In-situ 3D contour measurement for laser powder bed fusion based on phase guidance

In-situ layerwise imaging measurement of laser powder bed fusion(LPBF)provides a wealth of forming and defect data which enables monitoring of components quality and powder bed homogeneity.Using high-resolution camera layerwise imaging and image processing algorithms to monitor fusion area and powder bed geometric defects has been studied by many researchers,which successfully monitored the contours of components and evaluated their accuracy.However,research for the methods of in-situ 3D contour measurement or component edge warping identification is rare.In this study,a 3D contour mea-surement method combining gray intensity and phase difference is proposed,and its accuracy is verified by designed experiments.The results show that the high-precision of the 3D contours can be achieved by the constructed energy minimization function.This method can detect the deviations of common ge-ometric features as well as warpage at LPBF component edges,and provides fundamental data for in-situ quality monitoring tools.

A light field measurement system through PSF estimation by a morphology-based method

Light field imaging technology can obtain three-dimensional(3D)information of a test surface in a single exposure.Traditional light field reconstruction algorithms not only take a long time to trace back to the original image,but also require the exact parameters of the light field system,such as the position and posture of a microlens array(MLA),which will cause errors in the reconstructed image if these parameters cannot be precisely obtained.This paper proposes a reconstruction algorithm for light field imaging based on the point spread function(PSF),which does not require prior knowledge of the system.The accurate PSF derivation process of a light field system is presented,and modeling and simulation were conducted to obtain the relationship between the spatial distribution characteristics and the PSF of the light field system.A morphology-based method is proposed to analyze the overlapping area of the subimages of light field images to identify the accurate spatial location of the MLA used in the system,which is thereafter used to accurately refocus light field imaging.A light field system is built to verify the algorithm’s effectiveness.Experimental results show that the measurement accuracy is increased over 41.0%compared with the traditional method by measuring a step standard.The accuracy of parameters is also improved through a microstructure measurement with a peak-to-valley value of 25.4%and root mean square value of 23.5%improvement.This further validates that the algorithm can effectively improve the refocusing efficiency and the accuracy of the light field imaging results with the superiority of refocusing light field imaging without prior knowledge of the system.The proposed method provides a new solution for fast and accurate 3D measurement based on a light field.

Research on inter-satellite measurement technique in high dynamic environment

An improved measurement algorithm,based upon the theory of two-way time transfer(TWTT),is proposed to measure satellites with high speeds.The algorithm makes theoretical analyses and corresponding deductions on a relative motion model of two satellites,and eliminates the measurement error caused by the equipment delay when a satellite moves at a high speed.Theoretical analysis and simulation results demonstrate that in comparison with the conventional TWTT algorithm,the proposed algorithm can significantly enhance the measurement accuracy of the inter-satellite ranging and time synchronization,and the algorithm is more effective with the relative velocity between the satellites and transmitting delay becoming larger.

THE INFLUENCE OF INHOMOGENEOUS WIND DISTRIBUTION ON DATA QUALITY FOR WIND PROFILING RADAR

Horizontal wind measured by wind profiling radar(WPR) is based on uniform wind assumption in volume of lateral beam. However, this assumption cannot completely meet in the real atmosphere. The subject of this work is to analyze the influence of atmospheric inhomogeneities for wind measurement. Five-beam WPR can measure two groups of horizontal wind components U and V independently, using the difference of horizontal wind components U and V can evaluate the influence of the inhomogeneity of the atmospheric motion on wind measurement. The influences can be divided into both inhomogeneous distribution of horizontal motion and vertical motion. Based on wind measurements and meteorological background information, a new means of coordinate rotation the two kinds of inhomogeneous factor was separated, and the impact in different weather background was discussed. From analysis of the wind measured by type of PB-II WPR(445MHz) during 2012 at Yanqing of Beijing, it is shown that the inhomogeneity of horizontal motion is nearly the same in U and V direction. Both the inhomogeneities of horizontal motion and vertical motion have influence on wind measurement, and the degrees of both influences are associated with changes of wind speed. In clear air, inhomogeneity of horizontal motion is the main influence on wind measurement because of small vertical velocity.In precipitation, the two influences are larger than that in clear air.

Semi-analytical assessment of the relative accuracy of the GNSS/INS in railway track irregularity measurements

An aided Inertial Navigation System(INS)is increasingly exploited in precise engineering surveying,such as railway track irregularity measurement,where a high relative measurement accuracy rather than absolute accuracy is emphasized.However,how to evaluate the relative measurement accuracy of the aided INS has rarely been studied.We address this problem with a semi-analytical method to analyze the relative measurement error propagation of the Global Navigation Satellite System(GNSS)and INS integrated system,specifically for the railway track irregularity measurement application.The GNSS/INS integration in this application is simplified as a linear time-invariant stochastic system driven only by white Gaussian noise,and an analytical solution for the navigation errors in the Laplace domain is obtained by analyzing the resulting steady-state Kalman filter.Then,a time series of the error is obtained through a subsequent Monte Carlo simulation based on the derived error propagation model.The proposed analysis method is then validated through data simulation and field tests.The results indicate that a 1 mm accuracy in measuring the track irregularity is achievable for the GNSS/INS integrated system.Meanwhile,the influences of the dominant inertial sensor errors on the final measurement accuracy are analyzed quantitatively and discussed comprehensively.

Notes on Rough Set Approximations and Associated Measures

We review and compare two definitions of rough set approximations.One is defined by a pair of sets in the universe and the other by a pair of sets in the quotient universe.The latter definition,although less studied,is semantically superior for interpreting rule induction and is closely related to granularity switching in granular computing.Numerical measures about the accuracy and quality of approximations are examined.Several semantics difficulties are commented.

Comparison of Two Time Series Decomposition Methods: Least Squares and Buys-Ballot Methods

This paper discusses comparison of two time series decomposition methods: The Least Squares Estimation (LSE) and Buys-Ballot Estimation (BBE) methods. As noted by Iwueze and Nwogu (2014), there exists a research gap for the choice of appropriate model for decomposition and detection of presence of seasonal effect in a series model. Estimates of trend parameters and seasonal indices are all that are needed to fill the research gap. However, these estimates are obtainable through the Least Squares Estimation (LSE) and Buys-Ballot Estimation (BBE) methods. Hence, there is need to compare estimates of the two methods and recommend. The comparison of the two methods is done using the Accuracy Measures (Mean Error (ME)), Mean Square Error (MSE), the Mean Absolute Error (MAE), and the Mean Absolute Percentage Error (MAPE). The results from simulated series show that for the additive model;the summary statistics (ME, MSE and MAE) for the two estimation methods and for all the selected trending curves are equal in all the simulations both in magnitude and direction. For the multiplicative model, results show that when a series is dominated by trend, the estimates of the parameters by both methods become less precise and differ more widely from each other. However, if conditions for successful transformation (using the logarithmic transform in linearizing the multiplicative model to additive model) are met, both of them give similar results.

Objective Model Selection in Physics: Exploring the Finite Information Quantity Approach

Traditional methods for selecting models in experimental data analysis are susceptible to researcher bias, hindering exploration of alternative explanations and potentially leading to overfitting. The Finite Information Quantity (FIQ) approach offers a novel solution by acknowledging the inherent limitations in information processing capacity of physical systems. This framework facilitates the development of objective criteria for model selection (comparative uncertainty) and paves the way for a more comprehensive understanding of phenomena through exploring diverse explanations. This work presents a detailed comparison of the FIQ approach with ten established model selection methods, highlighting the advantages and limitations of each. We demonstrate the potential of FIQ to enhance the objectivity and robustness of scientific inquiry through three practical examples: selecting appropriate models for measuring fundamental constants, sound velocity, and underwater electrical discharges. Further research is warranted to explore the full applicability of FIQ across various scientific disciplines.