Industrial Consultancy & Sponsored Research (IC&SR) , IIT Madras
Accelerated Machine-learning-based System and Method for Predicting Appropriate Materials and Rapid Prototyping of Energy-storage Devices
Categories for this Invention
Technology: Predicting appropriate material for rapid prototyping devices
Category: Artificial intelligence-based machine learning systems and methods
Industry: Material Science
Application: Energy Storage/Rapid prototyping systems and applications
Market: The global market size was USD 700 million in 2019 and is poised to grow from USD 749 million in 2023 to USD 1131 million by 2031, growing at a CAGR of 7% in the forecast period (2024-2031).
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Problem Statement
- Globalization and customization are putting pressure on manufacturers to create prototypes for design changes, especially in energy-efficient products.
- The storage device industry needs flexible upgrades and rapid prototyping to meet evolving needs.
- The energy storage industry is utilizing advanced computing tools like CAD systems and artificial intelligence for rapid prototyping applications.
- However, rapid development is needed for materials discovery and implementation.
- A combination of data science, robotics, 3D printing, testing, and database management is crucial for successful development.
- An enhanced AI-based machine learning system is required to meet demands.
Technology
- An accelerated machine-learning-based system, comprises:
- A materials recommender module (110) configured with an AI
(Artificial Intelligence)-based dynamic materials database (120) - For putative optimum composition and identification of appropriate material recommendation with respect to an end user specification; and
- A rapid prototyping application (140) configured with a prototype developer tool (150)
- For receiving the appropriate material recommendation and rapid prototyping of energy-storage devices (160) based on end-user specifications
- wherein the accelerated machine learning-based system (100) predicts appropriate/discovering materials and rapid prototypes of energy-storage devices based on end-user specifications.
Key Features / Value Proposition
- For example supercapacitors are known for their rapid energy delivery (1-2 kW kg-1) and quick charging time (4-10s) based on the material used.
- Based on the material used as an electrode material, a supercapacitor exhibits
- electrical double-layer,
- pseudocapacitive,
- hybrid behavior.
- Hence, the selection of material is important to deliver a user required product.
AI-based support system comprises
- an accelerated material discovery setup
Rapid prototyping devices
- Based on end-user specifications.
- The database would be updated continuously with the results we achieve through the system literately.
The prototype developer tool
- Comprises a 3D printer or a robot system.
Questions about this Technology?
Contact For Licensing
sm-marketing@imail.iitm.ac.in
ipoffice2@iitm.ac.in
Research Lab
Prof. Tiju Thomas
Department of Metallurgical and Materials eng.
Intellectual Property
IITM IDF Ref. 2019
Patent No: IN 495621
Technology Readiness Level
TRL-2
Technology concept formulated