scholarly journals Automated analysis of platelet microstructures using a feature length orientation space

2022 ◽  
Author(s):  
A. Campbell ◽  
P. Murray ◽  
E. Yakushina ◽  
A. Borocco ◽  
P. Dokladal ◽  
...  
Keyword(s):  
Human Error ◽  
Volume Fraction ◽  
Microstructural Analysis ◽  
Automated Analysis ◽  
Automated Image Analysis ◽  
Wide Field ◽  
Orientation Space ◽  
Analysis Technique ◽  
New Feature ◽  
Manual Interaction

AbstractThe ability to measure elongated structures such as platelets and colonies, is an important step in the microstructural analysis of many materials. Widely used techniques and standards require extensive manual interaction making them slow, laborious, difficult to repeat and prone to human error. Automated approaches have been proposed but often fail when analysing complex microstructures. This paper addresses these challenges by proposing a new, automated image analysis technique, to reliably assess platelet microstructure. Tools from Mathematical Morphology are designed to probe the image and map the response onto a new feature-length orientation space (FLOS). This enables automated measurement of key microstructural features such as platelet width, orientation, globular volume fraction, and colony size. The method has a wide field of view, low dependency on input parameters, and does not require prior thresholding, common in other automated analysis techniques. Multiple datasets of complex Titanium alloys were used to evaluate the new techniques which are shown to match measurements from expert materials scientists using recognized standards, while drastically reducing measurement time and ensuring repeatability. The per-pixel measurement style of the technique also allows for the generation of useful colourmaps, that aid further analysis and provide evidence to increase user confidence in the quantitative measurements.

scholarly journals Effect of Graphene Nanosheets Content on Microstructure and Mechanical Properties of Titanium Matrix Composite Produced by Cold Pressing and Sintering

Nanomaterials ◽  
2018 ◽  
Vol 8 (12) ◽  
pp. 1024 ◽  
Author(s):  
Milad Haghighi ◽  
Mohammad Shaeri ◽  
Arman Sedghi ◽  
Faramarz Djavanroodi
Keyword(s):  
Mechanical Properties ◽  
Matrix Composite ◽  
Volume Fraction ◽  
Graphene Nanosheets ◽  
Microstructural Analysis ◽  
Titanium Matrix ◽  
Titanium Matrix Composite ◽  
Cold Pressing ◽  
Sintering Time ◽  
Microstructure And Mechanical Properties

The effect of graphene nanosheet (GNS) reinforcement on the microstructure and mechanical properties of the titanium matrix composite has been discussed. For this purpose, composites with various GNS contents were prepared by cold pressing and sintering at various time periods. Density calculation by Archimedes’ principle revealed that Ti/GNSs composites with reasonable high density (more than 99.5% of theoretical density) were produced after sintering for 5 h. Microstructural analysis by X-ray diffraction (XRD) and a field emission scanning electron microscope (FESEM) showed that TiC particles were formed in the matrix during the sintering process as a result of a titanium reaction with carbon. Higher GNS content as well as sintering time resulted in an increase in TiC particle size and volume fraction. Microhardness and shear punch tests demonstrated considerable improvement of the specimens’ mechanical properties with the increment of sintering time and GNS content up to 1 wt. %. The microhardness and shear strength of 1 wt. % GNS composites were enhanced from 316 HV and 610 MPa to 613 HV and 754 MPa, respectively, when composites sintered for 5 h. It is worth mentioning that the formation of the agglomerates of unreacted GNSs in 1.5 wt. % GNS composites resulted in a dramatic decrease in mechanical properties.

Automated Analysis of SEM X-Ray Spectral Images: A Powerful New Microanalysis Tool

2003 ◽  
Vol 9 (1) ◽  
pp. 1-17 ◽  
Author(s):  
Paul G. Kotula ◽  
Michael R. Keenan ◽  
Joseph R. Michael
Keyword(s):  
Image Data ◽  
Large Data ◽  
Automated Analysis ◽  
Phase Identification ◽  
Data Sets ◽  
Multivariate Statistical ◽  
X Ray ◽  
Analysis Technique ◽  
Statistical Analysis Technique ◽  
Multi Phase

Spectral imaging in the scanning electron microscope (SEM) equipped with an energy-dispersive X-ray (EDX) analyzer has the potential to be a powerful tool for chemical phase identification, but the large data sets have, in the past, proved too large to efficiently analyze. In the present work, we describe the application of a new automated, unbiased, multivariate statistical analysis technique to very large X-ray spectral image data sets. The method, based in part on principal components analysis, returns physically accurate (all positive) component spectra and images in a few minutes on a standard personal computer. The efficacy of the technique for microanalysis is illustrated by the analysis of complex multi-phase materials, particulates, a diffusion couple, and a single-pixel-detection problem.

Innovative Method for Efficient Real Time Online Well Monitoring to Enhance Crew Respond Time in Marginal Field

2021 ◽  
Author(s):  
Dias Anugrah Massewa ◽  
Muhammad Rifaat ◽  
Ferdyan Ihza Akbar ◽  
Rahmanda Fadri ◽  
Denny Mulia Akbar ◽  
...  
Keyword(s):  
Real Time ◽  
Human Error ◽  
Smart Phone ◽  
Wide Field ◽  
C Language ◽  
Measurement Tools ◽  
Production Crew ◽  
Marginal Field ◽  
The Impact ◽  
Smart Phone Application

Abstract Previously, well monitoring in Siak block relied on production crew scheduled tour that needed six hours to complete one cycle of all wells in Lindai field. This paper describes the utilization of digital technology to observe well parameters while sending notification if there is any anomaly regarding those parameters through smart phone application or website. Smart microcontroller was installed in wellhead panel and three sensors are mounted in desired point around wellhead to perform online Intelligent Well Monitoring (IWM) for well’s parameters. If abnormality occurs, real time notification would be sent to user’s smart phone application or website by using global mobile communication system (GSM) signal. The parameters monitored were pressure, temperature, and load because they are essential to be analyzed as initial diagnosis of well problem. Based on the readings, production team could quickly perform troubleshooting to prevent loss production opportunity (LPO). The programming of this smart microcontroller used C language as data compiler. This method was tested in one of the wells in Lindai field, which has the highest oil production. After three months of surveillance, in terms of data quality, the values shown by this tool had only five percent differences compared to manual survey using calibrated measurement tools. Additionally, the parameters could be monitored online, real time, and gave the notification directly to users should there be any issues. Moreover, this tool could reduce the response time of the field crew significantly from six hours following the conventional field tour to only in five minutes by relying on real time notification. In addition, the operational cost of this tool was 82% cheaper compared to other well-known online monitoring tool available in the market so it is considered economical. In the long term, this tool will be implemented on all wells in Siak block for integrated real time monitoring. Furthermore, the impact of field scale implementation will be much greater such as increasing data accuracy by eliminating human error from manual well checking and improving safety of the crew by reducing the possibility of fatigue. The utilization of smart microcontroller for online well monitoring is beneficial for marginal field with high number of wells and wide field coverage. Earlier, real time well monitoring is usually considered expensive investment that rarely become priority. However, the implementation of IoT (Internet of Things) by using this tool can be the game changer in marginal field and maximize the well’s production by reducing LPO.

Low C High Mn Cold Rolled TWIP Steel: Kinetics of Isothermal Recrystallization

2012 ◽  
Vol 706-709 ◽  
pp. 2181-2186 ◽  
Author(s):  
Tulio M.F. Melo ◽  
Érica Ribeiro ◽  
Lorena Dutra ◽  
Dagoberto Brandão Santos
Keyword(s):  
Automobile Industry ◽  
Twip Steel ◽  
Volume Fraction ◽  
Microstructural Analysis ◽  
High Strength ◽  
Specific Weight ◽  
Carbon Steels ◽  
Cold Rolled ◽  
Increasing Demand ◽  
Kinetics Of

The increasing demand, mainly from the automobile industry, for materials which combine high strength, high ductility and low specific weight makes steels with the TWIP (TWinning Induced Plasticity) effect a promising material to meet these requirements. This work aimed to study the kinetics of isothermal recrystallization of a TWIP steel (C-0.06%, Mn-25%, Al-3%, Si-2%, and Ni-1%) after cold rolling. The steel was hot and cold-rolled and then annealed at 700°C with soaking times ranging from 10 to 7200 s. Microstructural analysis was performed using light (LM) and scanning electron microscopy (SEM). Furthermore, quantitative metallography was performed in order to evaluate the recrystallized volume fraction and grain size. A JMAK based model was applied to describe the nucleation grain growth process. The restoration of the steel was also evaluated by microhardness tests. A complete recrystallization after 7200 s at 700°C was observed. It was found that with increasing annealing times, the recrystallized volume fraction also increases, while the nucleation and growth rates decrease, in agreement with the results for plain carbon steels.

Automatic Grain Size Determination in Microstructures Using Computer Vision Algorithm Based on Support Vector Machine (SVM)

2017 ◽  
Vol 1143 ◽  
pp. 194-199
Author(s):  
Florin Bogdan Marin ◽  
Florentina Potecaşu ◽  
Mihaela Marin ◽  
Petrică Alexandru
Keyword(s):  
Image Analysis ◽  
Human Error ◽  
Visual Inspection ◽  
Support Vector ◽  
Specific Knowledge ◽  
Image Analysis Technique ◽  
Analysis Technique ◽  
Image Analysis Algorithm ◽  
Important Time ◽  
Vision Algorithm

The properties of materials are identified as the results of its microstructures characteristics. Consequently the task of analysis of microstructure is very important in engineering. There are several methods such as the visual inspection and the semi-automatic inspection by image analysis. Visual inspection by an operator is subject to human error and can take important time. The semiautomatic method using specific algorithm and technique improves performance of the work, however still needs specific knowledge concerning image filters and image analysis technique. This research’s objectives are to present automatic image analysis algorithm to measure grains in microstructure images.

scholarly journals Flow and fracture of austenitic stainless steels at cryogenic temperatures

2021 ◽  
Author(s):  
Pilar Fernández-Pisón ◽  
Jose Rodriguez-Martinez ◽  
E. García-Tabares ◽  
I. Avilés-Santillana ◽  
S. Sgobba
Keyword(s):  
Fracture Toughness ◽  
Martensitic Transformation ◽  
Liquid Helium ◽  
Volume Fraction ◽  
Microstructural Analysis ◽  
Liquid Helium Temperature ◽  
Post Mortem ◽  
Helium Temperature ◽  
Aisi 304L ◽  
Section Sample

In this paper, we have characterized the microstructural evolution and the plastic flow and fracture behaviours of AISI 304L and AISI 316LN stainless steel grades at liquid nitrogen temperature (77 K) and at liquid helium temperature (4 K). Uninterrupted tensile experiments, where the sample is continuously deformed under quasi-static loading conditions until fracture, have been carried out with a Single-Section Sample to obtain the stress-strain characteristics of the two grades. Interrupted tensile experiments, in which the sample is unloaded before fracture, have been performed with a novel Double-Section Sample to later characterize the strain-induced martensitic transformation at different levels of deformation. The content of martensite has been determined post-mortem, using magnetic induction, electron backscatter diffraction and quantitative light optical micrography. The results obtained with the three methods show quantitative agreement, and reveal that the martensitic transformation in AISI 304L occurs faster and to a greater extent than in AISI 316LN both at 77 K and at 4 K. To the authors' knowledge, in this paper we provide the first experimental results for the evolution of the content of strain-induced martensite in AISI 304L and AISI 316LN samples tested at liquid helium temperature. In addition, the experimental data for the evolution of the martensite volume fraction with the strain have been used to identify the temperature-dependent parameters of the martensitic transformation kinetic models proposed by Olson and Cohen (1975) and Garion and Skoczen (2002). Moreover, Mode I fracture tests with fatigue-precracked Compact Samples have been carried out to determine the fracture properties of the two investigated materials using the "resistance curve procedure" (ASTM-E1820-20a, 2020). The crack-growth resistance curves have been obtained with four different methods here referred to as ASTM Compliance Method, W-N Compliance Method, Modified W-N Compliance Method and ASTM Normalization Method, which is an original methodological contribution of this paper. While the four approaches yield similar results for the fracture toughness, only the W-N Compliance Method and the Modified W-N Compliance Method, the latter being proposed in this paper, fulfil all the requirements of the standard ASTM-E1820-20a (2020) so that the calculated fracture toughness can be accepted as a material property. The comparison of results for both materials and testing temperatures shows that the AISI 316LN displays higher fracture toughness than the AISI 304L. Moreover, post-mortem microstructural analysis of the Compact Samples near the fracture surface has revealed that the content of martensite is greater in AISI 304L than in AISI 316LN. Furthermore, for AISI 304L more martensite is formed in the sample tested at 77 K because the plastic deformation near the crack is greater than at 4 K.

scholarly journals SynActJ: Easy-to-Use Automated Analysis of Synaptic Activity

2021 ◽  
Vol 3 ◽  
Author(s):  
Christopher Schmied ◽  
Tolga Soykan ◽  
Svenja Bolz ◽  
Volker Haucke ◽  
Martin Lehmann
Keyword(s):  
Image Analysis ◽  
Organic Dyes ◽  
Calcium Influx ◽  
Glutamate Release ◽  
Automated Analysis ◽  
Time Lapse ◽  
Synaptic Activity ◽  
Automated Image Analysis ◽  
Batch Mode ◽  
Fluorescent Intensity

Neuronal synapses are highly dynamic communication hubs that mediate chemical neurotransmission via the exocytic fusion and subsequent endocytic recycling of neurotransmitter-containing synaptic vesicles (SVs). Functional imaging tools allow for the direct visualization of synaptic activity by detecting action potentials, pre- or postsynaptic calcium influx, SV exo- and endocytosis, and glutamate release. Fluorescent organic dyes or synapse-targeted genetic molecular reporters, such as calcium, voltage or neurotransmitter sensors and synapto-pHluorins reveal synaptic activity by undergoing rapid changes in their fluorescence intensity upon neuronal activity on timescales of milliseconds to seconds, which typically are recorded by fast and sensitive widefield live cell microscopy. The analysis of the resulting time-lapse movies in the past has been performed by either manually picking individual structures, custom scripts that have not been made widely available to the scientific community, or advanced software toolboxes that are complicated to use. For the precise, unbiased and reproducible measurement of synaptic activity, it is key that the research community has access to bio-image analysis tools that are easy-to-apply and allow the automated detection of fluorescent intensity changes in active synapses. Here we present SynActJ (Synaptic Activity in ImageJ), an easy-to-use fully open-source workflow that enables automated image and data analysis of synaptic activity. The workflow consists of a Fiji plugin performing the automated image analysis of active synapses in time-lapse movies via an interactive seeded watershed segmentation that can be easily adjusted and applied to a dataset in batch mode. The extracted intensity traces of each synaptic bouton are automatically processed, analyzed, and plotted using an R Shiny workflow. We validate the workflow on time-lapse images of stimulated synapses expressing the SV exo-/endocytosis reporter Synaptophysin-pHluorin or a synapse-targeted calcium sensor, Synaptophysin-RGECO. We compare the automatic workflow to manual analysis and compute calcium-influx and SV exo-/endocytosis kinetics and other parameters for synaptic vesicle recycling under different conditions. We predict SynActJ to become an important tool for the analysis of synaptic activity and synapse properties.

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