Heat and Mass Transfer of Additive Manufacturing Processes for Metals

Additive manufacturing (AM), a method in which a part is fabricated layer by layer from a digital design package, provides the potential to produce complex components at reduced cost and time. Many techniques (using many different names) have been developed to accomplish this via melting or solid-state joining. However, to date, only a handful can be used to produce metallic parts that fulfill the requirements of industrial applications. The thermal physics and weld pool behaviors in metal AM process have decisive influence on the deposition quality, the microstructure and service performance of the depositions. Accurate analysis and calculation of thermal processes and weld pool behaviors are of great significance to the metallurgy analysis, stress and deformation analysis, process control and process optimization etc. Numerical modeling is also a necessary way to turn welding from qualitative description and experience-based art into quantitative analysis- and science-based engineering branch. In this chapter, two techniques for producing metal parts are explored, with a focus on the thermal science of metal AM: fluid flow and heat transfer. Selective laser melting (SLM) is the one that is most widely used because it typically has the best resolution. Another is named metal fused-coated additive manufacturing (MFCAM) that is cost competitive and efficient in producing large and middle-complex components in aerospace applications

Efficient Algorithms for Node Disjoint Subgraph Homeomorphism Determination

Recently, great efforts have been dedicated to researches on the management of large scale graph based data such as WWW, social networks, biological networks. In the study of graph based data management, node disjoint subgraph homeomorphism relation between graphs is more suitable than (sub)graph isomorphism in many cases, especially in those cases that node skipping and node mismatching are allowed. However, no efficient node disjoint subgraph homeomorphism determination (ndSHD) algorithms have been available. In this paper, we propose two computationally efficient ndSHD algorithms based on state spaces searching with backtracking, which employ many heuristics to prune the search spaces. Experimental results on synthetic data sets show that the proposed algorithms are efficient, require relative little time in most of the testing cases, can scale to large or dense graphs, and can accommodate to more complex fuzzy matching cases.Comment: 15 pages, 11 figures, submitted to DASFAA 200

Additive Manufacturing of Sn63Pb37 Component by Micro-coating

AbstractMicro-coating is a novel technology to build near-net component layer by layer, which uses a crucible and nozzle instead of a weld head and wire feeder to supply material compared with shaped metal deposition. A pneumatic system is adopted to adjust liquid metal flow rate and the layer height is controlled by the distance between nozzle and substrate. Height and width of a single channel are measured by confocal microscopy, it is found that the error between numerical results and experiment are 5.5% and 1.1%. Tensile stress vertically to the deposition layers reaches to 40.89Mpa, while tensile stress parallel to the deposition layers gives a value of 43.14Mpa. Yield stress of vertically and parallel to the layer are respectively 34.28Mpa and 35.23Mpa. Specimens exhibit better mechanical properties than casting component, whose tensile stress and yield stress are respectively 36.51Mpa and 29.25Mpa

Improved online sequential extreme learning machine for simulation of daily reference evapotranspiration

Yubin, Z., Zhengying, W., Lei, Z., Qinyin, L., & Jun, D. (March-April, 2017). Improved online sequential extreme learning machine for simulation of daily reference evapotranspiration. Water Technology and Sciences (in Spanish), 8(2), 127-140. The traditional extreme learning machine has significant disadvantages, including slow training, difficulty in selecting parameters, and difficulty in setting the singularity and the data sample. A prediction model of an improved Online Sequential Extreme Learning Machine (IOS-ELM) of daily reference crop evapotranspiration is therefore examined in this paper. The different manipulation of the inverse of the matrix is made according to the optimal solution and using a regularization factor at the same time in the model. The flexibility of the IOS-ELM in ET0 modeling was assessed using the original meteorological data (Tmax, Tm, Tmin, n, Uh, RHm, φ, Z) of the years 1971–2014 in Yulin, Ankang, Hanzhong, and Xi’an of Shaanxi, China. Those eight parameters were used as the input, while the reference evapotranspiration values were the output. In addition, the ELM, LSSVM, Hargreaves, Priestley-Taylor, Mc Cloud and IOS-ELM models were tested against the FAO- 56 PM model by the performance criteria. The experimental results demonstrate that the performance of IOS-ELM was better than the ELM and LSSVM and significantly better than the other empirical models. Furthermore, when the total ET0 estimation of the models was compared by the relative error, the results of the intelligent algorithms were better than empirical models at rates lower than 5%, but the gross ET0 empirical models mainly had 12% to 64.60% relative error. This research could provide a reference to accurate ET0 estimation by meteorological data and give accurate predictions of crop water requirements, resulting in intelligent irrigation decisions in Shaanxi

The Effect of Roughness on the Nonlinear Flow in a Single Fracture with Sudden Aperture Change

AbstractAbrupt changes in aperture (sudden expansion and contraction) are commonly seen in naturally occurred or artificial single fractures. The relevant research mainly focuses on the changes in fluid properties caused by the sudden expansion of the aperture in smooth parallel fractures. To investigate the effects of roughness on the nonlinear flow properties in a single rough fracture with abruptly aperture change (SF-AC), the flow characteristics of the fractures under different Reynolds numbers Re (50~2000) are simulated by the turbulence k-ε steady-state modulus with the Naiver-Stokes equation. The results show that, in a rough SF-AC, the growth of the eddy and the flow path deflection of the mainstream zone are more obvious than those in a smooth SF-AC, and the discrepancies between the rough and smooth SF-ACs become even more obvious when the relative roughness and/or Re values become greater. The increase of the fracture roughness leads to the generation of more local eddies on the rough SF-ACs and enhances the flow path deflection in the sudden expansion fracture. The number of eddies increases with Re, and the size of eddy area increases linearly with Re at first. When Re reaches a value of 300-500, the growth rate of the eddy size slows down and then stabilizes. Groundwater flow in a rough SF-AC follows a clearly visible nonlinear (or non-Darcy) flow law other than the linear Darcy’s law. The Forchheimer equation fits the hydraulic gradient-velocity (J-v) better than the linear Darcy’s law. The corresponding critical Re value at which the nonlinear flow starts to dominate in a rough SF-AC is around 300~500

FIMO: A Challenge Formal Dataset for Automated Theorem Proving

We present FIMO, an innovative dataset comprising formal mathematical problem statements sourced from the International Mathematical Olympiad (IMO) Shortlisted Problems. Designed to facilitate advanced automated theorem proving at the IMO level, FIMO is currently tailored for the Lean formal language. It comprises 149 formal problem statements, accompanied by both informal problem descriptions and their corresponding LaTeX-based informal proofs. Through initial experiments involving GPT-4, our findings underscore the existing limitations in current methodologies, indicating a substantial journey ahead before achieving satisfactory IMO-level automated theorem proving outcomes

Towards Automated Deep Learning: Analysis of the AutoDL challenge series 2019

International audienceWe present the design and results of recent competitions in Automated Deep Learning (AutoDL). In the AutoDL challenge series 2019, we organized 5 machine learning challenges: AutoCV, AutoCV2, AutoNLP, AutoSpeech and AutoDL. The first 4 challenges concern each a specific application domain, such as computer vision, natural language processing and speech recognition. At the time of March 2020, the last challenge AutoDL is still ongoing and we only present its design. 1 Some highlights of this work include: (1) a benchmark suite of baseline AutoML solutions, with emphasis on domains for which Deep Learning methods have had prior success (image, video, text, speech, etc); (2) a novel "anytime learning" framework, which opens doors for further theoretical consideration; (3) a repository of around 100 datasets (from all above domains) over half of which are released as public datasets to enable research on meta-learning; (4) analyses revealing that winning solutions generalize to new unseen datasets, validating progress towards universal AutoML 1. Its results will be presented in future work together with detailed introduction of winning solutions of each challenge

MnmE, a Central tRNA-Modifying GTPase, Is Essential for the Growth, Pathogenicity, and Arginine Metabolism of Streptococcus suis Serotype 2

Streptococcus suis is an important pathogen in pigs and can also cause severe infections in humans. However, little is known about proteins associated with cell growth and pathogenicity of S. suis. In this study, a guanosine triphosphatase (GTPase) MnmE homolog was identified in a Chinese isolate (SC19) that drives a tRNA modification reaction. A mnmE deletion strain (ΔmnmE) and a complementation strain (CΔmnmE) were constructed to systematically decode the characteristics and functions of MnmE both in vitro and in vivo studies via proteomic analysis. Phenotypic analysis revealed that the ΔmnmE strain displayed deficient growth, attenuated pathogenicity, and perturbation of the arginine metabolic pathway mediated by the arginine deiminase system (ADS). Consistently, tandem mass tag -based quantitative proteomics analysis confirmed that 365 proteins were differentially expressed (174 up- and 191 down-regulated) between strains ΔmnmE and SC19. Many proteins associated with DNA replication, cell division, and virulence were down-regulated. Particularly, the core enzymes of the ADS were significantly down-regulated in strain ΔmnmE. These data also provide putative molecular mechanisms for MnmE in cell growth and survival in an acidic environment. Therefore, we propose that MnmE, by its function as a central tRNA-modifying GTPase, is essential for cell growth, pathogenicity, as well as arginine metabolism of S. suis

Short Term Prediction Model of Environmental Parameters in Typical Solar Greenhouse Based on Deep Learning Neural Network

The type of single-slope solar greenhouse is mainly used for vegetable production in China. The coupling of heat storage and release courses and the dynamic change in the outdoor weather parameters momentarily affect the indoor environment. Due to the high cost of small weather stations, the environmental parameters monitored by the nearest meteorological stations are usually used as outdoor environmental parameters in China. In order to accurately predict the solar greenhouse and crop water demand, this paper proposes three deep learning models, including neural network regression (DNNR), long short-term memory (LSTM), and convolutional neural network-long- short-term memory (CNN-LSTM), and the hyperparameters of three models were determined by orthogonal experimental design (OD). The temperature and relative humidity monitored by the indoor sensors and outdoor weather station were taken as the inputs of models, the temperature and relative humidity 3, 6, 12 and 24 h in advance were taken as the output, 16 combinations of input and output data of two typical solar greenhouses were trained separately by three deep learning models, those models were trained 144, 144 and 288 times, respectively. The best model of three type models at four prediction time points were selected, respectively. For the forecast time point of 12 h in advance, the errors of the best LSTM and CNN-LSTM models in two greenhouses were all smaller than the DNNR models. For the three other time points, the results show that the DNNR models have excellent prediction accuracy among the three models. The maximum and minimum temperature, relative humidity, and ETo were also accurately predicted using the corresponding optimized models. In sum, this study provided an optimized deep learning prediction model for environmental parameters of greenhouse and provides technical support for irrigation decision-making and water allocation