-
- News
- Books
Featured Books
- smt007 Magazine
Latest Issues
Current IssueCertifications
Certifications have historically been seen as a cost of doing business, but how do we turn them into a positive ROI and a value to both customer and vendor?
The Butterfly Effect
The basis of chaos theory is a key concept known as the “butterfly effect.” It’s the idea that a small event in one place creates a cascading set of events elsewhere. So, how is the EMS landscape changing? We’re tracking the concerns and dynamics of this landscape, and there’s a lot to learn.
Coming to Terms With AI
In this issue, we examine the profound effect artificial intelligence and machine learning are having on manufacturing and business processes. We follow technology, innovation, and money as automation becomes the new key indicator of growth in our industry.
- Articles
- Columns
Search Console
- Links
- Events
||| MENU - smt007 Magazine
Machine Learning Techniques Improve X-ray Materials Analysis
November 21, 2023 | ACN NewswireEstimated reading time: 2 minutes
![](https://iconnect007.com/application/files/8317/0055/5919/SPring-8.jpg)
Researchers of RIKEN at Japan’s state-of-the-art synchrotron radiation facility, SPring-8, and their collaborators, have developed a faster and simpler way to carry out segmentation analysis, a vital process in materials science. The new method was published in the journal Science and Technology of Advanced Materials: Methods.
Segmentation analysis is used to understand the fine-scale composition of a material. It identifies distinct regions (or ‘segments’) with specific compositions, structural characteristics, or properties. This helps evaluate the suitability of a material for specific functions, as well as its possible limitations. It can also be used for quality control in material fabrication and for identifying points of weakness when analyzing materials that have failed.
Segmentation analysis is very important for synchrotron radiation X-ray computed tomography (SR-CT), which is similar to conventional medical CT scanning but uses intense focused X-rays produced by electrons circulating in a storage ring at nearly the speed of light. The team have demonstrated that machine learning is capable in conducting the segmentation analysis for the refraction contrast CT, which is especially useful for visualizing the three-dimensional structure in samples with small density differences between regions of interest, such as epoxy resins.
“Until now, no general segmentation analysis method for synchrotron radiation refraction contrast CT has been reported,” says first author Satoru Hamamoto. “Researchers have generally had to do segmentation analysis by trial and error, which has made it difficult for those who are not experts.”
The team’s solution was to use machine learning methods established in biomedical fields in combination with a transfer learning technique to finely adjust to the segmentation analysis of SR-CTs. Building on the existing machine learning model greatly reduced the amount of training data needed to get results.
“We’ve demonstrated that fast and accurate segmentation analysis is possible using machine learning methods, at a reasonable computational cost, and in a way that should allow non-experts to achieve levels of accuracy similar to experts,” says Takaki Hatsui, who led the research group.
The researchers carried out a proof-of-concept analysis in which they successfully detected regions created by water within an epoxy resin. Their success suggests that the technique will be useful for analyzing a wide range of materials.
To make this analysis method available as widely and quickly as possible, the team plans to establish segmentation analysis as a service offered to external researchers by the SPring-8 data center, which has recently started its operation.
Suggested Items
U.S. Army Selects RTX's RCADE Defense Analysis Solution to Enable Future Concept Experimentation
07/11/2024 | RTXRaytheon, an RTX business, has been awarded a contract from the U.S. Army Futures Command (AFC) Futures and Concepts Center (FCC) to conduct theater level concept experimentation and mission analysis to support agile learning of the future battlefield.
Altair Releases Altair HyperWorks 2024
07/10/2024 | AltairAltair, a global leader in computational intelligence, announced the release of Altair® HyperWorks 2024, the market’s leading platform for design and simulation.
Siemens Introduces Innovator3D IC - a Comprehensive Multiphysics Cockpit for 3D IC Design, Verification and Manufacturing
06/24/2024 | SiemensSiemens Digital Industries Software announced today Innovator3D IC, new software that delivers a fast, predictable path for the planning and heterogeneous integration of ASICs and chiplets using the latest and most advanced semiconductor packaging 2.5D & 3D technologies and substrates in the world.
NEC, Ursa Space Collaborate on Satellite Image Analysis Services Using One of the World's Largest Virtual Constellations
06/21/2024 | JCN NewswireNEC Corporation and Ursa Space Systems Inc. (Ursa Space) have agreed to collaborate on satellite image data analysis services. By combining services and technologies, the two companies will provide solutions for various applications for enterprises.
NEOTech Acquires Advanced 3D Computed Tomography Equipment for Enhanced Process Validation and Failure Analysis
06/20/2024 | NEOTechNEOTech, a leading provider of electronic manufacturing services (EMS), design engineering, and supply chain solutions in the high-tech industrial, medical device, and aerospace/defense markets, is excited to announce a significant investment in and acquisition of state-of-the-art three-dimensional (3D) Computed Tomography (CT) scan equipment for its engineering laboratory.