END-TO-END PREDICTION OF WELD PENETRATION IN REAL TIME BASED ON DEEP LEARNING

Welding is an important joining technique that has been automated/robotized. In automated/robotic welding applications, however, the parameters are preset and are not adaptively adjusted to overcome unpredicted disturbances, which cause these applications to not be able to meet the standards from welding/manufacturing industry in terms of quality, efficiency, and individuality. Combining information sensing and processing with traditional welding techniques is a significant step toward revolutionizing the welding industry. In practical welding, the weld penetration as measured by the back-side bead width is a critical factor when determining the integrity of the weld produced. However, the back-side bead width is difficult to be directly monitored during manufacturing because it occurs underneath the surface of the welded workpiece. Therefore, predicting back-side bead width based on conveniently sensible information from the welding process is a fundamental issue in intelligent welding. Traditional research methods involve an indirect process that includes defining and extracting key characteristic information from the sensed data and building a model to predict the target information from the characteristic information. Due to a lack of feature information, the cumulative error of the extracted information and the complex sensing process directly affect prediction accuracy and real-time performance. An end-to-end, data-driven prediction system is proposed to predict the weld penetration status from top-side images during welding. In this method, a passive-vision sensing system with two cameras to simultaneously monitor the top-side and back-bead information is developed. Then the weld joints are classified into three classes (i.e., under penetration, desirable penetration, and excessive penetration) according to the back-bead width. Taking the weld pool-arc images as inputs and corresponding penetration statuses as labels, an end-to-end convolutional neural network (CNN) is designed and trained so the features are automatically defined and extracted. In order to increase accuracy and training speed, a transfer learning approach based on a residual neural network (ResNet) is developed. This ResNet-based model is pre-trained on an ImageNet dataset to process a better feature-extracting ability, and its fully connected layers are modified based on our own dataset. Our experiments show that this transfer learning approach can decrease training time and improve performance. Furthermore, this study proposes that the present weld pool-arc image is fused with two previous images that were acquired 1/6s and 2/6s earlier. The fused single image thus reflects the dynamic welding phenomena, and prediction accuracy is significantly improved with the image-sequence data by fusing temporal information to the input layer of the CNN (early fusion). Due to the critical role of weld penetration and the negligible impact on system implementation, this method represents major progress in the field of weld-penetration monitoring and is expected to provide more significant improvements during welding using pulsed current where the process becomes highly dynamic

The spatiotemporal variation and control mechanism of surface pCO2 in winter in Jiaozhou Bay, China

In many mid-latitude coastal waters during winter months, in addition to temperature, the large change in biogeochemical processes often influence and complicate the surface partial pressure of CO2 (pCO2). Based on the hydrological and carbonate parameters in seven cruises, this study analysed the evolution process and explored the control mechanism of the surface pCO2 in Jiaozhou Bay, China, from December to March. The results showed that the pCO2 ranged from 157 μatm to 647 μatm, and the bay represented a sink for atmospheric CO2 (-3.8 mmol m-2 d-1) in the whole winter. The non-temperature processes were the dominant factors affecting intra-winter pCO2 variation. In December, the bay was dominated by aerobic respiration and acted as a CO2 source (3.0 mmol m-2 d-1). From early January to late February, however, the vigorous growth of cold algae caused strong primary production, and the bay presented as a CO2 sink (from -6.4 mmol m-2 d-1 in early January to -15.5 mmol m-2 d-1 in late February). In March, primary production weakened and the effects of the CaCO3 precipitation appeared, and the strength of the CO2 sink was obviously weakened (-1.1 mmol m-2 d-1). Meanwhile, the water temperature decreased gradually from December to late January and then increased until March, and it further expanded the variation range of pCO2. Our results highlight the obvious source/sink change in mid-latitude seawater CO2 in winter, while more field observations are still needed to further understand the complicated biogeochemical processes and its influence on seawater pCO2

A multicast mechanism in WiMax mesh network

Abstract: The IEEE standard 802.16x based broadband wireless Lots of multicast routing protocols are proposed for ad-hoc access is a promising technology to provide wireless broadband wireless networks. One category is called tree-based protocols connectivity. Besides Point to Multi-Point mode, a mechanism to (e.g. MAODV, ABAM, ADMR[3,4,5]) running on the similar create multi-hop network through Mesh mode is defined for the network topology as WiMax mesh network, in which there frequency spectrum band 2-11 GHz. The standard only defines unicast transmission under mesh mode, but does not specify exists only a single path for a source-receiver pair. Tree based multicast transmission scheme. This paper proposes a novel multicast protocols can be further divided into two types: multicast mechanism in WiMax mesh network by building a high source-tree-based and share-tree-based. In source-tree-based efficient multicast tree with utilization of scheduling messages multicast protocols, the tree is rooted at the source and each defined in the standard. An easy-to-implement tree construction session has its own multicast tree, whereas in shared-treealgorithm is presented based on the adjustment of the centralized based multicast protocols, a single tree is shared by all the routing tree. Simulation results show the proposed multicast sources within the multicast group. However, because of mechanism improves transmission efficiency and reduces group frequent topology changing in ad-hoc network, flooding latency over the traditional way to fulfill multicast by multiple message exchanging will be generated during the process o