TL;DR:
- Researchers at MIT have developed a method for determining the inner workings of materials by analyzing their surface properties using deep learning.
- The resulting predictive system can be applied to various engineering disciplines, including fluid dynamics, and can determine various properties such as stress, strain, fluid fields, and magnetic fields.
- The method was developed through an iterative process of making preliminary predictions, comparing with actual data, and fine-tuning the model.
- The training data included imagery and various surface property measurements, including simulated data based on an understanding of the material’s structure.
- The system can be applied in laboratory settings, such as in testing materials used in soft robotics.
- The method is expected to be used as a cost-effective way of collecting data and making predictions, guiding decisions about where to use more expensive equipment for further testing.
- The research was supported by the U.S. Army Research Office, the Air Force Office of Scientific Research, the Google Cloud platform, and the MIT Quest for Intelligence.
Main AI News:
Unveiling the Inner Mechanics of Materials Researchers at MIT
Have paved the way for engineers to determine the inner workings of various materials, from airplane parts to medical implants, simply by examining the properties of the material’s surface. Utilizing deep learning, the team used a large set of simulated data regarding a material’s exterior force fields and corresponding internal structure to develop a predictive system for the material’s interior.
In the paper published in the journal Advanced Materials, doctoral student Zhenze Yang and civil and environmental engineering professor Markus Buehler outline their findings. “It’s a common issue in engineering,” Buehler says. “To understand what’s inside a material, engineers might measure surface strains through image analysis, but the only way to truly inspect the interior is by cutting it.”
X-rays and other techniques exist, but they can be costly and require cumbersome equipment. The team aimed to create a non-invasive AI algorithm that could analyze surface observations, such as through a microscope or surface measurement, and deduce the interior details, including any damage, cracks, or stresses in the material or its internal microstructure. The system could also apply to biological tissues, determining if there is disease or changes in growth.
To address the challenge of multiple solutions for a single surface scenario, the team developed a technique that provides all possibilities. They trained an AI model using a vast amount of data regarding surface measurements and their corresponding interior properties, including uniform and composite materials, as well as materials made up of multiple components with differing properties.
The method even works for materials with complexities not yet fully understood. Buehler notes, “Even with complex biological tissue, we can measure its behavior and train the model with enough data collected, even if we don’t have a full understanding of its behavior.”
The Universal Application of Deep Learning
MIT researchers have found a way to determine the inner workings of various materials by analyzing the properties of the material’s surface, thanks to the use of deep learning. The resulting predictive system, which is being made freely available for use through GitHub, can be applied to various engineering disciplines, including fluid dynamics, and can determine a variety of properties such as stress, strain, fluid fields, and magnetic fields.
Doctoral student Zhenze Yang came up with the idea while studying data on a material and wondering how to fill in the missing information in a blurred area, an issue known as the inverse problem. The method was developed through an iterative process of making preliminary predictions, comparing with actual data, and fine-tuning the model.
The training data included imagery and various surface property measurements, including simulated data based on an understanding of the material’s structure. The system can be applied in laboratory settings, such as in testing materials used in soft robotics, where it’s difficult to determine what’s happening inside the material.
Buehler expects the method to be used as a cost-effective way of collecting data and making predictions, guiding decisions about where to use more expensive equipment for further testing. This research was supported by the U.S. Army Research Office, the Air Force Office of Scientific Research, the Google Cloud platform, and the MIT Quest for Intelligence.
Conlcusion:
The development of this deep learning approach for material analysis holds significant potential for the engineering and technology market. The ability to determine the inner workings of materials through surface analysis, without the need for invasive methods, offers a cost-effective solution for data collection and prediction.
This could have wide-ranging applications, from laboratory testing of materials used in soft robotics to guide decisions about where to use more expensive equipment for further testing in the aerospace and medical industries. As the method becomes more widely adopted and developed, it is likely to have a significant impact on the way engineers and analysts approach material analysis and evaluation.
