Abstract: In design for forming, it is becoming increasingly significant to develop surrogate models of high-fidelity finite element analysis (FEA) simulations of forming processes to achieve effective component feasibility assessment as well as process and component optimizations. However, surrogate models using traditional scalar-based machine learning methods (SBMLMs) fall short on accuracy and generalizability. This is because SBMLMs fail to harness the location information available from the simulations. To overcome this shortcoming, the theoretical feasibility and practical advantages of innovatively applying image-based machine learning methods (IBMLMs) in developing surrogate models of sheet stamp forming simulations are explored in this study. To demonstrate the advantages of IBMLMs, the effect of the location information on both design variables and simulated physical fields is first proposed and analyzed. Based on a sheet steel stamping case study, a Res-SE-U-Net IBMLM surrogate model of stamping simulations is then developed and compared with a baseline multilayer perceptron (MLP) SBMLM surrogate model. The results show that the IBMLM model is advantageous over the MLP SBMLM model in accuracy, generalizability, robustness, and informativeness. This article presents a promising methodology in leveraging IBMLMs as surrogate models to make maximum use of information from stamp forming FEA results. Future prospective studies that are inspired by this article are also discussed.

Abstract: This paper presents a shape optimisation system to design the shape of an acoustically-hard object in the three-dimensional open space. The boundary element method (BEM) is suitable to analyse such an exterior field. However, the conventional BEM, which is based on piecewise polynomial shape and approximate (interpolation) functions, can require many design variables because they are usually chosen as a part of the nodes of the underlying boundary element mesh. In addition, it is not easy for the conventional method to compute the gradient of the sound pressure on the surface, which is necessary to compute the shape derivative of our interest, of a given object. To overcome these issues, we employ the isogeometric boundary element method (IGBEM), which was developed in our previous work. With using the IGBEM, we can design the shape of surfaces through control points of the NURBS surfaces of the target object. We integrate the IGBEM with the nonlinear programming software through the adjoint variable method (AVM), where the resulting adjoint boundary value problem can be also solved by the IGBEM with a slight modification. The numerical verification and demonstration validate our shape optimisation framework.

Abstract: Although there is an increase in the amount of seismic data acquired with wide-azimuth geometry, it is difficult to achieve regular data distributions in spatial directions owing to limitat...

Abstract: The meshless numerical manifold method (MNMM) inherits two covers of the numerical manifold method. A mathematical cover is composed of nodes' influence domains and a physical cover consists of physical patches, which are produced through cutting mathematical cover by physical boundaries. Because two covers are adopted, MNMM can naturally and uniquely solve both the continuous and discontinuous problems under Galerkin's variational framework. However, Galerkin's meshless method needs background integration grids to realize solving, which often does not match the nodes' influence domains, so the accuracy of numerical integration is reduced. Consider that MNMM allows even distribution of nodes and the physical cover contains the characteristics of the boundary and nodes' influence domains, the study presents a new numerical integration strategy to ensure that the background integration grids match the nodes' influence domains. The method can be applied to continuous and discontinuous problems, and is proved to be equivalent to the influence domain integration. At the same time, a reasonable arrangement of mathematical nodes is made to assure the background integration grid that it is accordant and straightforward. In this way, the number of physical patches of each integral point is the same, which improves the accuracy of interpolation calculation. The effectiveness of the proposed method is verified by numerical examples of both continuous and discontinuous problems.

Abstract: In the current work, the stable node-based smoothed radial point interpolation method (SNS-RPIM) was used to investigate the dynamic responses of magneto-electro-elastic (MEE) beams under hygrothermal environment with the generalized Newmark method. Based on the generalized smoothed Galerkin weak form and the gradient smoothing technique, the basic equations for hygro-thermo-magneto-electro-elastic (HTMEE) coupling problems were derived. The stable item of SNS-RPIM made the stiffness ‘close-to-exact’ and cured the ‘over-soft’ of the model. Through the numerical examples, the effect of empirical constants, temperature variation and moisture concentration on the elastic stiffness coefficients and generalized displacement was investigated. Besides, numerical results verified the accuracy, convergence and correctness of the present method compared with the traditional finite element method when analyzing the HTMEE coupling problems. Moreover, SNS-RPIM was very stable and accurate for extremely distributed meshes, which can have an excellent application for future studies of the coupled multi-physical problems.

Abstract: By adopting the scaling functions of the B-spline wavelet on the interval (BSWI) as interpolation functions, a wavelet-based boundary element method (BEM) model is constructed to compute the band structures of two-dimensional photonic crystals (2D PCs), which are composed of square or triangular lattices with arbitrarily shaped inclusions. The boundary integral equations of both the matrix and inclusion are developed in a unit cell because of the structural periodicity. In order to make the curve boundary be compatible well, geometric boundaries are interpolated by employing second order BSWI scaling functions while arbitrary order scaling functions are used to approximate boundary variables. For any given angular frequency, an effective technique is provided to generate matrix values related to the boundary shape. Moreover, singular integral problem involved in the presented wavelet-based BEM is considered. Then, combining the Bloch theorem and the interface conditions, a linear eigenvalue equation related to the Bloch wave vector is obtained. Numerical examples are given to verify the performance of the wavelet-based BEM developed herein compared with the conventional BEM.

Abstract: In this study, an improved explicit time integration method based on quartic B-spline interpolation is generalized for linear and nonlinear dynamics. The accuracy order of the proposed method is analytically obtained as well as its spectral radius , period elongation, and algorithmic damping. The analysis shows the proposed method achieves third-order and at least second-order accuracy for displacement and velocity, respectively. With one algorithmic parameter, the proposed method can adjust numerical dissipation and accuracy. Linear dynamic examples demonstrate that the effectiveness of the proposed method as well as its high-order accuracy. Nonlinear dynamic problems show the proposed method can provide desirable solutions. Numerical results demonstrate the proposed method can provide more stable and accurate solutions than other classical explicit methods.

Abstract: For many geoscience applications, the quality of digital elevation models (DEMs) is the first and foremost requirement. DEM quality generally depends on the interpolation procedure, especially in forested areas with complex terrain. However, classical interpolation methods do not always preserve terrain features, owing to their isotropic and local estimation nature. Therefore, this paper presents a feature-preserving interpolation method, where a structure tensor is integrated into the radial basis function to ensure its anisotropy. This method alleviates the influence of points located on different surface patches. The performance of the proposed method was compared with those of classical interpolation methods, including the inverse distance weighting (IDW), ordinary kriging (OK), topo to raster (ANUDEM), and natural neighbour (NN) methods. The performance of these methods was tested on benchmark data provided by the International Society for Photogrammetry and Remote Sensing (ISPRS) Commission, and on one private dataset. Both datasets were collected via the airborne light detection and ranging (LiDAR) technique. The results from both datasets demonstrate that from a quantitative perspective, the proposed method produces more accurate DEMs than the classical interpolation methods. Specifically, in terms of root mean square error (RMSE), the proposed method is at least 15.8% and 7.6% more accurate when applied to the ISPRS and private data, respectively. Moreover, the proposed method produces more visually appealing surfaces with a good trade-off between terrain feature retention and noise removal.

Abstract: Geographic Information System (GIS) is a cost effective computer based tool used to visualize, analyze and display geospatial data. GIS interpolation method has been widely used in various domains to predict values of unknown points by using the similarity of nearby sample points. GIS based groundwater mapping is one such area, where interpolation is used for the mapping of various parameters such as microbial contamination, physiochemical concentrations, water level, and so on so forth. Two types of interpolation techniques are used in GIS, i.e., deterministic and geostatistical. Deterministic techniques make use of mathematical functions to interpolate while geostatistics uses both statistical and mathematical methods to create surfaces as well as to assess the uncertainty present in the predictions. Although, there is some confusion associated with the selection of these methods. So, in this paper, a comparison between geostatistics and deterministic methods has been shown. This study will help the GIS users to get a better insight into both the interpolation methods.

Abstract: Trochoidal (TR) milling is a popular means for slotting operation. Attributing to its unique circular-shaped path pattern, TR milling avoids the full tool–workpiece engagement, which helps reduce the cutting heat accumulation and hence slow down the tool wear. While traditionally TR milling is only used for machining 2.5D cavities, it has now been extended to machining genuine 3D curved cavities under the realm of five-axis machining. However, since for a typical five-axis machine tool the rotary axes have a much larger moment of inertia than the three linear axes, to reduce both the total machining time and the consumed electric energy (for driving the machine tool), it is desirable to minimize the use of the two rotary axes (particularly the one with the larger moment of inertia) when planning a TR tool path for a given 3D cavity. Nevertheless, due to the newness of five-axis TR machining, there has no published reports on this subject. In this paper, we present a five-axis TR tool path planning algorithm for machining an arbitrary 3D curved cavity, which will consider the kinematical characteristics of the five-axis machine tool and try to minimize the use of the rotary axis with the largest moment of inertia, while tending to all the required constraints such as the threshold on the cutting force. Both computer simulation and physical cutting experiments of the proposed method have been conducted, and the results give a preliminary confirmation on the feasibility and advantages of the proposed method.

Abstract: Groundwater flow problems are generally solved using analytical or numerical methods. Though analytical solutions are exact and preferable, they are not available for complex field problems. Hence numerical methods such as Finite Element and Finite Difference methods are used to solve complex groundwater problems. These conventional mesh/ grid-based numerical methods need construction of a detailed mesh/ grid. On the other hand, the meshless approach creates a system of algebraic equations on a collection of distributed nodes in the problem area and the boundary. As a result, it is easy to incorporate any modifications to the model at a later time by simply adding nodes to the domain. In this study a weak form meshless method known as local radial point interpolation method (LRPIM) which uses radial basis functions for approximation or interpolation is developed to solve the groundwater flow problems in a confined aquifer. The results obtained from the LRPIM model has been compared with other numerical methods for benchmark and real field problems, and are found to be satisfactory. Implementation of the essential boundary conditions was relatively easier in LRPIM and gave good accuracy for the problems considered. LRPIM can potentially be used as an alternative to the other conventional methods, especially where the domain boundary is irregular or varying with time.

Abstract: The high spatial, spectral, and temporal resolutions of the Vegetation and Environment monitoring New Micro-Satellite (VENµS) satellite data facilitate field-level phenological analysis of crops. This study proposes deep learning (DL) based approaches to resolve the issues prevalent in crop phenology-based fingerprint estimation at field-level using VENµS satellite data. An encoder-decoder-based framework, called piece-wise kernel encoding network (PKNet), is proposed for missing data imputation of the vegetation index (VI) curves derived from time-series image data. PKNet adopts interpolation-based convolution, dynamic time wrapping (DTW) based layer formulation, and imputation-specific constraints for optimal smoothing of the irregularly sampled VI curves. Besides, PKNet learns kernel parameters dynamically. A variational encoding framework called a dynamic-projection-based generalization network (DPGNet), is proposed to generalize the pixel-level VI curves to synthesize a representative VI curve for a given field. DPGNet is more effective than the use of multiple moments as it is resilient to outliers and learns normally distributed latent space with a small number of samples. The current research also proposes a classifier, called dynamic time wrapping based capsule network (DTCapsNet), which learns a discriminative latent space and accurately models the VI curve features. The DTCapsNet considers the time-series nature of the input using DTW-based convolution layers. The feature characterization improves generalizability and gives good results, even with a limited number of training samples. Experiments using the ground truth information and satellite images, acquired over two farms in Israel, illustrate that the proposed frameworks give better results than the commonly-used existing approaches.