Planning of circlelocus for multi-path/multi-layer welding robot with automatical error-correction

In this paper, a planning algorithm for multi path/multi layer circular locus is poposed. The algorithm is applied to weld the nipples on the header of boiler. Multi path/multi layer circular locus is planned according to three teaching points, which is lapped head on end to satisfy the requirement of technology. For the nipples wherever they are arranged radially or axially, even if there are errors caused by positioning and thermal deformations, providing that nipple's position and orientation relative to the teaching one can be measured, the multi path/multi layer circular locus can be planned without teaching any more. The algorithm has been applied in welding robot for manufacturing power station' boiler.

Optimization Design of the Multi-Layer Cross-Sectional Layout of An Umbilical Based on the GA-GLM

Marine umbilical is one of the key equipment for subsea oil and gas exploitation,which is usually integrated by a great number of different functional components with multi-layers.The layout of these components directly affects manufacturing,operation and storage performances of the umbilical.For the multi-layer cross-sectional layout design of the umbilical,a quantifiable multi-objective optimization model is established according to the operation and storage requirements.Considering the manufacturing factors,the multi-layering strategy based on contact point identification is introduced for a great number of functional components.Then,the GA-GLM global optimization algorithm is proposed combining the genetic algorithm and the generalized multiplier method,and the selection operator of the genetic algorithm is improved based on the steepest descent method.Genetic algorithm is used to find the optimal solution in the global space,which can converge from any initial layout to the feasible layout solution.The feasible layout solution is taken as the initial value of the generalized multiplier method for fast and accurate solution.Finally,taking umbilicals with a great number of components as examples,the results show that the cross-sectional performance of the umbilical obtained by optimization algorithm is better and the solution efficiency is higher.Meanwhile,the multi-layering strategy is effective and feasible.The design method proposed in this paper can quickly obtain the optimal multi-layer cross-sectional layout,which replaces the manual design,and provides useful reference and guidance for the umbilical industry.

Microstructure and impact mechanical properties of multi-layer and multi-pass TIG welded joints of Al-Zn-Mg alloy plates

The microstructure and mechanical properties of multi-layer multi-pass TIG welded joints of Al-Zn-Mg alloy plates were studied.The phase constituent and microstructure of different regions of the welded joints were characterized by scanning electron microscopy(SEM),X-ray diffraction(XRD),transmission electron microscopy(TEM)and energy disperse spectrum(EDS),while the mechanical properties were evaluated according to the impact test.A dispersively distributed spherical and needle-likeη(MgZn2)phase was obtained in the welding seam.The phase composition of the heat-affected zone(HAZ)wasα(Al)+η(MgZn2)+Al6Mn,and there were a large number of dispersively precipitated nanoscale particles.The welded joint zone had the highest impact toughness as compared with the other parts of the joint.The MgZn2 phase in the weld zone contributed to the improved toughness of the joint.Al2 MgCu phase in HAZ was proven to act as a crack source during fracture.

Multi-layer perceptron-based data-driven multiscale modelling of granular materials with a novel Frobenius norm-based internal variable

One objective of developing machine learning(ML)-based material models is to integrate them with well-established numerical methods to solve boundary value problems(BVPs).In the family of ML models,recurrent neural networks(RNNs)have been extensively applied to capture history-dependent constitutive responses of granular materials,but these multiple-step-based neural networks are neither sufficiently efficient nor aligned with the standard finite element method(FEM).Single-step-based neural networks like the multi-layer perceptron(MLP)are an alternative to bypass the above issues but have to introduce some internal variables to encode complex loading histories.In this work,one novel Frobenius norm-based internal variable,together with the Fourier layer and residual architectureenhanced MLP model,is crafted to replicate the history-dependent constitutive features of representative volume element(RVE)for granular materials.The obtained ML models are then seamlessly embedded into the FEM to solve the BVP of a biaxial compression case and a rigid strip footing case.The obtained solutions are comparable to results from the FEM-DEM multiscale modelling but achieve significantly improved efficiency.The results demonstrate the applicability of the proposed internal variable in enabling MLP to capture highly nonlinear constitutive responses of granular materials.

The comparison of multi-layer narrow-gap laser and arc welding of S32101 duplex stainless steel

Multi-layer narrow-gap welding of thick S32101 duplex stainless steel was conducted using laser welding with beam wobble process.The phase transition,grain size,phase proportion and crystal texture of welded joint were also studied and compared with gas metal arc welding process.The microhardness and tensile strength were measured and fracture surface was analyzed to evaluate the mechanical properties of welded joints.The results showed that beam wobble technology improved the misalignment of laser beam and filler wire in narrow groove and helped to avoid incomplete fusion defects.Compared to arc welding process,the groove size and heat input were reduced,while welding efficiency was increased.The faster cooling rate and lower temperature gradient of laser wobble welding favored grain refinement,while the austenite content in weld zone decreased.Both the beam wobble and swing arc were conducive to stir weld pool,optimizing the weld microstructure and joint formation.The microstructural variance in various weld passes was caused by the heat input and heat dissipation ability.The microhardness of laser welded joint was lower,while the tensile strength and elongation percentage were higher.The fracture surface of arc welded joint was featured with shallower dimples and cleavage steps.

Target Controllability of Multi-Layer Networks With High-Dimensional Nodes

This paper studies the target controllability of multilayer complex networked systems,in which the nodes are highdimensional linear time invariant(LTI)dynamical systems,and the network topology is directed and weighted.The influence of inter-layer couplings on the target controllability of multi-layer networks is discussed.It is found that even if there exists a layer which is not target controllable,the entire multi-layer network can still be target controllable due to the inter-layer couplings.For the multi-layer networks with general structure,a necessary and sufficient condition for target controllability is given by establishing the relationship between uncontrollable subspace and output matrix.By the derived condition,it can be found that the system may be target controllable even if it is not state controllable.On this basis,two corollaries are derived,which clarify the relationship between target controllability,state controllability and output controllability.For the multi-layer networks where the inter-layer couplings are directed chains and directed stars,sufficient conditions for target controllability of networked systems are given,respectively.These conditions are easier to verify than the classic criterion.

A flexible ultra-broadband multi-layered absorber working at 2 GHz-40 GHz printed by resistive ink

A flexible extra broadband metamaterial absorber(MMA)stacked with five layers working at 2 GHz–40 GHz is investigated.Each layer is composed of polyvinyl chloride(PVC),polyimide(PI),and a frequency selective surface(FSS),which is printed on PI using conductive ink.To investigate this absorber,both one-dimensional analogous circuit analysis and three-dimensional full-wave simulation based on a physical model are provided.Various crucial electromagnetic properties,such as absorption,effective impedance,complex permittivity and permeability,electric current distribution and magnetic field distribution at resonant peak points,are studied in detail.Analysis shows that the working frequency of this absorber covers entire S,C,X,Ku,K and Ka bands with a minimum thickness of 0.098λ_(max)(λ_(max) is the maximum wavelength in the absorption band),and the fractional bandwidth(FBW)reaches 181.1%.Moreover,the reflection coefficient is less than-10 dB at 1.998 GHz–40.056 GHz at normal incidence,and the absorptivity of the plane wave is greater than 80%when the incident angle is smaller than 50°.Furthermore,the proposed absorber is experimentally validated,and the experimental results show good agreement with the simulation results,which demonstrates the potential applicability of this absorber at 2 GHz–40 GHz.

Dynamic Multi-Layer Perceptron for Fetal Health Classification Using Cardiotocography Data

Fetal health care is vital in ensuring the health of pregnant women and the fetus.Regular check-ups need to be taken by the mother to determine the status of the fetus’growth and identify any potential problems.To know the status of the fetus,doctors monitor blood reports,Ultrasounds,cardiotocography(CTG)data,etc.Still,in this research,we have considered CTG data,which provides information on heart rate and uterine contractions during pregnancy.Several researchers have proposed various methods for classifying the status of fetus growth.Manual processing of CTG data is time-consuming and unreliable.So,automated tools should be used to classify fetal health.This study proposes a novel neural network-based architecture,the Dynamic Multi-Layer Perceptron model,evaluated from a single layer to several layers to classify fetal health.Various strategies were applied,including pre-processing data using techniques like Balancing,Scaling,Normalization hyperparameter tuning,batch normalization,early stopping,etc.,to enhance the model’s performance.A comparative analysis of the proposed method is done against the traditional machine learning models to showcase its accuracy(97%).An ablation study without any pre-processing techniques is also illustrated.This study easily provides valuable interpretations for healthcare professionals in the decision-making process.

Multi-layer network embedding on scc-based network with motif

Interconnection of all things challenges the traditional communication methods,and Semantic Communication and Computing(SCC)will become new solutions.It is a challenging task to accurately detect,extract,and represent semantic information in the research of SCC-based networks.In previous research,researchers usually use convolution to extract the feature information of a graph and perform the corresponding task of node classification.However,the content of semantic information is quite complex.Although graph convolutional neural networks provide an effective solution for node classification tasks,due to their limitations in representing multiple relational patterns and not recognizing and analyzing higher-order local structures,the extracted feature information is subject to varying degrees of loss.Therefore,this paper extends from a single-layer topology network to a multi-layer heterogeneous topology network.The Bidirectional Encoder Representations from Transformers(BERT)training word vector is introduced to extract the semantic features in the network,and the existing graph neural network is improved by combining the higher-order local feature module of the network model representation network.A multi-layer network embedding algorithm on SCC-based networks with motifs is proposed to complete the task of end-to-end node classification.We verify the effectiveness of the algorithm on a real multi-layer heterogeneous network.

Dynamic interwell connectivity analysis of multi-layer waterflooding reservoirs based on an improved graph neural network

The analysis of interwell connectivity plays an important role in the formulation of oilfield development plans and the description of residual oil distribution. In fact, sandstone reservoirs in China's onshore oilfields generally have the characteristics of thin and many layers, so multi-layer joint production is usually adopted. It remains a challenge to ensure the accuracy of splitting and dynamic connectivity in each layer of the injection-production wells with limited field data. The three-dimensional well pattern of multi-layer reservoir and the relationship between injection-production wells can be equivalent to a directional heterogeneous graph. In this paper, an improved graph neural network is proposed to construct an interacting process mimics the real interwell flow regularity. In detail, this method is used to split injection and production rates by combining permeability, porosity and effective thickness, and to invert the dynamic connectivity in each layer of the injection-production wells by attention mechanism.Based on the material balance and physical information, the overall connectivity from the injection wells,through the water injection layers to the production layers and the output of final production wells is established. Meanwhile, the change of well pattern caused by perforation, plugging and switching of wells at different times is achieved by updated graph structure in spatial and temporal ways. The effectiveness of the method is verified by a combination of reservoir numerical simulation examples and field example. The method corresponds to the actual situation of the reservoir, has wide adaptability and low cost, has good practical value, and provides a reference for adjusting the injection-production relationship of the reservoir and the development of the remaining oil.

Target layer state estimation in multi-layer complex dynamical networks considering nonlinear node dynamics

In many engineering networks, only a part of target state variables are required to be estimated.On the other hand,multi-layer complex network exists widely in practical situations.In this paper, the state estimation of target state variables in multi-layer complex dynamical networks with nonlinear node dynamics is studied.A suitable functional state observer is constructed with the limited measurement.The parameters of the designed functional observer are obtained from the algebraic method and the stability of the functional observer is proven by the Lyapunov theorem.Some necessary conditions that need to be satisfied for the design of the functional state observer are obtained.Different from previous studies, in the multi-layer complex dynamical network with nonlinear node dynamics, the proposed method can estimate the state of target variables on some layers directly instead of estimating all the individual states.Thus, it can greatly reduce the placement of observers and computational cost.Numerical simulations with the three-layer complex dynamical network composed of three-dimensional nonlinear dynamical nodes are developed to verify the effectiveness of the method.

A Well Productivity Model for Multi-Layered Marine and Continental Transitional Reservoirs with Complex Fracture Networks

Using the typical characteristics of multi-layered marine and continental transitional gas reservoirs as a basis,a model is developed to predict the related well production rate.This model relies on the fractal theory of tortuous capillary bundles and can take into account multiple gas flow mechanisms at the micrometer and nanometer scales,as well as the flow characteristics in different types of thin layers(tight sandstone gas,shale gas,and coalbed gas).Moreover,a source-sink function concept and a pressure drop superposition principle are utilized to introduce a coupled flow model in the reservoir.A semi-analytical solution for the production rate is obtained using a matrix iteration method.A specific well is selected for fitting dynamic production data,and the calculation results show that the tight sandstone has the highest gas production per unit thickness compared with the other types of reservoirs.Moreover,desorption and diffusion of coalbed gas and shale gas can significantly contribute to gas production,and the daily production of these two gases decreases rapidly with decreasing reservoir pressure.Interestingly,the gas production from fractures exhibits an approximately U-shaped distribution,indicating the need to optimize the spacing between clusters during hydraulic fracturing to reduce the area of overlapping fracture control.The coal matrix water saturation significantly affects the coalbed gas production,with higher water saturation leading to lower production.

Multi-layer phenomena in petawatt laser-driven acceleration of heavy ions

Laser-accelerated high-flux-intensity heavy-ion beams are important for new types of accelerators.A particle-in-cell program(Smilei) is employed to simulate the entire process of Station of Extreme Light(SEL) 100 PW laser-accelerated heavy particles using different nanoscale short targets with a thickness of 100 nm Cr, Fe, Ag, Ta, Au, Pb, Th and U, as well as 200 nm thick Al and Ca. An obvious stratification is observed in the simulation. The layering phenomenon is a hybrid acceleration mechanism reflecting target normal sheath acceleration and radiation pressure acceleration, and this phenomenon is understood from the simulated energy spectrum,ionization and spatial electric field distribution. According to the stratification, it is suggested that high-quality heavy-ion beams could be expected for fusion reactions to synthesize superheavy nuclei. Two plasma clusters in the stratification are observed simultaneously, which suggest new techniques for plasma experiments as well as thinner metal targets in the precision machining process.

Multi-Layer Feature Extraction with Deformable Convolution for Fabric Defect Detection

In the textile industry,the presence of defects on the surface of fabric is an essential factor in determining fabric quality.Therefore,identifying fabric defects forms a crucial part of the fabric production process.Traditional fabric defect detection algorithms can only detect specific materials and specific fabric defect types;in addition,their detection efficiency is low,and their detection results are relatively poor.Deep learning-based methods have many advantages in the field of fabric defect detection,however,such methods are less effective in identifying multiscale fabric defects and defects with complex shapes.Therefore,we propose an effective algorithm,namely multilayer feature extraction combined with deformable convolution(MFDC),for fabric defect detection.In MFDC,multi-layer feature extraction is used to fuse the underlying location features with high-level classification features through a horizontally connected top-down architecture to improve the detection of multi-scale fabric defects.On this basis,a deformable convolution is added to solve the problem of the algorithm’s weak detection ability of irregularly shaped fabric defects.In this approach,Roi Align and Cascade-RCNN are integrated to enhance the adaptability of the algorithm in materials with complex patterned backgrounds.The experimental results show that the MFDC algorithm can achieve good detection results for both multi-scale fabric defects and defects with complex shapes,at the expense of a small increase in detection time.

Characterization of Multi-layer Weld Metal and Creep–Rupture Behavior of Modified 10Cr–1Mo Welded Joint

The rupture behavior of the modified 10Cr–1Mo steel multi-layer welded joint is determined by the fine-grain zones of the weld metal adjacent to the fusion line during the long-term creep test at 620℃. The microstructures of multi-layer weld metal before and after the creep tests were characterized in detail, and its role in creep behavior was systematically investigated. Most grain boundaries of subgrains represented the low-angle boundaries in the weld metal adjacent to the fusion line both before and after the creep test. The widths of grains in the fine-grain zones were about 0.5–1 μm. The fracture morphology appeared as "wave" structure due to the cracking initiating from multi-layer grain boundaries in the fine-grain zones. Some W elements that melted into weld metal adjacent to the fusion line altered the thermodynamic and kinetic conditions of the Laves phase formation during long-term creep exposure. Laves phase particles mainly distributed along the grain boundaries due to the faster diffusion and segregation of Mo, W, and Si elements. Moreover, higher-density grain boundaries in the fine-grain zones led to easier nucleation and growth of Laves phase particles. Compared with other areas in the welded joint, the size of Laves phase particles in the fine-grain zones of the weld metal adjacent to the fusion line was the largest ones. The interface between Laves phase particles and the matrix acted as the nucleation site of creep micro-cavities. The creep micro-cavities grew up at the expense of fine-grain boundaries and even grew across the grain boundary deeply into adjacent grains, and then developed to cracks in the fine-grain zones.

A Novel Model for Describing Rail Weld Irregularities and Predicting Wheel-Rail Forces Using a Machine Learning Approach

Rail weld irregularities are one of the primary excitation sources for vehicle-track interaction dynamics in modern high-speed railways.They can cause significant wheel-rail dynamic interactions,leading to wheel-rail noise,component damage,and deterioration.Few researchers have employed the vehicle-track interaction dynamic model to study the dynamic interactions between wheel and rail induced by rail weld geometry irregularities.However,the cosine wave model used to simulate rail weld irregularities mainly focuses on the maximum value and neglects the geometric shape.In this study,novel theoretical models were developed for three categories of rail weld irregularities,based on measurements of the high-speed railway from Beijing to Shanghai.The vertical dynamic forces in the time and frequency domains were compared under different running speeds.These forces generated by the rail weld irregularities that were measured and modeled,respectively,were compared to validate the accuracy of the proposed model.Finally,based on the numerical study,the impact force due to rail weld irrregularity is modeled using an Artificial Neural Network(ANN),and the optimum combination of parameters for this model is found.The results showed that the proposed model provided a more accurate wheel/rail dynamic evaluation caused by rail weld irregularities than that established in the literature.The ANN model used in this paper can effectively predict the impact force due to rail weld irrregularity while reducing the computation time.

Finite element analysis of temperature field during multi-layer multi-pass weld-based rapid prototyping

During weld-bused rapid prototyping, the component experiences complex thermal process. In this paper, the temperature field evolution, thermal cycle characteristics, and temperature gradients of multi-layer multi-puss weld-based rapid prototyping are investigated using three-dimensional finite element models presented. The single-puss weld-bused rapid prototyping experiment is carried out. Thermal cycles calculated agree with experimental measurements. Furthermore, simulated results indicate that there exist the pre-heating effect of the fore layer and the post-heating effect of the rear layer in the multi-layer multi-pass weld-based rapid prototyping. In the first layer, the heat accumulates obviously. After the first layer, the dimension increase of the high temperature region behind the molten pool is not obvious. The heat diffusion condition in the first layer is the best, the heat diffusion condition in the second layer is the worst, and the heat diffusion conditions in the higher layers improve gradually.

基于SYSWELD的等高齿弧锥齿轮铣刀盘多层焊接数值模拟

本文借助Visual-Environment焊接模拟仿真软件,以SYSWELD为求解器对等高齿弧锥齿轮铣刀盘V形坡口多层焊接的焊接温度场进行数值模拟,得到了焊接温度场分布云图,分析并对比了每层焊缝中心、焊趾及距焊缝中心5mm处的温度循环曲线,推导出三层焊接温度场公式用于计算验证。结果表明,熔池中心温度最高,距离焊缝中心越近,温度梯度变化越大。

Structural Analysis of the Multi-layer Detachment Folding in Eastern Sichuan Province

A serial of＂comb-like and trough-like＂folds developed in eastern Sichuan,controlled by the multi-layer detachment folding,is different from the classical Jura-type structure in their development.The key factor resulting in the development of these structures is the occurrence of detachment layers in different parts of Neoprotozoic to Mesozoic stratigraphy of study area,which, from the bottom to the top,are the lower part of Banxi Group,Lower Cambrian（Niutitang Formation）,Lower Silurian（Longmaxi Formation and Luoreping Formation）,Upper Permian （Wujiaping Formation） and Lower Triassic（Daye Formation）.On the basis of field survey combined with sand-box modeling,this study argued that the detachment layer of the lower part of Banxi Group controlled the development of the＂comb-like＂folds,and the lower part of Cambrian detachment layer controlled the development of＂trough-like＂folds.Because of several detachment layers occurring in the study area,the development of duplex structures different scales is an important deformation mechanism,and the duplexes are the important structures distinguished from the typical detachment folding structures.Due to these duplexes,the surface structures and structural highs may not be the structural highs in the depth.Meanwhile,the detachment layers are good channels for oil/ gas migration benefiting the understanding of accumulation and migration of oil and gas.

Measurement of residual stress in a multi-layer semiconductor heterostructure by micro-Raman spectroscopy

Si-based multilayer structures are widely used in current microelectronics. During their preparation, some inhomogeneous residual stress is induced, resulting in competition between interface mismatching and surface energy and even leading to structure failure. This work presents a methodological study on the measurement of residual stress in a multi-layer semiconductor heterostructure. Scanning electron microscopy（SEM）, micro-Raman spectroscopy（MRS）, and transmission electron microscopy（TEM） were applied to measure the geometric parameters of the multilayer structure. The relationship between the Raman spectrum and the stress/strain on the [100] and [110] crystal orientations was determined to enable surface and crosssection residual stress analyses, respectively. Based on the Raman mapping results, the distribution of residual stress along the depth of the multi-layer heterostructure was successfully obtained.