| Author Name | Affiliation | | Song Pang | State Key Laboratory of Organic-Inorganic Composites, Beijing University of Chemical Technology, Beijing 100029, China
Key Laboratory of Beijing City on Preparation and Processing of Novel Polymer Materials, Beijing University of Chemical Technology, Beijing 100029, China | | Yang Yu | State Key Laboratory of Organic-Inorganic Composites, Beijing University of Chemical Technology, Beijing 100029, China
Key Laboratory of Beijing City on Preparation and Processing of Novel Polymer Materials, Beijing University of Chemical Technology, Beijing 100029, China | | Huanhuan Liu | State Key Laboratory of Organic-Inorganic Composites, Beijing University of Chemical Technology, Beijing 100029, China
Key Laboratory of Beijing City on Preparation and Processing of Novel Polymer Materials, Beijing University of Chemical Technology, Beijing 100029, China | | Youping Wu | State Key Laboratory of Organic-Inorganic Composites, Beijing University of Chemical Technology, Beijing 100029, China
Beijing Engineering Research Center of Advanced Elastomers, Beijing University of Chemical Technology, Beijing 100029, China |
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| Abstract: |
| Multi-performance optimization of tread rubber composites is a key issue of great concern in automotive industry. Traditional experimental design approach via “trial and error” or intuition is ineffective due to mutual inhibition among multiple properties. A “Uniform design-Machine learning” strategy for performance prediction and multi-performance optimization of tread rubber composites was proposed. The wear resistance, rolling resistance, tensile strength and wet skid resistance were simultaneously optimized. A series of feasible optimization designs were screened via statistical analysis and machine learning analysis, and were experimentally prepared. The verification experiments demonstrate that the optimization design via machine learning analysis meets the optimization requirements of all target performance, especially for Akron abrasion and 60 ℃ tan δ(about 21% and 9% lower than the design targets, respectively) due to the inhibition of mechanical degradation and good dispersion of fillers. |
| Key words: machine learning multi-performance optimization natural rubber wear resistance |
| DOI：10.11916/j.issn.1005-9113.2021120 |
| Clc Number:TQ336.1 |
| Fund: |
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| Descriptions in Chinese: |
| 机器学习辅助天然橡胶复合材料的多性能优化设计 庞松1,2, 于洋1,2, 刘欢欢1,2, 吴友平1,3, (1．北京化工大学 有机无机复合材料国家重点实验室, 北京 100029；2. 北京化工大学 北京市新型高分子材料制备与加工重点实验室,, 北京 100029；3. 北京化工大学 北京先进弹性体工程研究中心, 北京 100029） 摘要：胎面胶复合材料的多性能优化是汽车工业十分关注的一个关键问题。因为多个性能之间相互抑制，采用“试错法”或直觉法等传统的实验设计方法十分低效。本文提出一种“均匀设计-机器学习”策略，用于胎面橡胶复合材料的性能预测和多性能优化。同时对耐磨性、滚动阻力、抗拉强度和抗湿滑性进行优化。通过统计学分析、机器学习分析和实验研究，筛选出一系列可行的优化设计方案。实验表明，通过机器学习分析得出的优化设计方案满足所有目标性能的优化要求，尤其是Akron磨损和60 ℃ tanδ（分别比设计目标低约21%和9%），证明该方案抑制了胶料的机械降解并实现了填料的良好分散。 关键词：机器学习; 多性能优化; 天然橡胶; 耐磨性 |
