Accurate Interpolation Research Articles

A regional augmented PPP algorithm for offshore considering NWP

ABSTRACT In offshore areas with inhomogeneous distribution of reference stations, the low accuracy of regional augmented Zenith Tropospheric Delay (ZTD) products directly impacts regional augmented Precise Point Positioning (PPP) convergence time. Considering the availability of Numerical Weather Prediction (NWP) data at sea, we propose a regional augmented PPP algorithm that integrates tropospheric delays derived from NWP virtual grid points and the Continuously Operating Reference Station (CORS) network observations. Land and marine experiments are carried out to verify the effectiveness of this algorithm, evaluating ZTD interpolation accuracy and PPP positioning performance. The land experimental results show that this algorithm achieves similar ZTD accuracy and PPP results compared to traditional augmented PPP with perfect reference stations. The ZTD accuracy is 8.5 mm, and the average positioning accuracy is approximately 3.75 cm. The convergence time is less than 11 min where the data sampling interval is 30 s. Marine experiments indicate that the convergence time of this algorithm is 11–32% shorter than that of ionospheric-free PPP. The convergence time of this algorithm is reduced by 56–59%, and positioning accuracy in the E, N and U directions is augmented by 33.3%, 19.5% and 53.7%, respectively, compare to traditional augmented PPP at sea where the distribution of reference stations is inhomogeneous. Meanwhile, almost all stations can achieve a faster solution convergence by this algorithm. Furthermore, the algorithm is suitable not only for areas that lack reference stations at sea but also for the lack of reference stations on land.

A Timoshenko–Ehrenfest planar beam element for geometrically nonlinear analysis using rational Bézier representations with varying weights

This study presents an enhanced Timoshenko–Ehrenfest beam element designed for geometrically nonlinear analysis of planar straight beams subjected to small strains. The methodology adopts the isogeometric analysis (IGA) approach, utilizing rational cubic Bernstein basis functions to construct the interpolations of kinematic unknowns. However, in contrast to the traditional IGA concept, the isoparameterization between the reference geometry and kinematic unknowns is relaxed by treating specific weights in the basis functions as degrees of freedom. This approach significantly improves the accuracy of interpolations, providing superior descriptions of complex deformed configurations with only a minimal increase in element degrees of freedom. Notably, the proposed beam element is shown to be free from the locking phenomena, i.e., membrane and shear locking, without the need for additional treatments. Through rigorous numerical experiments, the study demonstrates the exceptional efficiency and robustness of the proposed beam element, confirming its superior capabilities.

Hierarchical Stratification for Spatial Sampling and Digital Mapping of Soil Attributes

This study assessed whether stratifying agricultural areas into macro- and micro-variability regions allows targeted sampling to better capture soil attribute variability, thus improving digital soil maps compared to regular grid sampling. Allocating more samples where soil variability is expected offers a promising alternative. We evaluated two sampling densities in two agricultural fields in Southeast Brazil: a sparse density (one sample per 2.5 hectares), typical in Precision Agriculture, and a denser grid (one sample per hectare), which usually provides reasonable mapping accuracy. For each density, we applied three designs: a regular grid and grids with 25% and 50% guided points. Apparent soil magnetic susceptibility (MSa) delimited macro-homogeneity zones, while Sentinel-2’s Enhanced Vegetation Index (EVI) identified micro-homogeneity, guiding sampling to pixels with higher Fuzzy membership. The attributes assessed included phosphorus (P), potassium (K), and clay content. Results showed that the 50% guided sample configuration improved ordinary kriging interpolation accuracy, particularly with sparse grids. In the six sparse grid scenarios, in four of them, the grid with 50% of the points in regular design and the other 50% directed by the proposed method presented better performance than the full regular grid; the higher improvement was obtained for clay content (RMSE of 54.93 g kg−1 to 45.63 g kg−1, a 16.93% improvement). However, prior knowledge of soil attributes and covariates is needed for this approach. We therefore recommend two-stage sampling to understand soil properties’ relationships with covariates before applying the proposed method.

Mechanical performance degradation modelling and prognosis method of high‐voltage circuit breakers considering censored data

AbstractDue to the untimely deployment of sensors and the early retirement of high‐voltage circuit breakers, life‐cycle data is missing, leading to an inability to accurately predict the mechanical performance degradation trend. A new modelling and prediction method of mechanical performance degradation of high‐voltage circuit breakers considering censored data was proposed. Firstly, multiple imputation by chained equations was used to impute the missing values in the dataset of circuit breaker closing time, creating an interpolated dataset. Secondly, the Nadaraya–Watson kernel regression method was employed to smoothly estimate the interpolated dataset and eliminate data measurement errors. Then, the functional principal component analysis method was utilized to extract the common degradation trend component and deviation component to construct the degradation model. Finally, Bayesian inference was applied to dynamically update the degradation model parameters and predict the degradation trend of the high‐voltage circuit breaker. The results showed that the proposed method was capable of achieving better interpolation accuracy under the condition of different closing time censored data of high‐voltage circuit breakers. Moreover, as the degradation progressed, the dynamic prediction effect improved. The research can be used to provide an effective decision‐making basis for the operation and maintenance strategy of high‐voltage circuit breakers.

Data-driven analysis of bonding strength in laser-structured metal-GFRP hybrid joints via groove morphology

This study aims to enhance predictions of the mechanical properties of mechanically interlocked hybrid joints by employing machine learning techniques coupled with feature engineering of cross-sectional groove morphology. Unlike mechanical fastening, which promotes localized stress, and adhesive bonding, which requires prolonged contaminant removal, mechanically interlocked joints offer a distinct advantage by eliminating the need for either. The mechanically interlocked joints in this study combine glass fiber reinforced composite fabricated via injection molding, with cold rolled steel structured by a nanosecond laser. Through optical microscopy, crucial groove dimensions such as depth and width are identified for feature extraction. Domain-specific feature engineering is employed to improve predictive accuracy, integrated with existing regression models. The concept of “structure density,” initially defined as groove width over hatch distance, is expanded during feature engineering to include additional relevant features over hatch distance. Experimental investigations identified optimal laser parameters for shear strength, yielding a maximum single lap shear strength of 33.3 MPa under specific conditions. The third polynomial regression model incorporating structure density features emerged as the most effective in predicting shear strength, demonstrating high accuracy in both interpolation and extrapolation scenarios. The study suggests potential cost savings by utilizing surface topography for shear strength prediction, with implications for industries amidst the increasing prevalence of composite materials.

Analysis of the impact of high temporal resolution tropospheric parameters on GNSS station coordinate and troposphere estimation

Abstract Troposphere modelling accuracy can seriously influence the performance of precise global navigation satellite system (GNSS) positioning. The temporal interpolation accuracies of troposphere parameters become important with the improvement in troposphere modelling, especially during the periods of severe weather changes. In this study, a one-month experiment was conducted for 10 EUREF Permanent GNSS Network stations to analyse the temporal interpolation errors of numerical weather model derived troposphere parameters and their influences on GNSS station coordinate and troposphere estimation. The root mean square of the differences between the 1- and 6-hourly European Centre for Medium-Range Weather Forecasts reanalysis v5-derived troposphere parameters are 0.72/11.52 mm for the zenith hydrostatic/zenith wet delay (ZWD), while they are 7.09/91.84 mm for the slant hydrostatic/wet delay at a 3 ∘ elevation angle. The corresponding influences on the estimated upper component and ZWD are 1.28 and 0.79 mm, respectively, in the daily static Precise Point Positioning ambiguity resolution solutions. However, the accuracy improvements are insignificant in the daily static solutions, mainly due to the drop of temporal interpolation errors with increasing elevation angles and the influence of other unmodeled systematic errors. In the 4-hourly static solutions, the accuracies of the up component improve 0.51 mm (5.9% in relative) and 0.29 mm (3.5% in relative) during the severe and stable periods, respectively. These impacts cannot be ignored in some GNSS subdaily applications, for example, monitoring non-tidal ocean loading effects, bedrock and monument thermal elastic effects, etc.

Variability of Interpolation Errors and Mutual Enhancement of Different Interpolation Methods

Data interpolation methods are important statistical analysis tools that can fill in data gaps and missing areas by predicting and estimating unknown data points, thereby improving the accuracy and credibility of data analysis and research. Different interpolation methods are widely used in related fields, but the error between different interpolation methods and their interpolation fusion optimization have a significant impact on the interpolation accuracy, which still deserves further exploration. This study is based on two different types of point data: PM2.5 (PM2.5 refers to particulate matter in the atmosphere with a diameter of 2.5 μm or less, also known as inhalable particles or fine particulate matter) in Xinyang City, Henan Province, and the elevation of typical gullies in Yuanmou County, Yunnan Province. Using relative difference coefficients and hotspot analysis methods, the differences in error characteristics among four interpolation methods, ordinary kriging (OK), universal kriging (UK), inverse distance weighted (IDW), and radial basis functions (RBFs), were compared, and the influence of interpolation fusion methods on the accuracy of interpolation results was explored. The results show that after interpolation of PM2.5 concentration and gully elevation, the error difference between OK and UK is the smallest in both datasets. For PM2.5 concentration data, IDW and UK interpolation errors have the largest difference; for elevation data, the differences between RBF and UK interpolation are the largest. The weighted fusion results show that the interpolation error accuracy of PM2.5 concentration data with an interpolation point density of 0.009 points per square kilometer is improved, and the root mean square error (RMSE) after fusion is reduced from 0.374 μg/m3 to 0.004 μg/m3. However, the error accuracy of the elevation data of the gully with an interpolation point density of 0.76 points/m2 did not improve significantly. This indicates that characteristics such as the density of the original data are important factors that affect the accuracy of interpolation. In the case of sparse interpolation points, it is possible to consider fusing the interpolation results with different error patterns to improve their accuracy. This study provides a new idea for improving the accuracy of interpolation errors.

EVALUATION OF INTERPOLATION METHODS FOR GNSS VELOCITIES IN CRUSTAL DEFORMATION: A CASE STUDY ON STRAIN RATE CALCULATION IN THE SOUTHERN SUMATRA

This study evaluates three interpolation methods—Inverse Distance Weighting (IDW), Kriging, and Sandwell—for analyzing crustal deformation in southern Sumatra, focusing on the on-site velocity consistency test. This test is crucial for assessing interpolation accuracy by comparing interpolated velocities to actual GNSS velocities at specific sites. Given the region's sparse GNSS network, accurate interpolation is vital for reliable deformation analysis. GNSS data from continuous and campaign sites, collected between 2017 and 2022 with a 30-second sampling interval, were processed to generate velocity. The Sandwell method, particularly with a Poisson’s ratio of 0, demonstrated superior performance, achieving the lowest mean residuals in the on-site velocity consistency test. This method consistently provided accurate interpolated velocities that closely matched original site data. IDW and Kriging methods also showed effectiveness but had different convergence behaviors: IDW required higher interpolation degrees for accuracy, while Kriging excelled in the east-west residuals. The Sandwell method’s velocities were used to calculate strain rates, revealing significant spatial variability. The findings underscore the importance of detailed on-site velocity consistency testing and highlight the need for expanding GNSS networks to improve accuracy and better assess seismic hazards in southern Sumatra.

Application of data partitioned Kriging algorithm with GPU acceleration in real-time and refined reconstruction of three-dimensional radiation fields

Accurately assessing personal radiation doses in real radiation environments like nuclear power plants requires precise and real-time reconstruction of three-dimensional radiation fields. The Kriging algorithm, known for its accuracy in spatial interpolation, provides a promising approach for this task. However, its computational demands can be significant, especially in real-time scenarios. To address this, we enhance the Kriging algorithm with GPU acceleration and data partitioning strategies, enabling efficient and accurate reconstruction of three-dimensional nuclear radiation fields. Using Fluka software for Monte Carlo simulations, we generated a virtual radiation field of dimensions 5 m × 5 m × 5 m for a single-source, unshielded scenario, and a field of dimensions 20 m × 6 m × 8 m for a multi-source, shielded scenario. Using the simulated data, we compared the prediction accuracy of the improved algorithm with the conventional Kriging algorithm and further explored factors influencing the acceleration ratio of the improved algorithm. The results indicate that the GPU-accelerated and data-partitioned Kriging algorithm achieves nearly identical accuracy compared to the traditional method. In the single-source, unshielded scenario, with more than 343 known (measurement) points and predicting 95×95×95= 857,375 points, the prediction accuracy remains above 92.25%. In the multi-source, shielded scenario, with more than 8000 known (measurement) points and predicting 95×95×95= 857,375 points, the prediction accuracy remains above 91.17%. The acceleration performance of the improved algorithm is consistent across both scenarios, with the acceleration ratio increasing as the number of known and predicted points grows, reaching approximately 20 for smaller datasets and up to 93 for larger datasets. Additionally, the acceleration effect of the improved algorithm varies with data partition size, initially increasing and then decreasing as the partition size increases. When the number of known points is 512 and the number of predicted points is 884,736, the optimal partition size lies between 80,000 and 90,000, resulting in a prediction time of only 0.24 s.

A Riemann-based SPH formulation for modelling elastoplastic soil behaviour using a Drucker–Prager model

The present paper aims at proposing and investigating a Riemann-based SPH formulation to simulate the elastoplastic behaviour of soils undergoing large deformations, using a Drucker–Prager model. Basing on the pioneer work from Parshikov and Medin (Parshikov and Medin, 2002), a Riemann solver is used to maintain regular fields while being free of tuning parameters. By contrast to the work in Parshikov and Medin (2002) where piecewise constant reconstructions were employed, piecewise linear reconstructions are preferred in this work to reduce the numerical diffusion. A Particle Shifting Technique (PST) is used to maintain regular particle distributions and consequently accurate SPH interpolations. To the best of the author’s knowledge, the use of a Riemann solver specific to solid mechanics with a pressure-dependent elastoplastic Drucker–Prager yield surface to model the behaviour of the material represents a novelty with respect to the existing literature. A Boundary Integral Method (BIM) initially derived for fluid dynamics (Ferrand et al., 2013; Chiron et al., 2019) is adapted to solid mechanics in order to handle complex geometries. It allows to deal with wall treatment without using fictitious particles, and shows satisfactory results even in sharp angle regions. The ability of the proposed Riemann-based formulation to simulate accurately elastoplastic problems and its robustness are examined through several test cases in plane strain conditions. Attention is paid to the capacity of the formulation to mitigate the occurrence of Tensile Instability (TI) with respect to other schemes, for which additional treatment is required to treat this issue, such as the additional artificial stress method (Gray et al., 2001).

An explainable spatial interpolation method considering spatial stratified heterogeneity

Spatial interpolation is essential for handling sparsity and missing spatial data. Current machine learning-based spatial interpolation methods are subject to the statistical constraints of spatial stratified heterogeneity (SSH), normally involving separate modeling of each stratum and simple weighted averaging to integrate intra-stratum and inter-strata features. However, these models overlook the different contributions of inter-strata features to different locations within a stratum (heterogeneous inter-strata associations, HIA) and the explanation of spatial effects on the interpolation process, leading to suboptimal and unreliable interpolation outcomes. This article proposes a novel explainable spatial interpolation method considering SSH (X-SSHM). Spatial and environmental features are utilized to describe intra-stratum and inter-strata information, which are fed into random forest-based learners to achieve high-level semantic feature mapping. Geographically weighted regression is employed to integrate intra-stratum and inter-strata features to achieve a unified expression of SSH and HIA, obtaining the final interpolation result. Geographically weighted Shapley (GSHAP) is proposed to decompose the marginal contributions of intra-stratum and inter-strata features. Model performance is evaluated on simulated and soil organic matter datasets. X-SSHM outperformed five baselines regarding interpolation accuracy. Moreover, statistical methods validated X-SSHM’s ability to elucidate the mechanisms by which SSH, spatial autocorrelation and HIA affect the model interpolation process.

An improved subpixel measurement method for piezoelectric ceramic driving characteristics based on particle filter

Microscopic vision displacement calculation is crucial for its high precision and non-contact nature in measuring piezoelectric ceramic displacement. However, the inefficient global search limits its effectiveness. This paper proposes an improved sub-pixel algorithm (GKF-PFBM) based on particle filtering. Firstly, particle filtering (PF) is combined with block matching (BM) to enhance matching efficiency and accuracy by replacing global search with particle state prediction and update. Then, subpixel interpolation using a Gaussian kernel function (GKF) is better adapted to nonlinear variations, handling high-frequency variations while preserving clear edges, thereby improving interpolation accuracy. Finally, a high-precision experimental platform is used to measure the driving characteristics of piezoelectric ceramics. Experimental results show that the mean errors of the GKF-PFBM algorithm and the bilinear interpolation algorithm (BI-BM) are 0.0032pixels and 0.1269pixels, respectively. The average hysteresis displacement error was measured to be approximately 12 nm by this method, verifying its precise non-contact measurement capability.

Contact fatigue life forecasting model considering micro-scale subsurface stress for aerospace spiral bevel gears

Focusing on stress distribution at subsurface layer under aerospace service condition in roughness tooth flank meshing interface, a new loaded contact fatigue life forecasting model is developed by considering micro-scale surface effect for aerospace spiral bevel gears. Firstly, tooth flank modeling considering the actual manufacturing process is used for accurate tooth flank point determination having high and uniform grid density. Then, with application geometric approximation and operation, discrete convolution and fast Fourier transformation (DC-FFT) based conjugate gradient (CG) method is applied to determine time-varying load distribution. While at normal direction of each point from the high-density tooth flank discretization after accurate interpolation is added the roughness height from the actual micro-scale geometric topography measurement, a tooth flank reconstruction is performed to determine the micro- scale geometric topography. Then, elastic half-space loaded contact model and DC-FFT method are employed to compute subsurface stress distribution for roughness tooth flank. Von Mises stress is selected as design variable and introduced into Zaretsky model to establish the contact fatigue life forecasting model. Finally, a spiral bevel gear set in aerospace industrial application is exercised to verify the impact of subsurface stress on contact fatigue life.

Radial Kernels Collocation Method for the Solution of Volterra Integro-Differential Equations

Radial kernel interpolation is an advanced method in approximation theory for the construction of higher order accurate interpolants for scattered data up to higher dimensional spaces. In this manuscript, we formulate a radial kernel collocation approach for solving problems involving the Volterra integro-differential equations using two radial kernels: The Generalized Multi-quadrics and the linear Laguerre-Gaussians. This was achieved by simplifying the Volterra integral problem's solution to an algebraic system of equations. The impact of the shape parameter present in every kernel on the method's accuracy is examined and found to be significant. Two examples were used to illustrate the process; the numerical results are shown as tables and graphs. MATLAB 2018a was employed in the process.

Geostatistical Interpolation Approach for Improving Flood Simulation Within a Data‐Scarce Region in the Tibetan Plateau

ABSTRACTThe complex orography of the Tibetan plateau (TP) and the scarcity and uneven spatial distribution of meteorological stations present significant challenges in accurately estimating meteorological variables for hydrological simulations. This study aims to enhance the accuracy of daily precipitation and temperature interpolation for hydrological simulations in the Lhasa River Basin (LRB), particularly during flood events. We evaluate and compare the performance of deterministic Inverse Distance Weighting—IDW and geostatistical (Ordinary Kriging—OK and Kriging with External Drift—KED) interpolation methods for estimating precipitation and temperature patterns. Subsequently, we investigate the influence of different interpolation methods on hydrological simulations by using the interpolated meteorological data as input for the Water Balance Simulation Model (WaSiM) to simulate daily discharge in the LRB. Our results revealed that geostatistical methods, specifically OK and KED, are more effective in capturing the spatial variability and anisotropy inherent in precipitation patterns influenced by the Indian summer monsoons. In addition, the KED method effectively captured the daily variation of the temperature lapse rate, indicating the inadequacy of using a constant lapse rate for hydrological modelling in high‐elevation regions like the TP. The geostatistical technique outperformed the Deterministic method, with KED realising the best temperature and precipitation interpolation performance based on cross‐validation results. However, although KED provides superior results based on cross‐validation performance, applying its precipitation interpolation as input into WaSiM led to the poorest discharge simulation. The combination of OK for precipitation and KED for temperature produced the most accurate discharge simulations in the LRB, highlighting the importance of not solely relying on cross‐validation results but also considering the practical implications of interpolation methods on hydrological model outputs. Our study offers a robust framework for improving flood simulations and water resource management in a data‐scarce, high‐elevation region like the TP.

NIERT: Accurate Numerical Interpolation Through Unifying Scattered Data Representations Using Transformer Encoder

NIERT: Accurate Numerical Interpolation Through Unifying Scattered Data Representations Using Transformer Encoder

Spatial Interpolation of Seasonal Precipitations Using Rain Gauge Data and Convection‐Permitting Regional Climate Model Simulations in a Complex Topographical Region

ABSTRACTIn mountainous areas, accurately estimating the long‐term climatology of seasonal precipitations is challenging due to the lack of high‐altitude rain gauges and the complexity of the topography. This study addresses these challenges by interpolating seasonal precipitation data from 3189 rain gauges across France over the 1982–2018 period, using geographical coordinates, and altitude. In this study, an additional predictor is provided from simulations of a Convection‐Permitting Regional Climate Model (CP‐RCM). The simulations are averaged to obtain seasonal precipitation climatology, which helps capture the relationship between topography and long‐term seasonal precipitation. Geostatistical and machine learning models are evaluated within a cross‐validation framework to determine the most appropriate approach to generate seasonal precipitation reference fields. Results indicate that the best model uses a machine learning approach to interpolate the ratio between long‐term seasonal precipitation from observations and CP‐RCM simulations. This method successfully reproduces both the mean and variance of observed data, and slightly outperforms the best geostatistical model. Moreover, incorporating the CP‐RCM outputs as an explanatory variable significantly improves interpolation accuracy and altitude extrapolation, especially when the rain gauge density is low. These results imply that the commonly used altitude‐precipitation relationship may be insufficient to derive seasonal precipitation fields. The CP‐RCM simulations, increasingly available worldwide, present an opportunity for improving precipitation interpolation, especially in sparse and complex topographical regions.

A Novel Interpolation Method for Soil Parameters Combining RBF Neural Network and IDW in the Pearl River Delta

Soil fertility is a critical factor in agricultural production, directly impacting crop growth, yield, and quality. To achieve precise agricultural management, accurate spatial interpolation of soil parameters is essential. This study developed a new interpolation prediction framework that combines Radial Basis Function (RBF) neural networks with Inverse Distance Weighting (IDW), termed the IDW-RBFNN. This framework initially uses the IDW method to apply preliminary weights based on distance to the data points, which are then used as input for the RBF neural network to form a training dataset. Subsequently, the RBF neural network further trains on these data to refine the interpolation results, achieving more precise spatial data interpolation. We compared the interpolation prediction accuracy of the IDW-RBFNN framework with ordinary Kriging (OK) and RBF methods under three different parameter settings. Ultimately, the IDW-RBFNN demonstrated lower error rates in terms of RMSE and MRE compared to direct RBF interpolation methods when adjusting settings based on different power values, even with a fixed number of data samples. As the sample size decreases, the interpolation accuracy of OK and RBF methods is significantly affected, while the error of IDW-RBFNN remains relatively low. Considering both interpolation accuracy and resource limitations, we recommend using the IDW-RBFNN method (p = 2) with at least 60 samples as the minimum sampling density to ensure high interpolation accuracy under resource constraints. Our method overcomes limitations of existing approaches that use fixed steady-state distance decay parameters, providing an effective tool for soil fertility monitoring in delta regions.

Evaluating Spatial Interpolation Maps of the Age Structure of the Population of Thi Qar Governorate Using Geographic Information Systems (GIS) Technologies

The research aims to harness spatial interpolation techniques to produce maps with a high level of perceptual accuracy in representing the population data of the study area. This is achieved after exploring the statistical and spatial nature of the databases used, analyzing them, and determining their distribution using a variety of spatial data exploration tools available within the GIS (Geographic Information Systems) environment. These tools contribute to evaluating the characteristics, distribution, and analysis of data, including testing data distribution, identifying its direction, and uncovering its spatial correlations. This highlights the importance of the study and understanding the distribution pattern of the population data in the study area, thereby facilitating the preparation of future that serve spatial organization by building spatial models for the numerical distribution of the data for age groups: young, middle-aged, and elderly, at the level of administrative units of the study area by relying on the inductive approach and the quantitative analysis approach. This is done using Geostatistical Analysis methods within the concept of spatial interpolation in GIS. The research also seeks to predict the population maps for Thi Qar Governorate by comparing spatial interpolation methods, namely Inverse Distance Weighting (IDW) and Simple Kriging (SK). The study concluded, after verifying the accuracy of the results using the Cross-Validation curve, that the Simple Kriging (SK) method is the most accurate model in spatial prediction of population data for representing the data of the young age group in the study area, as it had the lowest Root Mean Square Error (RMSE) of 10.012. It was followed by the Inverse Distance Weighting (IDW) method for representing the data of the elderly age group with an RMSE of 350.48. This indicates that the use of statistical criteria improves the accuracy of spatial interpolation and that the spatial prediction of the population data for the study area using the Simple Kriging (SK) method was more accurate than the Inverse Distance Weighting (IDW) method based on statistical indicators.

Heterogeneous Transfer Learning of Electrohydrodynamic Printing Under Zero-Gravity Toward In-Space Manufacturing

Abstract As we continue to commercialize space and mature in-space manufacturing (ISM) processes, there is a strong need to transfer the knowledge we learn from experiments on the ground to zero-gravity environments. Physics-motivated manufacturing processes, like additive manufacturing, experience a shift in fabrication parameters due to the absence of gravity and the change of environments. Thus, we found traditional machine learning methods are not capable of addressing this domain shift and present a transfer learning scheme as a solution in this paper. We tested a kernel ridge regression model built for heterogeneous transfer learning (KRR-HeITL) on data from the electrohydrodynamic inkjet printing (EHD printing) process. EHD printing is a process that uses electrical force to control material flows, thus achieving the fabrication of electronics without requiring gravity. Our team has successfully conducted three rounds of parabolic flights to validate this technology for ISM. We trained on multiple datasets built from on-ground experiments and tested using zero-gravity printing data obtained from parabolic flight tests. Measurements of the Taylor cone both on-ground and in zero-gravity were taken and exploited as a part of the training data. We found that our method obtains good interpolation accuracy (MAPE 3.85%) compared to traditional machine learning methods (MAPE 16.84%) for predicting the printed line width. We concluded that the KRR-HeITL method is well suited for zero-gravity domain shifts of EHD printing parameters. This study paves the way for future predictions of ISM parameters when there are only on-ground experiments or very limited zero-gravity datasets for a given process.