基于凝的增材制造中的机器学习:从材料设计到过程优化
Zhizhou Zhang1, Yaxin Wang2, Weiguang Wang3
1Department of Mechanical and Aerospace Engineering, School of Engineering, The University of Manchester, Manchester M13 9PL, UK.
Gels (Basel, Switzerland)
|August 28, 2025
概括
机器学习加速基于凝的增材制造,用于材料设计和工艺控制. 这篇评论强调了凝配方,可打印性预测和实时优化方面的进展,为高效的材料发现铺平了道路.
科学领域:
- 添加剂制造
- 材料科学
- 人工智能
背景情况:
- 基于凝的增材制造 (GAM) 传统上依靠试错来设计材料和优化工艺.
- 现有的方法在预测凝特性和确保一致的打印能力方面存在局限性.
- 加快材料发现和过程控制对于推进GAM应用至关重要.
研究的目的:
- 提供GAM中机器学习 (ML) 应用的全面审查.
- 探索ML在凝配方,可打印性预测和实时过程控制中的作用.
- 确定GAM的当前挑战和未来方向.
主要方法:
- 对应用到GAM的ML算法 (例如神经网络,随机森林,支向量机器) 的最新文献的审查.
- 使用组合和处理数据分析ML在模拟凝特性 (风湿性,弹性,胀,粘性) 的能力.
- 检查数据驱动的配方和闭环机器人的进步.
主要成果:
- ML可以从各种数据集中准确地建模凝特性.
- 数据驱动的方法和机器人正在将GAM转变为自主材料发现.
- 在预测性打印和实时流程调整方面取得了重大进展.
结论:
- 在GAM中,ML集成显著提高了材料设计和流程优化.
- 解决数据稀疏性,模型稳定性和系统集成是关键的挑战.
- 未来的工作应集中在多式传感,生成设计和自动化实验,用于组织工程,生物医学设备和可持续材料的更广泛应用.
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