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机器学习在聚合物平模挤出中的实施
Nickolas D Polychronopoulos1, Ioannis Sarris1, John Vlachopoulos2
1Department of Mechanical Engineering, University of West Attica, Ancient Olive Grove Campus, Egaleo, 12241 Athens, Greece.
Molecules (Basel, Switzerland)
|May 14, 2025
概括
机器学习模型可以预测和减少聚合物挤出中的厚度变化和缺陷. 这种方法最大限度地减少了试错,优化了无缺陷的聚合物薄膜和薄膜的生产.
科学领域:
- 聚合物加工加工的方法
- 材料科学是一种材料科学.
- 计算建模计算建模
背景情况:
- 聚合物板和片的平模挤压在实现均厚度和无缺陷生产方面面临着挑战.
- 目前的方法依赖于对不同聚合物等级和流速的试错,增加成本和时间.
研究的目的:
- 研究机器学习 (ML) 的应用,用于指导平模挤压工艺.
- 开发 ML 模型,预测并帮助减少挤出聚合物产品的厚度变化,不均性和缺陷.
主要方法:
- 使用计算机模拟平模挤压来生成200个病例的数据集.
- 变化包括模具几何,聚合物特性 (rheological,热物理) 和加工条件 (吞吐量,温度).
- 训练了三个ML算法 - 随机森林 (RF),XGBoost和支向量回归 (SVR) - 以预测厚度变化,压力下降和墙壁剪切率.
主要成果:
- 沙普利添加式扩张 (SHAP) 分析确定了影响挤出结果的关键特征.
- 聚合物质学,吞吐率和限制器下面的间隙是影响厚度变化和压力下降的关键因素.
- 训练有素的ML模型准确地预测了目标变量,证明了他们的预测能力.
结论:
- 基于ML的方法提供了一种有前途的方法来优化平模挤压工艺.
- 这种方法有可能显著减少或消除对广泛的试错程序的需求.
- 实施ML可以带来更高效和更具成本效益的高质量聚合物板材和薄膜的生产.
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