In the realm of advanced manufacturing, researchers Rigoberto Advincula and Jihua Chen from the University of Tennessee, Knoxville, are exploring innovative ways to optimize the use of renewable and bio-based materials. Their work, published in the journal Advanced Manufacturing, focuses on integrating artificial intelligence and machine learning (AI/ML) workflows to enhance process optimization and reduce waste, energy consumption, and emissions.
Advincula and Chen’s research highlights the potential of additive manufacturing (AM), commonly known as 3D printing, in creating durable and sustainable materials. By leveraging AI/ML-derived models, they aim to accelerate the synthesis and adaptation of new bio-derived materials, promoting a more circular economy. The use of self-driving laboratories (SDL) and AI/ML workflows can optimize the structure, composition, processing, and properties (SCPP) of these materials, leading to improved manufacturing processes.
The researchers emphasize the role of various AI/ML techniques, such as deep learning (DL), reinforcement learning (RL), and deep reinforcement learning (DRL) with deep neural networks (DNNs), in driving advancements in AM and SDL. These technologies can enhance optimization protocols continuously, making the manufacturing process more efficient and environmentally friendly. By incorporating supervised or unsupervised learning, the researchers aim to improve the overall performance and sustainability of advanced manufacturing with bio-based materials.
The practical applications of this research for the energy sector are significant. Optimizing the use of renewable and bio-based materials can lead to more sustainable energy solutions, reducing the environmental impact of energy production and consumption. Additionally, the integration of AI/ML workflows can enhance the efficiency and profitability of energy-related manufacturing processes, making them more competitive and sustainable in the long run.
In summary, Advincula and Chen’s research offers a promising approach to advancing manufacturing with renewable and bio-based materials. By leveraging AI/ML technologies, they aim to optimize processes, reduce waste, and promote a more circular economy. These innovations have the potential to transform the energy sector, making it more sustainable and efficient.
This article is based on research available at arXiv.