"If you wish to make an apple pie from scratch, you must first invent the universe."
(Scroll to the bottom for the answer.)
I admire this attitude, but it's a bit overwhelming. Maybe it is okay to buy the ingredients and make a pie, and worry about where the ingredients came from later.
The final episode covers manufacturing and QC. Angela will discuss how X-ray CT techniques can reveal the internal structure of solid-form tablets and capsules, drug delivery devices, and other pharmaceutical products.
We will also discuss how X-ray CT provides quantitative information to guide manufacturing processes and proves to be a valuable tool for evaluating product quality.
AM-made parts can have intricate internal structures both by design and as defects. CT offers analyses of characteristics that other techniques cannot analyze.
Porosity and internal defects: CT can quantify porosity in 3D, identifying pore size, shape, distribution, and unmelted particles, including distance to surfaces. It detects internal inclusions, both metallic and nonmetallic, missed by surface or 2D methods.
Internal dimensions: It’s the only method to measure internal dimensions like wall thickness, lattice struts, and internal channels. It also supports CAD-based nominal versus actual comparison.
Internal surface topography: It measures internal surface flatness and roughness in areas that are hard or impossible to access.
Monitoring changes over time: 4D CT tracks changes after loading or heat treatment, showing deformation, pore closure, or regrowth.
Simulations: CT scans provide real-geometry data for simulations, linking specific defects to mechanical performance.
Internal powder particle characterization: It analyzes powder particles in 3D, revealing internal porosity, sphericity, and surface area.
Materials interface characterization: It visualizes interfaces and material distributions in multi-material parts, including unmelted inclusions. In short, micro-CT reveals internal defects and structures that other methods can't, making it essential for evaluating AM parts.
What is your go-to segmentation method? With the advancement of machine learning based segmentation, it is tempting to use it for all CT data analysis. But traditional methods have benefits, and choosing the right one, depending on the data and analysis purpose, is important.
Here is a quick comparison of commonly used segmentation methods.
Traditional thresholding, such as Otsu binarization
Best for: Distinct material phases with high contrast Pros: Fast and traceable
Cons: Struggles with noisy, low contrast, or complex datasets
Machine learning methods
Best for: Noisy or low contrast but relatively simple datasets
Pros: Minimum training required
Cons: Slower, less traceable, some training skills required
Deep learning
Best for: Noisy, low contrast, or complex datasets with multiple phases at the same gray scale
Pros: Clean segmentation even on complex or low-quality datasets
Cons: Even slower, less traceable than machine learning, large training data set required, some training skills required
Real Scientists, Not Actors
A collection of priceless and embarrassing moments curated by Carlos Astudillo.
Carl Edward Sagan
American astronomer, planetary scientist, cosmologist, astrophysicist, astrobiologist, author, and science communicator (9 November 1934 – 20 December 1996)
"If you wish to make an apple pie from scratch, you must first invent the universe."
That's a wrap. Please let us know how we can help you learn more about X-ray CT. We love to hear from you!
