The session includes real examples, from tiny defects to full assemblies, showing how multiscale data can improve the way materials are evaluated, verified, and refined.

You will learn how to deal with unique challenges in life science sample preparation and how to conduct quantitative analyses.

In this episode, we will discuss how X-ray computed tomography (CT) can non-destructively access these internal structures, voids, cracks, as well as the external shape. Dimensional inspection including actual to nominal comparison, section thickness,  mechanical load distribution of final product, as well as metal powder morphology are characterized for both qualitative visual inspection and quantitative analysis. Analysis examples from various AM feedstock, such as polymer, metal, and ceramic will be discussed. 

In this webinar, we will discuss important factors to consider when using X-ray CT methods to inspect batteries.

In this episode, we will define what a functional failure is and review how you can use X-ray CT to characterize various defects that can cause diminished or loss of function.

In this episode, we will define what structural failure is and discuss how it manifests in materials and designed parts in the form of cracks, pores, etc.

In this episode, we will start with an introduction to Failure Analysis, an investigational method to identify and understand the root causes of failure for materials and products.

We invited Dr. Awen Autret, R&D Engineer from NOVITOM, and asked him questions to learn how you can simulate X-ray images and where this technology can be useful.

We invited Roger Wende, Senior Business Development Manager from Hexagon, and asked him questions to learn how exactly the object surface was identified in CT data, how to represent the results as mesh data, and what type of advanced analyses and simulations we could run using them.

We invited Prof. Chandra Reedy from the University of Delaware and asked her questions about how she chose what to quantify using the CT images to extract meaningful and trustworthy information about ancient ceramics.

We invited Dr. Philipp Eichheimer, the Application Engineer specialized in filtration from Math2Market, and asked him questions to learn how using CT can make the research and development process of filter materials more efficient by running simulations to identify effective filter characteristics.

We invited Dr. Arne Jacob, the Application Engineer specialized in DRP from Math2Market, and asked him questions to learn how you can use DRP and apply the results to decision-making processes involved in oil and gas reservoir development and production.

We invited Joshua Lomeo, the Director of Application Science from DigiM Solution, and asked him questions to learn how you can advance  pharmaceutical product development with CT analyses.

We invited Dr. Mike Marsh, the Dragonfly Product Manager from Object Research Systems, and asked him questions to learn how to use deep learning the right way and get the most out of it.

Rigaku and Micromeritics will together show how X-ray and physical textural analysis technologies can provide reliable determinations of density, elemental purity, surface area, porosity, and crystal structure that are essential to a high-quality anode or cathode.  

When the CT image of filter material has sufficient resolution and is segmented well, we can simulate flow through filtration materials to determine filter efficiency and to analyze filter clogging and lifetime. In this episode, we will investigate how we can run these simulations.

In this joint webinar, Retsch and Rigaku present how to optimally prepare the sample for analysis and how to analyze the physical and chemical composition of battery materials and the complete battery.

In this episode, we will review image segmentation. Then, we will calculate some basic properties of filter samples and prepare data for simulations.

In this episode, we will go into the lab to investigate how to choose the best data collection geometry and data collection parameters. Then, we will review how to collect high-quality CT images of filter media that can be used for digital filtration analysis.

When the CT image of a rock sample has sufficient resolution and is segmented well, we can simulate the flow characteristics of the rock, obtain permeabilities, and visualize the percolation path, flow field velocity, etc. In this episode, we will look into how we can run these simulations.

In this episode, we will review image segmentation and calculate the basic properties of rock samples.

In this episode, we will go into the lab and compare different data collection settings. Then, we will review how to collect high-quality CT images of a drill core that can be used for digital rock analysis.

In this workshop, we will use Dragonfly software by Object Research Systems and review more advanced image processing techniques and the benefits and convenience of using commercial software specifically designed for CT image analysis.

We will use ImageJ to generate Radon transforms, sinograms, and reconstructed images.

In this workshop, we will review morphological operations and see how they can be applied to refine segmentation results.

In this workshop, we will review how these image processing techniques work and how to choose the right one.

In this webinar, we discuss sample preparation techniques for life science samples.

You will learn how to use Fiji/ImageJ, how to analyze CT data, how to use macros, and how to visualize 4D CT data. You can download the sample images and try data analysis with us.

You will learn the keys to successful 4D and in-situ X-ray CT measurements and how to plan experiments.

You will learn how to analyze CT data, how to use deep learning image segmentation, and how to separate segmented objects for further quantitative analysis.

You will learn how high-resolution CT scans are done, how X‑ray energy and sample movement relate to the scanning conditions, and how each scan condition affects the data quality.

You will learn how CT scans are done, how to optimize CT scan conditions for organic materials, and how each scan condition affects the contrast of low-density samples.

You will learn how to use Fiji/ImageJ and how to analyze CT data. You can download the sample images and try data analysis with us.

Basics of X-ray CT and a number of application examples will be presented.

You will learn how to analyze CT data, how to choose the best segmentation method, and how to apply quantitative analyses such as volume fraction and size distribution calculation.

You will learn how to prepare samples, how to obtain high-resolution CT scans, how to optimize scan conditions, and how each scan condition affects the data quality.

We review different types of denoising filters, their side effects on the sharpness and resolution of the image, and how these filters help improve the segmentation process.

How to use machine learning (Weka) to segment challenging images with noise and uneven gray levels.

We explore the concept of resolution and discusses the optimal sampling size, the effect of contrast on resolution, and the consequences of insufficient resolution.

We explain how the filtered back projection reconstruction works by generating the Radon transform of an image and reconstructing the original image.

We review how we can analyze CT scans quantitatively using the image segmentation results and how we need to treat 2D and 3D images differently.

We review how to calculate file sizes of CT images and how to reduce them when necessary to speed up the image analysis process.

Where to go to download open source FIJI/ImageJ and reviews how to open files, adjust contrast, adjust scale, take measurements, and display a 3D rendering of a CT scan

You will learn how CT scans are done, starting from mounting the sample to obtaining the reconstructed volume data, how to optimize scan conditions, and how each scan condition affects the data quality.

Basics of metrology analysis and a number of X-ray CT application examples are discussed.