■ Technological Core
Our research lab is dedicated to developing AI-related technologies for use in materials science and industry. Through the use of the AI platform MACSiMUM (https://www.macsimum.org/) and other digital
tools, we offer AI tools and talent development for industry use in materials design simulation, process data, and product testing verification, both online and offline. Our services include platform, research projects, App (models, material design databases), and personnel training courses. We aim to lower the barriers for material manufacturers to enter the realm of intelligent IT technology, and combine material technologies with the verification data of materials/processes/components to introduce the data-driven R&D models and thinking into domestic material manufacturers. We provide integrated solutions to show our technical expertise to customers in a more intuitive manner and offer assistance as needed.
■ Features
• We offer easy-to-use machine learning tools that allow operators to quickly apply them to real-world scenarios without the need to write programming code
• We digitize traditional process parameters management and convert them into high-value information applications to maximize development efficiency
• By combining artificial intelligence, IoT, and other hardware and software technologies with specialized knowledge in materials science, we provide integrated solutions that achieve high value and customization. We assist businesses in their digital transformation efforts to enhance their competitiveness
■ Applications
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Data Collection and Prediction - Solar Panel Power Generation
To verify the performance of a newly developed easy-detachable solar panel, a 5KW solar power generation module and IoT system were established to collect complete one-year power generation data and environmental climate data. At the same time, the monitoring data was uploaded to the cloud for machine learning modeling to predict future PV power generation.
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Material Electronic Database - Access to Expertise in the Material Field
The MACSiMUM platform has now established 17 different material databases, providing access to professional research data in the material field for reference in related areas. It is also possible to establish dedicated electronic databases for specific industries or fields to enhance data management efficiency and facilitate subsequent AI data analysis.
(Materials include: epoxy resin, glass processing, concrete, powder materials, thermal insulation composite materials, high-hardness corrosion-resistant heterogeneous materials, HPSI, 5G materials, ZTA materials...)
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Establish AI models for data analysis and prediction, and visualize the results
Record experimental data from researchers and manage it by establishing a database. Then, use machine learning to build models to assist with property simulation and quickly design new materials and optimize formula parameters, accelerating the development of new materials.
Data analysis and prediction results are presented through visual charts, allowing users to intuitively obtain analysis results and make decisions based on predictive recommendations.
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Using AI for energy-saving and carbon reduction equipment deterioration warning
This can be applied to monitor the status of equipment in manufacturing industry, import machine monitoring data, identify abnormalities through AI, and provide timely warnings to avoid safety and productivity issues caused by equipment abnormalities, ensuring normal operation of production lines and maximizing efficiency.
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Combining AIoT and Carbon Emission Coefficient Database for Carbon Inventory and Reduction
Tailored carbon inventory implementation plans can be developed for different scenarios, which calculate the carbon footprint of products or the carbon emissions of companies/factories. The actual carbon reduction could be evaluated and assessed with the aid of IOT systems and AI-supported devices to conserve energy and develop low-carbon products.
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Industrial Technology Research Institute (ITRI)
Material and Chemical Research Laboratories (MCL)
Dept. of Artificial Intelligence for Materials R&D Laboratory (X200)