-
- News
- Books
Featured Books
- design007 Magazine
Latest Issues
Current IssuePartial HDI
Our expert contributors provide a complete, detailed view of partial HDI this month. Most experienced PCB designers can start using this approach right away, but you need to know these tips, tricks and techniques first.
Silicon to Systems: From Soup to Nuts
This month, we asked our expert contributors to weigh in on silicon to systems—what it means to PCB designers and design engineers, EDA companies, and the rest of the PCB supply chain... from soup to nuts.
Cost Drivers
In this month’s issue of Design007 Magazine, our expert contributors explain the impact of cost drivers on PCB designs and the need to consider a design budget. They discuss the myriad design cycle cost adders—hidden and not so hidden—and ways to add value.
- Articles
- Columns
Search Console
- Links
- Media kit
||| MENU - design007 Magazine
Machine Learning Can Predict the Mechanical Properties of Polymers
October 30, 2024 | ACN NewswireEstimated reading time: 2 minutes
Polymers such as polypropylene are fundamental materials in the modern world, found in everything from computers to cars. Because of their ubiquity, it’s vital that materials scientists know exactly how each newly developed polymer will perform under different preparation conditions. Thanks to a new study, which was published in Science and Technology of Advanced Materials, scientists can now use machine learning to determine what to expect from a new polymer.
Machine learning predicts the material properties of new polymers with high accuracy, providing a nondestructive alternative to conventional polymer testing methods. Machine learning predicts the material properties of new polymers with high accuracy, providing a nondestructive alternative to conventional polymer testing methods.
Predicting the mechanical properties of new polymers, such as their tensile strength or flexibility, usually involves putting them through destructive and costly physical tests. However, a team of researchers from Japan, led by Dr. Ryo Tamura, Dr. Kenji Nagata, and Dr. Takashi Nakanishi from the National Institute for Materials Science in Tsukuba, showed that machine learning can predict the material properties of polymers. They developed the method on a group of polymers called homo-polypropylenes, using X-ray diffraction patterns of the polymers under different preparation conditions to provide detailed information about their complex structure and features.
“Machine learning can be applied to data from existing materials to predict the properties of unknown materials,” Drs. Tamura, Nagata, and Nakanishi explain. “However, to achieve accurate predictions, it’s essential to use descriptors that correctly represent the features of these materials.”
Thermoplastic crystalline polymers, such as polypropylene, have a particularly complex structure that is further altered during the process of molding them into the shape of the end product. It was, therefore, important for the team to adequately capture the details of the polymers’ structure with X-ray diffraction and to ensure that the machine learning algorithm could identify the most important descriptors in that data.
The new method accurately captured the structural changes of commonly used plastic Polypropylene during the molding process into the end product. The new method accurately captured the structural changes of commonly used plastic Polypropylene during the molding process into the end product.
To that end, they analysed two datasets using a tool called Bayesian spectral deconvolution, which can extract patterns from complex data. The first dataset was X-ray diffraction data from 15 types of homo-polypropylenes subjected to a range of temperatures, and the second was data from four types of homo-polypropylenes that underwent injection molding. The mechanical properties analysed included stiffness, elasticity, the temperature at which the material starts to deform, and how much it would stretch before breaking.
The team found that the machine learning analysis accurately linked features in the X-ray diffraction imagery with specific material properties of the polymers. Some of the mechanical properties were easier to predict from the X-ray diffraction data, while others, such as the stretching break point, were more challenging.
“We believe our study, which describes the procedure used to provide a highly accurate machine learning prediction model using only the X-ray diffraction results of polymer materials, will offer a nondestructive alternative to conventional polymer testing methods,” the NIMS researchers say.
The team also suggested that their Bayesian spectral deconvolution approach could be applied to other data, such as X-ray photoelectron spectroscopy, and used to understand the properties of other materials, both inorganic and organic.
“It could become a test case for future data-driven approaches to polymer design and science,” the NIMS team says.
Suggested Items
Electra Polymers Ltd Expands Manufacturing Capacity, Invests in New Facilities and Talent
03/26/2024 | Electra Polymers LtdElectra Polymers Ltd, a leading provider of coatings for the electronics industry, proudly announces a significant expansion of its manufacturing capacity for inkjet materials. The company is making substantial investments in new facilities, talent acquisition, and cutting-edge laboratory equipment to meet the increasing demand for high-performance functional inkjet materials in the market.
Toyochem Constructs New Pilot Facility for High-performance Polymers in Japan
06/05/2023 | ToyochemToyochem Co., Ltd., the polymer and coatings arm of Japan’s Toyo Ink Group, announced today that it has completed construction of a new Polymer Pilot Facility at its Kawagoe Factory, a manufacturing complex located in Kawagoe City, Saitama Prefecture in Japan.
Henniker Plasma: Plasma Treatment of Fluoropolymers
02/14/2023 | Henniker PlasmaPTFE, and other fluorinated polymers, are chemically inert, thermally stable and highly hydrophobic, due to their intrinsically low surface energy.
With Fuzzy Nanoparticles, Researchers Reveal a Way to Design Tougher Ballistic Materials
12/23/2021 | NISTResearchers at the National Institute of Standards and Technology (NIST) and Columbia Engineering have discovered a new method to improve the toughness of materials that could lead to stronger versions of body armor, bulletproof glass and other ballistic equipment.
Shaun Tibbals and Electra Polymer: Finding the Silver Linings in COVID-19
07/09/2020 | Nolan Johnson, PCB007On July 8, Nolan Johnson spoke with Shaun Tibbals, sales and marketing director for Electra Polymers. Shaun discusses the Electra Polymer’s business outlook responses to the ongoing COVID-19 outbreak.