Machine Learning Speeds Up Search for New Sustainable Materials
May 26, 2022 | ACN NewswireEstimated reading time: 2 minutes
Researchers from Konica Minolta and the Nara Institute of Science and Technology in Japan have developed a machine learning method to identify sustainable alternatives for composite materials. Their findings were published in the journal Science and Technology of Advanced Materials: Methods.
Researchers are looking for sustainable options, such as recyclable materials or biomass, to substitute the constituent materials in composites which are used in various applications including electrical and information technologies.
Composite materials are compounds made of two or more constituent materials. Due to the complex nature of the interactions between the different components, their performance can greatly exceed that of single materials. Composite materials, such as fibre-reinforced plastics, are very important for a wide range of industries and applications, including electrical and information technologies.
In recent years, there has been increasing demand for more environmentally sustainable materials that help reduce industrial waste and plastic use. One way to achieve this is to substitute the constituent materials in composites with recyclable materials or biomass. However, this can reduce performance compared to the original material, not only due to the features of the individual constituent materials, such as their physicochemical properties, but also due to the interactions between the constituents.
"Finding a new composite material that achieves the same performance as the original using human experience and intuition alone takes a very long time because you have to evaluate countless materials while also taking into account the interactions between them," explains Michihiro Okuyama, assistant manager at Konica Minolta, Inc.
Machine learning offers a potential solution to this problem. Scientists have proposed several machine learning methods to conduct rapid searches among a large number of materials, based on the relationship between the materials' features and performance. However, in many cases the properties of the constituent materials are unknown, making these types of predictive searches difficult.
To overcome this limitation, the researchers developed a new type of machine learning method for finding alternative materials. A key advantage of the new method is that it can quantitatively evaluate the interactions among the component materials to reveal how much they contribute to the overall performance of the composite. The method then searches for replacement constituents with similar performance to the original material.
The researchers tested their method by searching for alternative constituent materials for a composite consisting of three materials - resin, a filler and an additive. They experimentally evaluated the performance of the substitute materials identified by machine learning and found that they were similar to the original material, proving that the model works.
"In developing alternatives, that make up composite materials, our new machine learning method removes the need to test large numbers of candidates by trial and error, saving both time and money." says Okuyama.
The method could be used to quickly and efficiently identify sustainable substitutes for composite materials, reducing plastic use and encouraging the use of biomass or renewable materials.
Suggested Items
Groundbreaking Ceremony Marks the Beginning of a New Era for Newccess Industrial; The Construction of the MINGXIN Building
04/12/2024 | Newccess IndustrialOn a clear and sunny day in March, the groundbreaking ceremony for the MINGXIN Building took place in Shenzhen, China. This moment marked the official commencement of construction for a project that will reshape the semiconductor materials industry.
The Need for a Holistic Global Sustainability Standard
04/10/2024 | Michael Ford, Aegis SoftwareNo one can deny that the resources of our fragile planet are finite. The environment seems like a third party, subject to constant degradation. We’re acutely aware of the effects of pollution on our climate, and despite our “throw-away” culture, recycling and recovery of materials has remained relatively expensive, even as we use more energy just to survive.
iNEMI Publishes Four Roadmap Topics
04/04/2024 | iNEMIThe International Electronics Manufacturing Initiative (iNEMI) announces the availability of the first roadmap topics in the new iNEMI Roadmap format. Printed circuit boards, sustainable electronics, smart manufacturing, and mmWave materials and test are now available online.
Insulectro’s 'Storekeepers' Extend Their Welcome to Technology Village at IPC APEX EXPO
04/03/2024 | InsulectroInsulectro, the largest distributor of materials for use in the manufacture of PCBs and printed electronics, welcomes attendees to its TECHNOLOGY VILLAGE during this year’s IPC APEX EXPO at the Anaheim Convention Center, April 9-11, 2024.
Checking In With ICAPE Group
04/03/2024 | Nolan Johnson, I-Connect007ICAPE Group’s field application engineer Erik Pederson drills down on sustainability, supply chain resiliency, and what value engineering really looks like in this exclusive interview. Founded in 1999, European-based ICAPE Group provides 21 million printed circuit boards and over six million technical parts to manufacturers every month. With 30 PCB manufacturing partners globally and 50 partners providing a wide array of technical parts, ICAPE Group has operations in China, Taiwan, Thailand, South Korea, Vietnam, South Africa, Europe, Mexico, and the United States. The company also focuses on the value proposition for its customers.