scholarly journals Development of an Additive Manufactured Artifact to Characterize Unfused Powder Using Computed Tomography

2020 ◽  
Vol 14 (3) ◽  
pp. 439-446 ◽  
Author(s):  
Ahmed Tawfik ◽  
◽  
Paul Bills ◽  
Liam Blunt ◽  
Radu Racasan
Keyword(s):  
Computed Tomography ◽  
Data Analysis ◽  
Additive Manufacturing ◽  
Voxel Size ◽  
Core Technology ◽  
Scan Time ◽  
Time Required ◽  
Porosity Analysis ◽  
The Impact ◽  
Subtractive Machining

Additive manufacturing (AM) is recognized as a core technology for producing high-value components. The production of complex and individually modified components, as well as prototypes, gives additive manufacturing a substantial advantage over conventional subtractive machining. For most industries, some of the current barriers to implementing AM include the lack of build repeatability and a deficit of quality assurance standards. The mechanical properties of the components depend critically on the density achieved. Therefore, defect/porosity analysis must be carried out to verify the components’ integrity and viability. In parts produced using AM, the detection of unfused powder using computed tomography is challenging because the detection relies on differences in density. This study presents an optimized methodology for differentiating between unfused powder and voids in additive manufactured components, using computed tomography. Detecting the unfused powder requires detecting the cavities between particles. Previous studies have found that the detection of unfused powder requires a voxel size that is as small as 4 μm3. For most applications, scanning using a small voxel size is not reasonable because of the part size, long scan time, and data analysis. In this study, different voxel sizes are used to compare the time required for scanning, and the data analysis showing the impact of voxel size on the detection of micro defects. The powder used was Ti6Al4V, which has a grain size of 45–100 μm, and is typically employed by Arcam electron beam melting (EBM) machines. The artifact consisted of a 6 mm round bar with designed internal features ranging from 50 μm to 1400 μm and containing a mixture of voids and unfused powder. The diameter and depth of the defects were characterized using a focus variation microscope, after which they were scanned using a Nikon XTH225 industrial CT to measure the artifacts and characterize the internal features for defects/pores. To reduce the number of the process variables, the measurement parameters, such as filament current, acceleration voltage, and X-ray filtering material and thickness were kept constant. The VGStudio MAX 3.0 (Volume Graphics, Germany) software package was used for data processing, surface determination, and defects/porosity analysis. The main focus of this study is to explore the optimal methods for enhancing the detection of pores/defects while minimizing the time taken for scanning, data analysis, and determining the effects of noise on the analysis.

Impact of voxel size and scan time on the accuracy of three-dimensional radiological imaging data from cone-beam computed tomography

2018 ◽  
Vol 46 (12) ◽  
pp. 2190-2196 ◽  
Author(s):  
Eva Dach ◽  
Bastian Bergauer ◽  
Anna Seidel ◽  
Cornelius von Wilmowsky ◽  
Werner Adler ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Cone Beam Computed Tomography ◽  
Three Dimensional ◽  
Cone Beam ◽  
Radiological Imaging ◽  
Imaging Data ◽  
Voxel Size ◽  
Scan Time

Influences of Surface Determination for Measurements Obtained by Industrial Computed Tomography

2014 ◽  
Vol 683 ◽  
pp. 142-146 ◽  
Author(s):  
Teodor Tóth ◽  
Alexander Végh ◽  
Miroslav Dovica ◽  
Jozef Živčák
Keyword(s):  
Computed Tomography ◽  
Conveyor Belt ◽  
Digital Data ◽  
Output Control ◽  
Measuring Devices ◽  
Industrial Computed Tomography ◽  
Conveyor Belts ◽  
Porosity Analysis ◽  
Visual Shape ◽  
The Impact

At present emphasis is placed on input or output control of products in the manufacturing process. One of the criteria is the dimensional analysis or porosity analysis of products. In the case that products are of complicated shapes, or measurements are to be taken in places which are not accessible to standard measuring devices (manual measurements, coordinate measuring machines) the use of computed tomography is one of the possibilities for obtaining the desired dimensions. These technologies work with digital data and therefore the surfaces which are to be assessed must be created on the basis of determined criteria. Surface determination is one of the most important settings during the evaluation of the visual shape (state) of a surface and assessing dimensions. During such evaluation this is the main parameter which globally influences the precision of the obtained data. In the case of an unsuitably determined surface the obtained results can vary from reality even by several tenths of a millimeter depending on the scanned object and the scanning conditions. The conveyor belts are composed of two or more materials with different densities, such as rubber and textile fibers. In cooperation with Faculty of Mining, Ecology, Process Control and Geotechnology is tested and evaluated the effect of the impactor with various energy into conveyor belt. The correct surface determination for selected material is necessary to evaluate the dimensions, the damage and pores. The article deals with the impact of surface determination on the result of a measurement.

scholarly journals The Detection of Unfused Powder in EBM and SLM Additive Manufactured Components

2020 ◽  
Vol 14 (6) ◽  
pp. 1025-1035
Author(s):  
Ahmed Tawfik ◽  
Mohamed Radwan ◽  
Mazen Ahmed Attia ◽  
Paul Bills ◽  
Radu Racasan ◽  
...  
Keyword(s):  
Density Measurement ◽  
Spherical Shape ◽  
Powder Size ◽  
Voxel Size ◽  
Volume Graphics ◽  
Core Technology ◽  
Powder Particles ◽  
X Ray Computed ◽  
Quality Assurance Standards ◽  
Subtractive Machining

Additive manufacturing (AM) is recognized as a core technology for producing high value, complex, and individually designed components as well as prototypes, giving AM a significant advantage over subtractive machining. Selective laser melting (SLM) or electron beam melting (EBM) are two of the main technologies used for producing metal components. The powder size varies, depending on the technology and manufacturer, from 20–50 μm for SLM and 45–100 μm for EBM. One of the current barriers for implementing AM for most industries is the lack of build repeatability and a deficit in quality assurance standards. The mechanical properties of the components depend critically on the density achieved; therefore, defect analysis and detection of unfused powder must be carried out to verify the integrity of the components. Detecting unfused powder in AM parts using X-ray computed tomography (XCT) is challenging because detection relies on variations in density. Unfused particles have the same density as the manufactured parts; therefore, detection is difficult using standard methods for density measurement. This study presents a methodology to detect unfused powders in SLM and EBM-manufactured components. Aluminum and titanium artefacts with designed internal defects filled with unfused powder are scanned with XCT and the results are analyzed with VGSTUDIO Max 3.0 (Volume Graphics, Germany) software package. Preliminary results indicate that detecting unfused powder in an aluminum SLM artifact with a 9.5 μm voxel size is achievable. This is possible because of the size of the voids between the powder particles and the non-uniform shape of the particles. Conversely, detecting unfused powder in the EBM-manufactured titanium artifact is less challenging owing to the uniform spherical shape and slightly larger size of the particles.

INDONESIA LANGUAGE INTERFERENCE FORM IN BASIC PPL LEARNING THAILAND UNHASY COLLEGE SYUDENTS

2020 ◽  
Vol 8 (2) ◽  
pp. 38
Author(s):  
Siti Mariana Ulfa
Keyword(s):  
Qualitative Research ◽  
Data Analysis ◽  
Data Collection ◽  
Teaching Practice ◽  
Data Presentation ◽  
Analysis Techniques ◽  
Thai Students ◽  
The Impact ◽  
Collection Methods ◽  
Recording Techniques

AbstractHumans on earth need social interaction with others. Humans can use more than one language in communication. Thus, the impact that arises when the use of one or more languages is the contact between languages. One obvious form of contact between languages is interference. Interference can occur at all levels of life. As in this study, namely Indonesian Language Interference in Learning PPL Basic Thailand Unhasy Students. This study contains the form of interference that occurs in Thai students who are conducting teaching practices in the classroom. This type of research is descriptive qualitative research that seeks to describe any interference that occurs in the speech of Thai students when teaching practice. Data collection methods in this study are (1) observation techniques, (2) audio-visual recording techniques using CCTV and (3) recording techniques, by recording all data that has been obtained. Whereas the data wetness uses, (1) data triangulation, (2) improvement in perseverance and (3) peer review through discussion. Data analysis techniques in this study are (1) data collection, (2) data reduction, (3) data presentation and (4) conclusions. It can be seen that the interference that occurs includes (1) interference in phonological systems, (2) interference in morphological systems and (3) interference in syntactic systems. 

scholarly journals Data augmentation for computed tomography angiography via synthetic image generation and neural domain adaptation

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Malte Seemann ◽  
Lennart Bargsten ◽  
Alexander Schlaefer
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Deep Learning ◽  
Medical Imaging ◽  
Computed Tomography Angiography ◽  
Data Augmentation ◽  
Domain Adaptation ◽  
Synthetic Image ◽  
Wide Range ◽  
The Impact

AbstractDeep learning methods produce promising results when applied to a wide range of medical imaging tasks, including segmentation of artery lumen in computed tomography angiography (CTA) data. However, to perform sufficiently, neural networks have to be trained on large amounts of high quality annotated data. In the realm of medical imaging, annotations are not only quite scarce but also often not entirely reliable. To tackle both challenges, we developed a two-step approach for generating realistic synthetic CTA data for the purpose of data augmentation. In the first step moderately realistic images are generated in a purely numerical fashion. In the second step these images are improved by applying neural domain adaptation. We evaluated the impact of synthetic data on lumen segmentation via convolutional neural networks (CNNs) by comparing resulting performances. Improvements of up to 5% in terms of Dice coefficient and 20% for Hausdorff distance represent a proof of concept that the proposed augmentation procedure can be used to enhance deep learning-based segmentation for artery lumen in CTA images.

scholarly journals Impact of the COVID-19 pandemic on neuroimaging scan volumes at a teaching hospital

2021 ◽  
pp. 197140092098866
Author(s):  
Daniel Thomas Ginat ◽  
James Kenniff
Keyword(s):  
Magnetic Resonance Imaging ◽  
Computed Tomography ◽  
Magnetic Resonance ◽  
The United States ◽  
Radiology Department ◽  
Magnetic Resonance Imaging Scan ◽  
Resonance Imaging ◽  
Academic Medical Centre ◽  
Academic Medical ◽  
The Impact

Background The COVID-19 pandemic led to a widespread socioeconomic shutdown, including medical facilities in many parts of the world. The purpose of this study was to assess the impact on neuroimaging utilisation at an academic medical centre in the United States caused by this shutdown. Methods Exam volumes from 1 February 2020 to 11 August 2020 were calculated based on patient location, including outpatient, inpatient and emergency, as well as modality type, including computed tomography and magnetic resonance imaging. 13 March 2020 was designated as the beginning of the shutdown period for the radiology department and 1 May 2020 was designated as the reopening date. The scan volumes during the pre-shutdown, shutdown and post-shutdown periods were compared using t-tests. Results Overall, neuroimaging scan volumes declined significantly by 41% during the shutdown period and returned to 98% of the pre-shutdown period levels after the shutdown, with an estimated 3231 missed scans. Outpatient scan volumes were more greatly affected than inpatient scan volumes, while emergency scan volumes declined the least during the shutdown. In addition, the magnetic resonance imaging scan volumes declined to a greater degree than the computed tomography scan volumes during the shutdown. Conclusion The shutdown from the COVID-19 pandemic had a substantial but transient impact on neuroimaging utilisation overall, with variable magnitude depending on patient location and modality type.

Household and Population-Level Behavioural Changes Due to COVID-19 Pandemic: A Smart Thermostat Based Comparative Data Analysis

2021 ◽  
Vol 10 (1) ◽  
pp. 1-6
Author(s):  
Kirti Sundar Sahu ◽  
Arlene Oetomo ◽  
Niloofar Jalali ◽  
Plinio P. Morita
Keyword(s):  
Data Analysis ◽  
Population Level ◽  
World Health ◽  
Motion Sensors ◽  
Household Level ◽  
Work From Home ◽  
Significant Difference ◽  
Health Organization ◽  
Smart Thermostat ◽  
The Impact

The World Health Organization declared the coronavirus outbreak as a pandemic on March 11, 2020. To inhibit the spread of COVID-19, governments around the globe, including Canada, have implemented physical distancing and lockdown measures, including a work-from-home policy. Canada in 2020 has developed a 24-Hour Movement Guideline for all ages laying guidance on the ideal amount of physical activity, sedentary behaviour, and sleep (PASS) for an individual in a day. The purpose of this study was to investigate changes on the household and population-level in lifestyle behaviours (PASS) and time spent indoors at the household level, following the implementation of physical distancing protocols and stay-at-home guidelines. For this study, we used 2019 and 2020 data from ecobee, a Canadian smart Wi-Fi thermostat company, through the Donate Your Data (DYD) program. Using motion sensors data, we quantified the amount of sleep by using the absence of movement, and similarly, increased sensor activation to show a longer duration of household occupancy. The key findings of this study were; during the COVID-19 pandemic, overall household-level activity increased significantly compared to pre-pandemic times, there was no significant difference between household-level behaviours between weekdays and weekends during the pandemic, average sleep duration has not changed, but the pattern of sleep behaviour significantly changed, specifically, bedtime and wake up time delayed, indoor time spent has been increased and outdoor time significantly reduced. Our data analysis shows the feasibility of using big data to monitor the impact of the COVID-19 pandemic on the household and population-level behaviours and patterns of change.

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