In-Line Monitoring and Control of Rheological Properties through Data-Driven Ultrasound Soft-Sensors

Tronci, Stefania; Neer, P.L.M.J. van; Giling, E.J.M.; Stelwagen, U.; Piras, Daniele; Mei, Roberto; Corominas, Francesc; Grosso, Massimiliano

doi:10.3390/s19225009

Cited by 6 publications

(5 citation statements)

References 21 publications

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“…Ref. [22] The use of X-rays is still uncommon in this area due to the high resolution necessary for the examination of particles in the submicrometre range and the resulting long scan times as well as the complex sample preparation. Usually the distribution of particles is determined offline by laser diffraction and the material composition by EDX [21].…”

Section: Production Of Electrodesmentioning

confidence: 99%

Applications and Development of X-ray Inspection Techniques in Battery Cell Production

et al. 2022

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Demand for lithium-ion battery cells (LIB) for electromobility has risen sharply in recent years. In order to continue to serve this growing market, large-scale production capacities require further expansion and the overall effectiveness of processes must be increased. Effectiveness can be significantly optimized through innovative manufacturing technology and by identifying scrap early in the production chain. To enable these two approaches, it is imperative to quantify safety- and function-critical product features in critical manufacturing steps through appropriate measurement techniques. The overview in this paper on quality control in LIB production illustrates the necessity for improved inspection techniques with X-rays to realize a fast, online measurement of inner features in large-scale cell assembly with short cycle times and to visualize inner product-process interactions for the optimization in electrolyte filling. Therefore, two new inspection techniques are presented that contribute to overcoming the aforementioned challenges through the targeted use of X-rays. First, based on the results of previous experiments in which the X-ray beam directions were deliberately varied, a online coordinate measurement of anode-cathode (AC) overhang was developed using a line detector. Second, a new concept and the results of a continuous 2D visualization of the electrolyte filling process are presented, which can be used in the future to optimize this time-critical process step. By using a X-ray-permeable and portable vacuum chamber it is possible to quantify the influence of process parameters on the distribution of the electrolyte in the LIB.

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Section: Production Of Electrodesmentioning

confidence: 99%

Applications and Development of X-ray Inspection Techniques in Battery Cell Production

et al. 2022

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“…A simple high-pass filter (at least the mean must be removed), is followed by windowing and Fast Fourier Transform (FFT) [19]. The use of more sophisticated adaptive estimators instead of FFT have been reported in literature as well [15,20,21]; however, more sophisticated adaptive estimators require a higher calculation power that makes the real-time hardware implementation more problematic. This procedure is applied to all the depths of the demodulated data matrix s SWDn (d, l), for obtaining the corresponding sequences of power spectral density lines:…”

Section: Power Spectral Estimationmentioning

confidence: 99%

FPGA-Based Doppler Frequency Estimator for Real-Time Velocimetry

Ricci

Meacci

2020

Electronics

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In range-Doppler ultrasound applications, the velocity of a target can be measured by transmitting a mechanical wave, and by evaluating the Doppler shift present on the received echo. Unfortunately, detecting the Doppler shift from the received Doppler spectrum is not a trivial task, and several complex estimators, with different features and performance, have been proposed in the literature for achieving this goal. In several real-time applications, hundreds of thousands of velocity estimates must be produced per second, and not all of the proposed estimators are capable of performing at these high rates. In these challenging conditions, the most widely used approaches are the full centroid frequency estimate or the simple localization of the position of the spectrum peak. The first is more accurate, but the latter features a very quick and straightforward implementation. In this work, we propose an alternative Doppler frequency estimator that merges the advantages of the aforementioned approaches. It exploits the spectrum peak to get an approximate position of the Doppler frequency. Then, centered in this position, a centroid search is applied on a reduced frequency interval to refine the estimate. Doppler simulations are used to compare the accuracy and precision performance of the proposed algorithm with respect to current state of the art approaches. Finally, a Field Programmable Gate Array (FPGA) implementation is proposed that is capable of producing more than 200 k low noise estimates per second, which is suitable for the most demanding real-time applications.

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“…Some of these applications are drilling optimization [40], optimizing drilling hydraulics [41], and prediction of rheological properties of invert emulsion mud, KCl water-based mud, CaCl 2 drilling fluid, NaCl water-based drill-in fluid rheological properties [42][43][44][45]. Additionally, new systems were developed using the integration between sensitive sensors measurements and AI application to estimate rheological parameters of non-Newtonian fluids [46]. Furthermore, an automated Marsh funnel was developed using data-driven sensors to allow real-time measurement of FV [47].…”

Section: Implementation Of Artificial Neural Network (Ann) To Predict Hbm Rheologymentioning

confidence: 99%

“…Table 3 rheological properties [42][43][44][45]. Additionally, new systems were developed using the integration between sensitive sensors measurements and AI application to estimate rheological parameters of non-Newtonian fluids [46]. Furthermore, an automated Marsh funnel was developed using datadriven sensors to allow real-time measurement of FV [47].…”

Section: Data Descriptionmentioning

confidence: 99%

Newly Developed Correlations to Predict the Rheological Parameters of High-Bentonite Drilling Fluid Using Neural Networks

Gowida

Elkatatny

Abdelgawad

et al. 2020

Sensors

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High-bentonite mud (HBM) is a water-based drilling fluid characterized by its remarkable improvement in cutting removal and hole cleaning efficiency. Periodic monitoring of the rheological properties of HBM is mandatory for optimizing the drilling operation. The objective of this study is to develop new sets of correlations using artificial neural network (ANN) to predict the rheological parameters of HBM while drilling using the frequent measurements, every 15 to 20 min, of mud density (MD) and Marsh funnel viscosity (FV). The ANN models were developed using 200 field data points. The dataset was divided into 70:30 ratios for training and testing the ANN models respectively. The optimized ANN models showed a significant match between the predicted and the measured rheological properties with a high correlation coefficient (R) higher than 0.90 and a maximum average absolute percentage error (AAPE) of 6%. New empirical correlations were extracted from the ANN models to estimate plastic viscosity (PV), yield point (YP), and apparent viscosity (AV) directly without running the models for easier and practical application. The results obtained from AV empirical correlation outperformed the previously published correlations in terms of R and AAPE.

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In-Line Monitoring and Control of Rheological Properties through Data-Driven Ultrasound Soft-Sensors

Cited by 6 publications

References 21 publications

Applications and Development of X-ray Inspection Techniques in Battery Cell Production

Applications and Development of X-ray Inspection Techniques in Battery Cell Production

FPGA-Based Doppler Frequency Estimator for Real-Time Velocimetry

Newly Developed Correlations to Predict the Rheological Parameters of High-Bentonite Drilling Fluid Using Neural Networks

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