基于强化学习的驱动波形的动态优化,用于粘弹性流体的喷墨打印
Seongju Kim1, Minsu Cho2, Sungjune Jung1
1Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), 77 Cheongam-Ro, Nam-gu, Pohang 37673, Republic of Korea.
Langmuir : the ACS journal of surfaces and colloids
|April 24, 2025
概括
本研究使用深度强化学习 (DRL) 来动态控制功能性油墨的喷墨打印波形. 人工智能系统优化喷射,用于精确的材料图案,适应不断变化的油墨特性.
科学领域:
- 材料科学 材料科学 材料科学
- 工程 工程师 工程师 工程师
- 人工智能的人工智能
背景情况:
- 通过数字打印对功能性材料的精确图案设计依赖于优化的压电打印头波形.
- 功能性油墨表现出由环境因素影响的可变性质,使得一致的喷射变得复杂.
研究的目的:
- 开发使用深度强化学习 (DRL) 的压电打印头的动态波形控制策略.
- 为了提高功能性喷墨喷射在数字印刷应用中的精度和适应性.
主要方法:
- 开发了一种多层感知子模型,以根据油墨形学和波形参数来预测掉落速度和喷射形态.
- 该预测模型被集成到DRL框架中,用于动态波形优化.
- 一个训练有素的DRL代理被应用到实时下降监控系统中,用于自适应控制.
主要成果:
- DRL方法实现了对驱动波形的精确控制,在20个步骤内达到量子点墨水的目标下降速度为3m/s.
- 该系统展示了实时波形调整功能,尽管温度引起的油墨属性变化,但仍然保持最佳喷射.
- 使用多层感知子模型实现了下降速度和喷射形态的准确预测.
结论:
- 机器学习,特别是DRL,为提高工业喷墨印刷的精度和适应性提供了巨大的潜力.
- 动态波形控制对于使用可变条件墨水的一致的功能材料图案至关重要.
- 开发的人工智能驱动系统可以实现强大且适应性强的喷墨印刷工艺.
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