A new neural network approach to density calculation on ceramic materials

The materials’ consolidation, especially ceramics, is very important in advanced research development and industrial technologies. Science of sintering with all incoming novelties is the base of all these processes. A very important question in all of this is how to get the more precise structure parameters within the morphology of different ceramic materials. In that sense, the advanced procedure in collecting precise data in submicro-processes is also in direction of advanced miniaturization. Our research, based on different electrophysical parameters, like relative capacitance, breakdown voltage, and [Formula: see text], has been used in neural networks and graph theory successful applications. We extended furthermore our neural network back propagation (BP) on sintering parameters’ data. Prognosed mapping we can succeed if we use the coefficients, implemented by the training procedure. In this paper, we continue to apply the novelty from the previous research, where the error is calculated as a difference between the designed and actual network output. So, the weight coefficients contribute in error generation. We used the experimental data of sintered materials’ density, measured and calculated in the bulk, and developed possibility to calculate the materials’ density inside of consolidated structures. The BP procedure here is like a tool to come down between the layers, with much more precise materials’ density, in the points on morphology, which are interesting for different microstructure developments and applications. We practically replaced the errors’ network by density values, from ceramic consolidation. Our neural networks’ application novelty is successfully applied within the experimental ceramic material density [Formula: see text] [kg/m3], confirming the direction way to implement this procedure in other density cases. There are many different mathematical tools or tools from the field of artificial intelligence that can be used in such or similar applications. We choose to use artificial neural networks because of their simplicity and their self-improvement process, through BP error control. All of this contributes to the great improvement in the whole research and science of sintering technology, which is important for collecting more efficient and faster results.

Learning from mistakes - Assessing the performance and uncertainty in process-based models

Typical applications of process- or physically-based models aim to gain a better process understanding or provide the basis for a decision-making process. To adequately represent the physical system, models should include all essential processes. However, model errors can still occur. Other than large systematic observation errors, simplified, misrepresented, inadequately parametrized or missing processes are potential sources of errors. This study presents a set of methods and a proposed workflow for analyzing errors of process-based models as a basis for relating them to process representations. The evaluated approach consists of three steps: (i) training a machine learning (ml) error-model using the input data of the process-based model and other available variables, (ii) estimation of local explanations (i.e., contributions of each variable to a individual prediction) for each predicted model error using SHapley Additive exPlanations (SHAP) in combination with principal component analysis, (iii) clustering of SHAP values of all predicted errors to derive groups with similar error generation characteristics. By analyzing these groups of different error-variable association, hypotheses on error generation and corresponding processes can be formulated. That can ultimately lead to improvements in process understanding and prediction. The approach is applied to a process-based stream water temperature model HFLUX in a case study for modelling an alpine stream in the Canadian Rocky Mountains. By using available meteorological and hydrological variables as inputs, the applied ml model is able to predict model residuals. Clustering of SHAP values results in three distinct error groups that are mainly related to shading and vegetation emitted longwave radiation. Model errors are rarely random and often contain valuable information. Assessing model error associations is ultimately a way of enhancing trust in implemented processes and of providing information on potential areas of improvement to the model.

Research on the printing error of tilted vertical beams in delta-robot 3D printers

Purpose A three-dimensional (3D) printing error simulation approach is proposed to analyze the influence of tilted vertical beams on the 3D printing accuracy. The purpose of this study is to analyze the influence of such errors on printing accuracy and printing quality for delta-robot 3D printer. Design/methodology/approach First, the kinematic model of a delta-robot 3D printer with an ideal geometric structure is proposed by using vector analysis. Then, the normal kinematic model of a nonideal delta-robot 3D robot with tilted vertical beams is derived based on the above ideal kinematic model. Finally, a 3D printing error simulation approach is proposed to analyze the influence of tilted vertical beams on the 3D printing accuracy. Findings The results show that tilted vertical beams can indeed cause 3D printing errors and further influence the 3D printing quality of the final products and that the 3D printing errors of tilted vertical beams are related to the rotation angles of the tilted vertical beams. The larger the rotation angles of the tilted vertical beams are, the greater the geometric deformations of the printed structures. Originality/value Three vertical beams and six horizontal beams constitute the supporting parts of the frame of a delta-robot 3D printer. In this paper, the orientations of tilted vertical beams are shown to have a significant influence on 3D printing accuracy. However, the effect of tilted vertical beams on 3D printing accuracy is difficult to capture by instruments. To reveal the 3D printing error mechanisms under the condition of tilted vertical beams, the error generation mechanism and the quantitative influence of tilted vertical beams on 3D printing accuracy are studied by simulating the parallel motion mechanism of a delta-robot 3D printer with tilted vertical beams.

Exact Analysis of the Finite Precision Error Generated in Important Chaotic Maps and Complete Numerical Remedy of These Schemes

A first aim of the present work is the determination of the actual sources of the “finite precision error” generation and accumulation in two important algorithms: Bernoulli’s map and the folded Baker’s map. These two computational schemes attract the attention of a growing number of researchers, in connection with a wide range of applications. However, both Bernoulli’s and Baker’s maps, when implemented in a contemporary computing machine, suffer from a very serious numerical error due to the finite word length. This error, causally, causes a failure of these two algorithms after a relatively very small number of iterations. In the present manuscript, novel methods for eliminating this numerical error are presented. In fact, the introduced approach succeeds in executing the Bernoulli’s map and the folded Baker’s map in a computing machine for many hundreds of thousands of iterations, offering results practically free of finite precision error. These successful techniques are based on the determination and understanding of the substantial sources of finite precision (round-off) error, which is generated and accumulated in these two important chaotic maps.

CMEMS-Based Coastal Analyses: Conditioning, Coupling and Limits for Applications

Recent advances in numerical modeling, satellite data, and coastal processes, together with the rapid evolution of CMEMS products and the increasing pressures on coastal zones, suggest the timeliness of extending such products toward the coast. The CEASELESS EU H2020 project combines Sentinel and in-situ data with high-resolution models to predict coastal hydrodynamics at a variety of scales, according to stakeholder requirements. These predictions explicitly introduce land discharges into coastal oceanography, addressing local conditioning, assimilation memory and anisotropic error metrics taking into account the limited size of coastal domains. This article presents and discusses the advances achieved by CEASELESS in exploring the performance of coastal models, considering model resolution and domain scales, and assessing error generation and propagation. The project has also evaluated how underlying model uncertainties can be treated to comply with stakeholder requirements for a variety of applications, from storm-induced risks to aquaculture, from renewable energy to water quality. This has led to the refinement of a set of demonstrative applications, supported by a software environment able to provide met-ocean data on demand. The article ends with some remarks on the scientific, technical and application limits for CMEMS-based coastal products and how these products may be used to drive the extension of CMEMS toward the coast, promoting a wider uptake of CMEMS-based predictions.

Learning from mistakes - Assessing the performance and uncertainty in process-based models

<p>Typical applications of process- or physically-based models aim to gain a better process understanding of certain natural phenomena or to estimate the impact of changes in the examined system caused by anthropogenic influences, such as land-use or climate change. To adequately represent the physical system, it is necessary to include all (essential) processes in the applied model and to observe relevant inputs in the field. However, model errors, i.e. deviations between observed and simulated values, can still occur. Other than large systematic observation errors, simplified, misrepresented or missing processes are potential sources of errors. This study presents a set of methods and a proposed workflow for analyzing errors of process-based models as a basis for relating them to process representations.</p><p>The evaluated approach consists of three steps: (1) prediction of model errors with a machine learning (ML) model using data that might be associated with model errors (e.g., model input data), (2) derivation of variable importance (i.e. contribution of each input variable to prediction) for each predicted model error using SHapley Additive exPlanations (SHAP), (3) clustering of SHAP values of all predicted errors to derive groups with similar error generation characteristics. By analyzing these groups of different error/variable association, hypotheses on error generation and corresponding processes can be formulated. This analysis framework can ultimately lead to improving hydrologic understanding and prediction.</p><p>The framework is applied to the physically-based stream water temperature model HFLUX in a case study for modelling an alpine stream in the Canadian Rocky Mountains. Initial statistical tests show a significant association of model errors with available meteorological and hydrological variables. By using these variables as input features, the applied ML model is able to predict model residuals. Clustering of SHAP values results in four distinct error groups that can be related to tree shading, sensible and latent heat flux and longwave radiation emitted by trees.</p><p>Model errors are rarely random and often contain valuable information. Assessing model error associations is ultimately a way of enhancing trust in implemented processes and of providing information on potential areas of improvement to the model.</p>

Correction to: Mid-spatial frequency error generation mechanisms and prevention strategies for the grinding process

An amendment to this paper has been published and can be accessed via the original article.