Real-Time Sensing of Output Polymer Flow Temperature and Volumetric Flowrate in Fused Filament Fabrication Process

In this paper we addressed key challenges in engineering an instrumentation system for sensing and signal processing for real-time estimation of two main process variables in the Fused-Filament-Fabrication process: (i) temperature of the polymer melt exiting the nozzle using a thermocouple; and (ii) polymer flowrate using extrusion width measurements in real-time, in-situ, using a microscope camera. We used a design of experiments approach to develop response surface models for two materials that enable accurate estimation of the polymer exit temperature as a function of polymer flowrate and liquefier temperature with a fit of 𝑅2=99.96% and 99.39%. The live video stream of the deposition process was used to compute the flowrate based on a road geometry model. Specifically, a robust extrusion width recognizer algorithm was developed to identify edges of the deposited road and for real-time computation of extrusion width, which was found to be robust to filament colors and materials. The extrusion width measurement was found to be within 0.08 mm of caliper measurements with an 𝑅2 value of 99.91% and was found to closely track the requested flowrate from the slicer. This opens new avenues for advancing the engineering science for process monitoring and control of FFF.

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A new algorithm for build time estimation for fused filament fabrication technologies

Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture ◽

10.1177/0954405416640661 ◽

2016 ◽

Vol 230 (12) ◽

pp. 2214-2228 ◽

Cited By ~ 12

Author(s):

Zicheng Zhu ◽

Vimal Dhokia ◽

Stephen T Newman

Keyword(s):

Time Estimation ◽

Numerical Control ◽

Accurate Estimation ◽

Fused Deposition Modelling ◽

Hybrid Manufacturing ◽

Production Operation ◽

Fused Filament Fabrication ◽

Fused Deposition ◽

Wide Range ◽

Build Time

The manufacture of highly complex and accurate part geometries with reduced costs has led to the emergence of hybrid manufacturing technologies where varied manufacturing operations are carried out in either parallel or serial manner. One such hybrid process being currently developed is the iAtractive process, which combines additive (i.e. fused filament fabrication, which is sometimes called fused deposition modelling. However, the latter term is trademarked by Stratasys Inc. and cannot be used publicly without authorisation from Stratasys) and subtractive (i.e. computer numerical control machining) processes. In the iAtractive process production, operation sequencing of additive and subtractive operations is essential. This requires accurate estimation of production time, in which the fused filament fabrication build time is the determining factor. There have been some estimators developed for fused deposition modelling. However, these estimators are not applicable to hybrid manufacturing, particularly in process planning, which is a vital stage. This article addresses the characteristics of fused filament fabrication technologies and develops a novel and rigorous method for predicting build times. An analytical model was first created to theoretically analyse the factors that affect the part build time and was subsequently used to facilitate the design of test parts and experiments. The experimental results indicate that part volume, interaction of volume and porosity and interaction of height and intermittent factor have significant effects on build times. Finally, the estimation algorithm has been developed, which was subsequently evaluated and validated by applying a wide range of identified influential factors. The major advantage of the new proposed algorithm is its ability to estimate the build time based on simple geometrical parameters of a given part. The key factors that drive the algorithm can be directly obtained from part dimensions/drawings, providing an efficient and accurate way for fused filament fabrication time estimation. Test part evaluations and analysis have clearly demonstrated that estimation errors range from 0.1% to 13.5%, showing the validity, capability and significance of the developed algorithm and its applications to hybrid manufacture.

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Real-Time Estimation of Origin–Destination Matrices with Partial Trajectories from Electronic Toll Collection Tag Data

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198105192300113 ◽

2005 ◽

Vol 1923 (1) ◽

pp. 119-126 ◽

Cited By ~ 5

Author(s):

Jaimyoung Kwon ◽

Pravin Varaiya

Keyword(s):

Statistical Model ◽

Real Time ◽

San Francisco ◽

Time Estimation ◽

Sample Survey ◽

Accurate Estimation ◽

Electronic Toll Collection ◽

Matrix Estimation ◽

Bay Area ◽

Toll Collection

The origin–destination (O-D) matrix of a traffic network is usually estimated from link traffic counts combined with a sample survey. Partially observed vehicle trajectories obtained with vehicle reidentification or automatic vehicle identification techniques such as electronic tags provide a new data source for real-time O-D matrix estimation. However, because of incomplete sampling, accurate estimation of O-D matrices from these data is not trivial. A statistical model was developed for such data, and an unbiased estimator of the O-D matrix was derived based on the method of moments. With further exploitation of the sound statistical model, the bootstrap standard error estimate of the O-D matrix estimator was also developed. The algorithm can be computed quickly and performs well under simulation compared with simpler estimators. Applied to data from vehicles with electronic toll collection tags in the San Francisco Bay Area, the algorithm produces a realistic time series of the hourly O-D matrix. The relationship of the proposed estimator with similar methods in the literature was also studied and extension of the methods to general, more complex networks is discussed.

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Robust and Timely Estimation of Excess Mortality: Statistical Analysis of Cremation Data During the COVID-19 Pandemic (Preprint)

10.2196/preprints.32426 ◽

2021 ◽

Author(s):

Gemma Postill ◽

Regan Murray ◽

Andrew S Wilton ◽

Richard A Wells ◽

Renee Sirbu ◽

...

Keyword(s):

Real Time ◽

Excess Mortality ◽

Age Groups ◽

Time Estimation ◽

Vital Statistics ◽

Accurate Estimation ◽

Mortality Data ◽

Mortality Estimation ◽

Statistics Canada ◽

The Impact

BACKGROUND Early estimates of excess mortality are crucial for understanding the impact of COVID-19. However, there is a lag of several months in the reporting of vital statistics mortality data for many jurisdictions. In Ontario, a Canadian province, certification by a coroner is required before cremation can occur, creating timely mortality data that encompasses the majority of deaths within the province. OBJECTIVE Our objectives were to (1) validate the ability of cremation data in permitting real-time estimation of excess all-cause mortality, interim of vital statistics data, and (2) describe the patterns of excess mortality. METHODS Cremation records from January 2020 until April 2021 were compared to the historical records from 2017-2019, grouped according to week, age, sex, and COVID-19 status. Cremation data were compared to Ontario’s provisional vital statistics mortality data released by Statistics Canada. The 2020 and 2021 records were then compared to previous years to determine whether there was excess mortality and if so, which age groups had the greatest number of excess deaths during the COVID Pandemic, and whether deaths attributed to COVID-19 account for the entirety of the excess mortality. RESULTS Between 2017-2019, cremations were performed for 67.4% (95% CI: 67.3–67.5%) of deaths; the proportion of cremated deaths remained stable throughout 2020, establishing that the COVID-19 pandemic did not significantly alter cremation practices, even within age and sex categories. During the first wave (from April to June 2020), cremation records detected a 16.9% increase (95% CI: 14.6–19.3%) in mortality. The accuracy of this excess mortality estimation was later confirmed by vital statistics data. CONCLUSIONS The stability in the percent of Ontarians cremated and the completion of cremation data several months before vital statistics data, enables accurate estimation of all-causes mortality in near real-time with cremation data. These findings demonstrate the utility of cremation data to provide timely mortality information during public health emergencies.

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Real-Time Cuffless Continuous Blood Pressure Estimation Using Deep Learning Model

Sensors ◽

10.3390/s20195606 ◽

2020 ◽

Vol 20 (19) ◽

pp. 5606

Author(s):

Yung-Hui Li ◽

Latifa Nabila Harfiya ◽

Kartika Purwandari ◽

Yue-Der Lin

Keyword(s):

Blood Pressure ◽

Deep Learning ◽

Real Time ◽

Short Term Memory ◽

Blood Pressure Monitoring ◽

Time Estimation ◽

Learning Model ◽

Accurate Estimation ◽

Arterial Blood ◽

Deep Learning Model

Blood pressure monitoring is one avenue to monitor people’s health conditions. Early detection of abnormal blood pressure can help patients to get early treatment and reduce mortality associated with cardiovascular diseases. Therefore, it is very valuable to have a mechanism to perform real-time monitoring for blood pressure changes in patients. In this paper, we propose deep learning regression models using an electrocardiogram (ECG) and photoplethysmogram (PPG) for the real-time estimation of systolic blood pressure (SBP) and diastolic blood pressure (DBP) values. We use a bidirectional layer of long short-term memory (LSTM) as the first layer and add a residual connection inside each of the following layers of the LSTMs. We also perform experiments to compare the performance between the traditional machine learning methods, another existing deep learning model, and the proposed deep learning models using the dataset of Physionet’s multiparameter intelligent monitoring in intensive care II (MIMIC II) as the source of ECG and PPG signals as well as the arterial blood pressure (ABP) signal. The results show that the proposed model outperforms the existing methods and is able to achieve accurate estimation which is promising in order to be applied in clinical practice effectively.

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Build Time Estimation for Fused Filament Fabrication via Average Printing Speed

Materials ◽

10.3390/ma12233982 ◽

2019 ◽

Vol 12 (23) ◽

pp. 3982 ◽

Cited By ~ 1

Author(s):

Gustavo Medina-Sanchez ◽

Rubén Dorado-Vicente ◽

Eloísa Torres-Jiménez ◽

Rafael López-García

Keyword(s):

Estimation Method ◽

Time Estimation ◽

Accurate Estimation ◽

Maximum Speed ◽

Maximum Relative Error ◽

Fused Filament Fabrication ◽

Fitting Procedure ◽

Build Time ◽

Analytical Approaches ◽

Printing Speed

Build time is a key issue in additive manufacturing, but even nowadays, its accurate estimation is challenging. This work proposes a build time estimation method for fused filament fabrication (FFF) based on an average printing speed model. It captures the printer kinematics by fitting printing speed measurements for different interpolation segment lengths and changes of direction along the printing path. Unlike analytical approaches, printer users do not need to know the printer kinematics parameters such as maximum speed and acceleration or how the printer movement is programmed to obtain an accurate estimation. To build the proposed model, few measurements are needed. Two approaches are proposed: a fitting procedure via linear and power approximations, and a Coons patch. The procedure was applied to three desktop FFF printers, and different infill patterns and part shapes were tested. The proposed method provides a robust and accurate estimation with a maximum relative error below 8.5%.

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Real-time spectrographic analysis of the melt pool during direct laser deposition process

10.2351/1.5061265 ◽

2008 ◽

Author(s):

Konrad Bartkowiak

Keyword(s):

Real Time ◽

Deposition Process ◽

Laser Deposition ◽

Spectrographic Analysis ◽

Direct Laser Deposition ◽

Melt Pool ◽

Direct Laser

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Real Time Automated Vehicle Monitoring and Control System Using Internet of Vehicles

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v6i3.491494 ◽

2018 ◽

Vol 6 (3) ◽

pp. 491-494

Author(s):

Nalina V. ◽

◽

P. Jayarekha ◽

Keyword(s):

Control System ◽

Real Time ◽

Monitoring And Control ◽

Monitoring And Control System ◽

Internet Of Vehicles ◽

Automated Vehicle ◽

Vehicle Monitoring ◽

And Control

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A machine for real-time estimation of the tribological characteristics of materials

Industrial laboratory Diagnostics of materials ◽

10.26896/1028-6861-2020-86-4-61-65 ◽

2020 ◽

Vol 86 (4) ◽

pp. 61-65

Author(s):

M. V. Abramchuk ◽

R. V. Pechenko ◽

K. A. Nuzhdin ◽

V. M. Musalimov

Keyword(s):

Real Time ◽

Time Estimation ◽

Tribological Characteristics ◽

Continuous Processing ◽

Friction Machine ◽

Dynamic Friction Coefficient ◽

Displacement Sensors ◽

Frictional Interaction ◽

Real Time Estimation ◽

Automated Quality Control

A reciprocating friction machine Tribal-T intended for automated quality control of the rubbing surfaces of tribopairs is described. The distinctive feature of the machine consists in implementation of the forced relative motion due to the frictional interaction of the rubbing surfaces fixed on the drive and conjugate platforms. Continuous processing of the signals from displacement sensors is carried out under conditions of continuous recording of mutual displacements of loaded tribopairs using classical approaches of the theory of automatic control to identify the tribological characteristics. The machine provides consistent visual real time monitoring of the parameters. The MATLAB based computer technologies are actively used in data processing. The calculated tribological characteristics of materials, i.e., the dynamic friction coefficient, damping coefficient and measure of the surface roughness, are presented. The tests revealed that a Tribal-T reciprocating friction machine is effective for real-time study of the aforementioned tribological characteristics of materials and can be used for monitoring of the condition of tribo-nodes of machines and mechanisms.

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