward expansion of the surface salinity maximum

Author Posting. © American Geophysical Union, 2019. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research-Oceans 124(7), (2019): 4433-4448, doi: 10.1029/2018JC014508.Yu et al. (2017, https://doi.org/10.1002/2017GL075772) reported that the annual mean sea surface salinity maximum (SSS‐max) in the North Atlantic expanded northward by 0.35 ± 0.11° per decade over the 34‐year data record (1979–2012). The expansion shifted and expanded the ventilation zone northward and increased the production of the Subtropical Underwater (STUW). As a result, the STUW became deeper, thicker, and saltier. In this study, the seasonal characteristics of the poleward expansion of the North Atlantic SSS‐max and their effects on the STUW are examined. The results show that the SSS‐max expansion occurred primarily during boreal spring (April, May, and June) and expanded northward by 0.43 ± 0.21° per decade over the 34‐year period. The annual volume of the STUW increased by 0.21 ± 0.09 1014 m3 per decade over the same period, and the spring (April, May, and June) volume increased by 0.31 ± 0.02 1014 m3 per decade (a relative increase of 48 ± 1%). The characteristics of the decadal changes in STUW were attributable to the increased subduction rate associated with the northward expansion of the SSS‐max. The annual subduction rate increased by 0.29 ± 0.07 Sv per decade over the 34 years, and the greatest increase of 1.73 ± 0.61 Sv per decade occurred in April. The change in subduction associated with the expansion of the SSS‐max appeared to be consistent with the Atlantic Multidecadal Oscillation.Most of the work was conducted at the Woods Hole Oceanographic Institution, while H. Liu was a guest student sponsored by the China Scholarship Council (201506330001). H. Liu thanks Drs. Ruixin Huang and Xiangze Jin for discussions on the computation of the STUW formation and subduction rates. The Ishii subsurface salinity and temperature analysis data sets were downloaded from https://rda.ucar.edu/datasets/ds285.3/. The EN4 data set is available at https://www.metoffice.gov.uk/hadobs/en4/download‐en4‐2‐1.html. The LEGOS SSS is accessible from http://www.legos.obs‐mip.fr/observations/sss/datadelivery/products.The OAFlux vector wind analysis is available at http://oaflux.whoi.edu. The NAO index was downloaded from https://www.ncdc.noaa.gov/teleconnections/nao/. The AMO index is available at https://www.esrl.noaa.gov/psd/data/timeseries/AMO/. X. Lin is supported by China's National Key Research and Development Projects (2016YFA0601803) in addition to the National Natural Science Foundation of China (41521091 and U1606402) and the Qingdao National Laboratory for Marine Science and Technology (2017ASKJ01).2019-12-1

DID-M3D: Decoupling Instance Depth for Monocular 3D Object Detection

Monocular 3D detection has drawn much attention from the community due to its low cost and setup simplicity. It takes an RGB image as input and predicts 3D boxes in the 3D space. The most challenging sub-task lies in the instance depth estimation. Previous works usually use a direct estimation method. However, in this paper we point out that the instance depth on the RGB image is non-intuitive. It is coupled by visual depth clues and instance attribute clues, making it hard to be directly learned in the network. Therefore, we propose to reformulate the instance depth to the combination of the instance visual surface depth (visual depth) and the instance attribute depth (attribute depth). The visual depth is related to objects' appearances and positions on the image. By contrast, the attribute depth relies on objects' inherent attributes, which are invariant to the object affine transformation on the image. Correspondingly, we decouple the 3D location uncertainty into visual depth uncertainty and attribute depth uncertainty. By combining different types of depths and associated uncertainties, we can obtain the final instance depth. Furthermore, data augmentation in monocular 3D detection is usually limited due to the physical nature, hindering the boost of performance. Based on the proposed instance depth disentanglement strategy, we can alleviate this problem. Evaluated on KITTI, our method achieves new state-of-the-art results, and extensive ablation studies validate the effectiveness of each component in our method. The codes are released at https://github.com/SPengLiang/DID-M3D.Comment: ECCV 202