Extrapolation Errors Research Articles

Design of Tracking, Telemetry, Command (TT&C) and Data Transmission Integrated Signal in TDD Mode

For satellite or aircraft networks, tracking, telemetry, and control (TT&C) and data transmission between different nodes are necessary. Traditional measurement mostly adopts the frequency division duplex (FDD) mode and uses a continuous measurement system to achieve high-precision measurement. However, as the number of network nodes increases, the mode suffers from complex frequency domain allocation, and high-cost measurement and data transmission equipment is required. This paper proposes the integrated signal in time division duplex (TDD) mode to improve frequency utilization to address these circumstances. The proposed signal can transmit the TT&C and data at the same frequency. In addition, the high-precision time-frequency synchronization and relative measurement technology in the TDD mode for distributed spacecraft or aircraft networks are studied. The simulation results show that the signal can work normally when the Doppler extrapolation error is less than a quarter of the integration frequency. The distance extrapolation error should be less than a quarter of the length of a chip. The integrated signal reduces the frequency band occupation and realizes the integration of TT&C and data transmission. In addition, the measurement performance is reduced by only 2~3 dB compared with that of the traditional pure TT&C signal.

On the accuracy of a logarithmic extrapolation of the wind speed measured by horizontal lidar scans

Remote sensing-based wind power forecasts are nowadays being increasingly investigated. Long-range lidar scans are hereby often performed at low heights, causing the need for a wind speed extrapolation to hub height. In this work we analysed the accuracy of the stability corrected logarithmic wind profile and its sensitivity to atmospheric stability, wind speed and extrapolation height by means of a theoretical error estimation using error propagation. Emphasis was given to analyse the contributions of the profile’s individual variables but also considering the measurement campaign framework. We further used lidar measurements at the offshore wind farm Global Tech I to support the theoretical analysis. The logarithmic profile was found to be able to describe profiles during most situations, however, decreasing wind speeds with height cannot be represented. Results showed that due to the nature of the stability correction term extrapolation errors are largest during very stable atmospheric conditions. Here, stability estimation errors were dominant. Under near neutral and neutral atmospheric conditions the wind speed error contributed most to the overall error. We conclude that extrapolation errors can mainly be reduced by optimising the estimation of atmospheric stability using accurate measurement devices. Furthermore, the precise horizontal alignment of the lidar device is important.

Hadronic light-by-light contribution to (g-2)_mu from lattice QCD with SU(3) flavor symmetry

We perform a lattice QCD calculation of the hadronic light-by-light contribution to (g-2)_mu at the SU(3) flavor-symmetric point m_pi =m_Ksimeq 420,MeV. The representation used is based on coordinate-space perturbation theory, with all QED elements of the relevant Feynman diagrams implemented in continuum, infinite Euclidean space. As a consequence, the effect of using finite lattices to evaluate the QCD four-point function of the electromagnetic current is exponentially suppressed. Thanks to the SU(3)-flavor symmetry, only two topologies of diagrams contribute, the fully connected and the leading disconnected. We show the equivalence in the continuum limit of two methods of computing the connected contribution, and introduce a sparse-grid technique for computing the disconnected contribution. Thanks to our previous calculation of the pion transition form factor, we are able to correct for the residual finite-size effects and extend the tail of the integrand. We test our understanding of finite-size effects by using gauge ensembles differing only by their volume. After a continuum extrapolation based on four lattice spacings, we obtain a_mu ^{mathrm{hlbl}}= (65.4pm 4.9 pm 6.6)times 10^{-11}, where the first error results from the uncertainties on the individual gauge ensembles and the second is the systematic error of the continuum extrapolation. Finally, we estimate how this value will change as the light-quark masses are lowered to their physical values.

Decreasing wind speed extrapolation error via domain-specific feature extraction and selection

Abstract. Model uncertainty is a significant challenge in the wind energy industry and can lead to mischaracterization of millions of dollars' worth of wind resources. Machine learning methods, notably deep artificial neural networks (ANNs), are capable of modeling turbulent and chaotic systems and offer a promising tool to produce high-accuracy wind speed forecasts and extrapolations. This paper uses data collected by profiling Doppler lidars over three field campaigns to investigate the efficacy of using ANNs for wind speed vertical extrapolation in a variety of terrains, and it quantifies the role of domain knowledge in ANN extrapolation accuracy. A series of 11 meteorological parameters (features) are used as ANN inputs, and the resulting output accuracy is compared with that of both standard log-law and power-law extrapolations. It is found that extracted nondimensional inputs, namely turbulence intensity, current wind speed, and previous wind speed, are the features that most reliably improve the ANN's accuracy, providing up to a 65 % and 52 % increase in extrapolation accuracy over log-law and power-law predictions, respectively. The volume of input data is also deemed important for achieving robust results. One test case is analyzed in depth using dimensional and nondimensional features, showing that the feature nondimensionalization drastically improves network accuracy and robustness for sparsely sampled atmospheric cases.

Extrapolation of O-cell drilled shaft tests for load and resistance factor design (LRFD) calibration

Load tests of drilled shafts are often performed using Osterberg cell (O-cell) testing, a popular load test method for drilled shafts, which measures both side and tip resistance. However, it is common that only one of the resistance components can be fully mobilized. Therefore, extrapolation of the partially mobilized resistance is often required to determine the total resistance or the equivalent top-down curve. The extrapolation tends to introduce errors to the constructed total resistance values, which subsequently affect the calibrated resistance factors required for the LRFD design of drilled shafts. In this study, eight O-cell tests of drilled shafts with total measured resistances close to the failure criteria defined by FHWA, 5% of the shaft diameter (B), were collected among 64 drilled shaft load tests from Louisiana and Mississippi. For each of the eight cases, extrapolation was performed on both tip and side movement curves for the construction of the equivalent top-down load-settlement (ELT) curves. Data points from the measured side or tip movement curve were removed systematically to create a total of 80 cases with partially mobilized movement curves, and extrapolation exercises were performed on each fabricated case to obtain its equivalent top-down curve. The error of bias for each fabricated case was determined for statistical analyses. Multiple linear regression analysis was performed on the bias errors to model the bias errors caused by extrapolation. Calibrated resistance factors were determined and compared between the original database and fabricated database needing extrapolation. A correction method is proposed, based on a linear regression relationship, to estimate and minimize the extrapolation error of bias for less mobilized databases.

Deep learning based spectral extrapolation for dual-source, dual-energy x-ray computed tomography.

Data completion is commonly employed in dual-source, dual-energy computed tomography (CT) when physical or hardware constraints limit the field of view (FoV) covered by one of two imaging chains. Practically, dual-energy data completion is accomplished by estimating missing projection data based on the imaging chain with the full FoV and then by appropriately truncating the analytical reconstruction of the data with the smaller FoV. While this approach works well in many clinical applications, there are applications which would benefit from spectral contrast estimates over the larger FoV (spectral extrapolation)-e.g. model-based iterative reconstruction, contrast-enhanced abdominal imaging of large patients, interior tomography, and combined temporal and spectral imaging. To document the fidelity of spectral extrapolation and to prototype a deep learning algorithm to perform it, we assembled a data set of 50 dual-source, dual-energy abdominal x-ray CT scans (acquired at Duke University Medical Center with 5 Siemens Flash scanners; chain A: 50cm FoV, 100kV; chain B: 33cm FoV, 140kV+Sn; helical pitch: 0.8). Data sets were reconstructed using ReconCT (v14.1, Siemens Healthineers): 768×768pixels per slice, 50cm FoV, 0.75mm slice thickness, "Dual-Energy - WFBP" reconstruction mode with dual-source data completion. A hybrid architecture consisting of a learned piecewise linear transfer function (PLTF) and a convolutional neural network (CNN) was trained using 40 scans (five scans reserved for validation, five for testing). The PLTF learned to map chain A spectral contrast to chain B spectral contrast voxel-wise, performing an image domain analog of dual-source data completion with approximate spectral reweighting. The CNN with its U-net structure then learned to improve the accuracy of chain B contrast estimates by copying chain A structural information, by encoding prior chain A, chain B contrast relationships, and by generalizing feature-contrast associations. Training was supervised, using data from within the 33-cm chain B FoV to optimize and assess network performance. Extrapolation performance on the testing data confirmed our network's robustness and ability to generalize to unseen data from different patients, yielding maximum extrapolation errors of 26HU following the PLTF and 7.5HU following the CNN (averaged per target organ). Degradation of network performance when applied to a geometrically simple phantom confirmed our method's reliance on feature-contrast relationships in correctly inferring spectral contrast. Integrating our image domain spectral extrapolation network into a standard dual-source, dual-energy processing pipeline for Siemens Flash scanner data yielded spectral CT data with adequate fidelity for the generation of both 50keV monochromatic images and material decomposition images over a 30-cm FoV for chain B when only 20cm of chain B data were available for spectral extrapolation. Even with a moderate amount of training data, deep learning methods are capable of robustly inferring spectral contrast from feature-contrast relationships in spectral CT data, leading to spectral extrapolation performance well beyond what may be expected at face value. Future work reconciling spectral extrapolation results with original projection data is expected to further improve results in outlying and pathological cases.

Scalable Decision-Theoretic Planning in Open and Typed Multiagent Systems

In open agent systems, the set of agents that are cooperating or competing changes over time and in ways that are nontrivial to predict. For example, if collaborative robots were tasked with fighting wildfires, they may run out of suppressants and be temporarily unavailable to assist their peers. We consider the problem of planning in these contexts with the additional challenges that the agents are unable to communicate with each other and that there are many of them. Because an agent's optimal action depends on the actions of others, each agent must not only predict the actions of its peers, but, before that, reason whether they are even present to perform an action. Addressing openness thus requires agents to model each other's presence, which becomes computationally intractable with high numbers of agents. We present a novel, principled, and scalable method in this context that enables an agent to reason about others' presence in its shared environment and their actions. Our method extrapolates models of a few peers to the overall behavior of the many-agent system, and combines it with a generalization of Monte Carlo tree search to perform individual agent reasoning in many-agent open environments. Theoretical analyses establish the number of agents to model in order to achieve acceptable worst case bounds on extrapolation error, as well as regret bounds on the agent's utility from modeling only some neighbors. Simulations of multiagent wildfire suppression problems demonstrate our approach's efficacy compared with alternative baselines.

Improving Vertical Wind Speed Extrapolation Using Short-Term Lidar Measurements

This study investigates how short-term lidar measurements can be used in combination with a mast measurement to improve vertical extrapolation of wind speed. Several methods are developed and analyzed for their performance in estimating the mean wind speed, the wind speed distribution, and the energy yield of an idealized wind turbine at the target height of the extrapolation. These methods range from directly using the wind shear of the short-term measurement to a classification approach based on commonly available environmental parameters using linear regression. The extrapolation strategies are assessed using data of ten wind profiles up to 200 m measured at different sites in Germany. Different mast heights and extrapolation distances are investigated. The results show that, using an appropriate extrapolation strategy, even a very short-term lidar measurement can significantly reduce the uncertainty in the vertical extrapolation of wind speed. This observation was made for short as well as for very large extrapolation distances. Among the investigated methods, the linear regression approach yielded better results than the other methods. Integrating environmental variables into the extrapolation procedure further increased the performance of the linear regression approach. Overall, the extrapolation error in (theoretical) energy yield was decreased by around 50% to 70% on average for a lidar measurement of approximately one to two months depending on the extrapolation height and distance. The analysis of seasonal patterns revealed that appropriate extrapolation strategies can also significantly reduce the seasonal bias that is connected to the season during which the short-term measurement is performed.

Asymptotic behavior of the extrapolation error associated with the estimation of extreme quantiles

We investigate the asymptotic behavior of the (relative) extrapolation error associated with some estimators of extreme quantiles based on extreme-value theory. It is shown that the extrapolation error can be interpreted as the remainder of a first order Taylor expansion. Necessary and sufficient conditions are then provided such that this error tends to zero as the sample size increases. Interestingly, in case of the so-called Exponential Tail estimator, these conditions lead to a subdivision of Gumbel maximum domain of attraction into three subsets. In constrast, the extrapolation error associated with Weissman estimator has a common behavior over the whole Frechet maximum domain of attraction. First order equivalents of the extrapolation error are then derived and their accuracy is illustrated numerically.

Method of individual forecasting of sow reproductive performance on the basis of a non-linear canonical model of a random sequence

Improvement of sow reproductive performance is a key factor determining the efficiency of the pig production cycle and profitability of pork production. This article presents the solution of an important scientific and practical problem of individual forecasting of sow reproduction . The population used for the present study is from a pig farm managed by the Limited Liability Company (LLC) ‘Tavriys’kisvyni’ located in Skadovsky district (Kherson region, Ukraine). The experimental materials used for this study consisted of 100 inds. of productive parent sows of the Large White breed.The litter size traits – the total number of piglets born (TNB), number of piglets born alive (NBA) and number of weaned piglets (NW) – were monitored in the first eight parities over an eleven year period (2007–2017). The method of the forecasting of sow litter size is developed based on the non-linear canonical model of the random sequence of a litter size change. The proposed method allows us to take maximum account of stochastic peculiarities of sow reproductive performance and does not impose any restrictions on the random sequence of a litter size change (linearity, stationarity, Markov property, monotony, etc.). The block diagram of the algorithm presented in this work reflects the peculiarities of calculation of the parameters of a predictive model. The expression for the calculation of an extrapolation error allows us to estimate the necessary volume of a priori and a posteriori information for achieving the required quality of solving the forecasting problem. The results of the numerical experiment confirmed the high accuracy of the proposed method of forecasting of sow reproduction. The method offered by us almost doubles the accuracy of forecasting of sow litter size compared to the use of the Wiener and Kalman methods. Thus, average forecast error decreases across the range of features TNB (1.71), NBA (1.68) and NW (1.25 piglets). Apparently, this may reflect a higher level of manifestation of the genetically determined level of individual sow fertility at the moment of piglet weaning. The higher adequacy of the developed mathematical model with regard to NW can be also due to the fact that the relations between sow litter size in different farrowings primarily have a non-linear character, which is taken into maximum account in our offered model. Given non-linearity, on the other hand, turns out to be a significant factor determining a lower estimation of the repeatability value for NW compared to the estimations for TNB and NBA. The use of the developed method will help to improve the efficiency of pig farming.

Complete Basis Set Extrapolation of Electronic Correlation Energies Using the Riemann Zeta Function.

In this article, we demonstrate the effectiveness of the method of complete basis set (CBS) extrapolation of correlation energies based on the application of the Riemann zeta function. Instead of fitting the results obtained with a systematic sequence of one-electron bases with a certain functional form, an analytic resummation of the missing contributions coming from higher angular momenta, l, is performed. The assumption that these contributions vanish asymptotically as an inverse power of l leads to an expression for the CBS limit given in terms of the zeta function. This result is turned into an extrapolation method that is very easy to use and requires no "empirical" parameters to be optimized. The performance of the method is assessed by comparing the results with very accurate reference data obtained with explicitly correlated theories and with results obtained with standard extrapolation schemes. On average, the errors of the zeta-function extrapolation are several times smaller compared with the conventional schemes employing the same sequence of bases. A recipe for the estimation of the residual extrapolation error is also proposed.

Analysis of a constant-coefficient pressure equation method for fast computations of two-phase flows at high density ratios

An analysis of a modified pressure-correction formulation for fast simulations of fully resolved incompressible two-phase flows has been carried out. By splitting of the density weighted pressure gradient, the pressure equation is reduced to a constant-coefficient Poisson equation, for which efficient linear solvers can be used. While the gain in speed-up is well documented, the error introduced by the temporal extrapolation of the pressure gradient requires further investigations. In this paper it is shown that the modified pressure equation can lead to unphysical pressure oscillations and large errors. By appropriately combining the extrapolated pressure gradient with a matching volume fraction gradient grid convergence at high density ratios could be recovered. The cases of a one-dimensional front and a sphere translating at uniform velocity were first considered, allowing to decouple the pressure equation from the momentum equation. Subsequently, the case of a rising bubble in an upflow is analysed for which the full set of governing equations is solved. The pressure jump extrapolation error has been found dependent on the density ratio and the CFL number. Ultimately, the gain in the computational time, made possible by the use of fast Poisson solvers, should be weighted by the additional computational time the reduction of the aforementioned error may require.

Robust Linear Interpolation and Extrapolation of Stationary Time Series in Lp

To deal with uncertainty of the spectral distribution, we consider minimax interpolation and extrapolation problems in Lp for stationary processes. The interpolation and extrapolation problems can be regarded as a linear approximation problem on the unit disk in the complex plane. Although the robust one‐step‐ahead predictor and interpolator has already been considered separately in the previous literature, we give two conditions for the uncertainty class to find the minimax interpolator and extrapolator in the general framework from both the point of view of the observation set and the point of view of evaluation on the interpolation and extrapolation error under the Lp‐norm. We show that there exists a minimax interpolator and extrapolator for the class of spectral densities ε‐contaminated by unknown spectral densities under our conditions. When the uncertainty class contains spectral distribution functions which are not absolutely continuous to the Lebesgue measure, we show that there exists an approximate interpolator and extrapolator in Lp such that its maximal interpolation and extrapolation error is arbitrarily close to the minimax error when the spectral distributions have densities. Our results are applicable to the stationary harmonizable stable processes.

Load extrapolation computational algorithms for energy efficiency measurement testing of high‐power and high‐voltage induction motors

This paper deals with load extrapolation methods and algorithms for the estimation of the energy efficiency of induction motors, starting from the electrical data generally available after parameter identification. The main aim of this paper is to help the readers move from the theoretical analysis to the experimental verification, which is suitable to be included in its own testing. For this reason, step‐by‐step algorithms are included in this paper. The first part deals with the determination of energy efficiency based on an original load extrapolation. Finally, experimental verification of the high‐power and high‐voltage induction motors is provided. Comparison between the values of the in–out method and load extrapolation method confirms the validity of the proposed algorithms. Compared to the direct in–out method, the error of the load extrapolation proposed in this paper is less than 1%.The load extrapolation method is mainly used for the high‐voltage and high‐power asynchronous motor. The high‐voltage and high‐power motor can be used to drive a low‐power motor to achieve coaxial twin trawling in the absence of a suitable power supply and matched load. The energy efficiency of a high‐power motor under different loads are measured and estimated using a mathematical fitting method. The estimated energy efficiency curves in the whole load range can be used to evaluate the high‐power and high‐voltage induction motor. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Model averaging fails to improve the extrapolation capability of the island species–area relationship

AbstractAimOne of the main applications of the island species–area relationship (SAR) is to predict species richness in areas of habitat too large to be sampled, but there are few clear guidelines for choosing an appropriate model for this purpose. We therefore aimed to test whether a multi‐model averaging approach could improve the accuracy of predictions made by extrapolating the ISAR. Specifically, we compared the performance of multi‐model averaging with that of the default ISAR model of choice, the power model, in predicting species richness in large habitat islands.LocationGlobalTaxaVertebrates, invertebrates and plantsMethodsWe removed the largest islands from 120 habitat island datasets, and fitted both the power model and a multi‐model average curve (averaging the predictions of up to 20 ISAR models) to this filtered dataset. We then assessed the accuracy of both approaches in predicting the species richness of the largest island in the original dataset using the log error of extrapolation (LEE) metric. A generalized additive regression modelling framework was used to determine whether any dataset characteristics could explain variation in the LEE values for the power model (LEE‐POW).ResultsThe power model gave the more accurate richness predictions for 58% of the analysed datasets and the multi‐model averaged curve gave the more accurate predictions for the remaining 42%. Both the power models (61% of LEE‐POW values were positive) and the multi‐model averaged curve (60% were positive) had a slightly greater tendency to over predict the observed richness. The confidence intervals were also on average narrower for the power model predictions (median 95% confidence interval width = 18 species) than for the multi‐model averaged curve predictions (median 95% confidence interval width = 78). The range in island areas and richness values explained a small amount of the variation in LEE‐POW.Main conclusionsContrary to expectation, multi‐model averaging was less accurate than the power model in the majority of cases, and thus does not appear to be a panacea for uncertainty in model choice when extrapolating the ISAR. However, further research is urgently needed to evaluate the performance of a multi‐model averaging approach at larger spatial scales.

Extrapolation of the Maximum Usable Frequency by Adapting the Ionospheric IRI-2007 Model According to Oblique Sounding Data in the Euro-Asian Region

We present the results of prediction and extrapolation of the maximum usable frequency (MUF) on the subauroral and midlatitude paths of the Euro-Asian region by adapting the reference ionosphere model IRI-2007 based on the results of the oblique chirp sounding of the ionosphere on the reference and working radio links. It is shown that under conditions of a quiet ionosphere where the adapted ionospheric model is used, the errors of the MUF prediction on the reference path and the error of the MUF extrapolation to the neighboring paths not equipped with diagnostic tools are 2–8%, which is significantly less than the long-term prediction data which amount to 9–20% [1–3].

Toward Precise Gaze Estimation for Mobile Head-Mounted Gaze Tracking Systems

The gaze estimation in the mobile scenario often suffers from the extrapolation and parallax errors. In this paper, we propose a novel calibration framework to achieve the precise gaze estimation for head-mounted gaze trackers. Our proposed framework consists of two steps to learn a point-to-point and a point-to-line relations, respectively. The aim of step I is to infer the relation between pupil centers and spatially constrained points of regard. By adopting the “CalibMe” gaze data acquisition method, a sparse Gaussian Process using pseudo-inputs is used to capture the smooth residual field unmodeled by the polynomial function. Meanwhile, a distraction detection criterion is introduced to identify the moment when user's attention is taken away from the calibration point thereby removing outliers. By combining with the point-to-point relation inferred in step I, the observed parallax errors are leveraged in step II to obtain a point-to-line relation, i.e., each pupil center will correspond to an epipolar line. Thus, the real image gaze point projected from different depths is predicted as the intersection of two epipolar lines inferred from binocular data. The simulation and experimental results show the effectiveness of our proposed calibration framework for head-mounted gaze trackers.

Horizontal circulation and volume transports in the Weddell Gyre derived from Argo float data

Argo floats provide both hydrographic and trajectory data, affording the opportunity to investigate surface to mid-depth ocean dynamics. Here, Argo float data are used to determine the absolute geostrophic velocity field of the upper 50–2000 m of the Weddell Gyre, from which the overall circulation pattern is investigated. The Weddell Gyre plays a pivotal role in the modification of climate by advecting heat towards the Antarctic ice shelves and by modifying the water masses that feed into the lowest limb of the global ocean overturning circulation. Warm Deep Water, the source water mass that delivers heat to the Weddell Gyre, is conveniently located within the upper 2000 m domain covered by the floats; we investigate its volume transport as it circulates the gyre. Full depth volume transports are estimated by applying a quadratic function to extrapolate the relative dynamic height field component to the full ocean depth, using CTD profiles from ship-based surveys to determine an extrapolation error. Major new insights are provided by this study. There is an established double-gyre structure to the circulation, with a strong eastern cell and a weaker western cell. Regional variation of the baroclinic component of the flow field is revealed, indicating a northeast-to-southwest reduction in the baroclinic flow, along with a strong meridional gradient of baroclinic flow along the northern limb of the gyre, especially east of ∼25°W. The zonal mean gyre strength away from the shelf edge is 32 ± 5 Sv (1 Sv = 1 × 106 m3 s−1), of which 13 ± 3 Sv are associated with the advection of Warm Deep Water. There is a considerable amount of recirculation within the gyre interior, where water does not traverse the full zonal extent of the gyre. The recirculation is stronger in the eastern cell of the observed double-gyre structure. The interior circulation cells partly explain the large variations in previous gyre strength estimates. We provide an extensive review of previous estimates in context of the new results obtained.

Extending the coverage area of regional ionosphere maps using a support vector machine algorithm

Abstract. The coverage of regional ionosphere maps is determined by the distribution of ground-based monitoring stations, e.g., GNSS receivers. Since ionospheric delay has a high spatial correlation, ionosphere map coverage can be extended using spatial extrapolation methods. This paper proposes a support vector machine (SVM) to extrapolate the ionosphere map data with solar and geomagnetic parameters. One year of IGS ionospheric delay map data over South Korea is used to train the SVM algorithm. Subsequently, 1 month of ionospheric delay data outside the input data region is estimated. In addition to solar and geomagnetic environmental parameters, the ionospheric delay data from the inner data region are used to estimate the ionospheric delay data for the outside region. The accuracy evaluation is performed at three levels of range −5, 10, and 15∘ outside the inner data regions. The extrapolation errors are 0.33 TECU (total electron content unit) for the 5∘ region and 1.95 TECU for the 15∘ region. These values are substantially lower than the GPS Klobuchar model error values. Comparison with another machine learning extrapolation method, the neural network, shows a substantial improvement of up to 26.7 %.

Using Digital Image Correlation Measurements for the Inverse Identification of Constitutive Material Parameters applied in Metal Cutting Simulations

An inverse identification of material parameters has the potential to increase the quality of Finite Element-simulations in metal cutting. Since basic data for the calculations are measured under the thermomechanical load in a cutting situation, extrapolation errors can be reduced. Difficulties arise from the determination of the mechanical load inside the material during chip formation. Most methods use a chip-root-analysis, which is a post-process procedure. This paper presents a newly developed inverse method, applying optical measurements analyzed with a Digital Image Correlation (DIC) technique. In this way the shear strain rate is determined at cutting speeds and load conditions significant for industrial applications.