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Research on computer neural network-aided design of insulating glass formulation and coating properties
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Haoran CAI1, 3, 4, Caiming CHEN2, Shengfeng LONG1, 3, 4, Jing DING1, 3, 4, Guisheng ZHU1, 3, 4, Huarui XU1, 3, 4, Wanqing XIE1
Insulating Materials | 2025, 58(7) : 79 - 85
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Insulating Materials | 2025, 58(7): 79-85
• Material Research •
Research on computer neural network-aided design of insulating glass formulation and coating properties
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Haoran CAI1, 3, 4, Caiming CHEN2, Shengfeng LONG1, 3, 4, Jing DING1, 3, 4, Guisheng ZHU1, 3, 4, Huarui XU1, 3, 4, Wanqing XIE1
Affiliations
  • 1. School of Materials Science and Engineering, Guilin University of Electronic Technology, Guilin 541004, China
  • 2. School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China
  • 3. Electrical Information Materials and Devices Engineering Research Center of Education, Guilin 541004, China
  • 4. Guangxi Key Laboratory of Information Materials, Guilin 541004, China
Published: 2025-07-20 doi: 10.16790/j.cnki.1009-9239.im.2025.07.009
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Thick-film heating has become a key thermal-management solution for new-energy vehicles. To meet the relevant application demands, it is necessary to develop dielectric slurries for aluminum-based thick-film heating elements. This study utilized the built-in machine learning model of the Inorganic Glass Engineer System for property prediction to assist in the development of dielectric insulating glass formulations for aluminum-based thick-film heating elements, and conducted experimental verification. The results show that the insulating glass prepared by the optimal formula can be sintered at 580℃, with a thermal expansion coefficient of 18.8×10-⁶℃-1. When the dielectric-layer thickness exceeds 110 μm, it has a breakdown voltage over 1.29 kV and a leakage current less than 0.21 mA, which can meet the usage requirements of the medium layer of aluminum-based thick-film heating elements.

dielectric slurry  /  insulating glass  /  aluminum-based thick-film heating element  /  machine-learning
Haoran CAI, Caiming CHEN, Shengfeng LONG, Jing DING, Guisheng ZHU, Huarui XU, Wanqing XIE. Research on computer neural network-aided design of insulating glass formulation and coating properties[J]. Insulating Materials, 2025 , 58 (7) : 79 -85 . DOI: 10.16790/j.cnki.1009-9239.im.2025.07.009
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Article Info
doi: 10.16790/j.cnki.1009-9239.im.2025.07.009
  • Receive Date：2025-02-21
  • Online Date：2025-10-29
  • Published：2025-07-20
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History
  • Received：2025-02-21
  • Revised：2025-03-10
Funding
Affiliations
    1. School of Materials Science and Engineering, Guilin University of Electronic Technology, Guilin 541004, China
    2. School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China
    3. Electrical Information Materials and Devices Engineering Research Center of Education, Guilin 541004, China
    4. Guangxi Key Laboratory of Information Materials, Guilin 541004, China
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https://castjournals.cast.org.cn/joweb/jycl/EN/10.16790/j.cnki.1009-9239.im.2025.07.009
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小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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