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人工智能的应用用于增材制造组件的表面粗度预测

Temesgen Batu^1,2, Hirpa G Lemu³, Hailu Shimels⁴

¹Department of Aerospace Engineering, Ethiopian Space Science and Geospatial Institute, Addis Ababa P.O. Box 33679, Ethiopia.

Materials (Basel, Switzerland)

|September 28, 2023

概括

人工智能 (AI) 和机器学习提高了增材制造中的表面粗度预测. 这些方法改善了质量控制,减少了再加工,并提高了整体生产效率.

科学领域:

材料科学与工程材料科学与工程
制造业制造技术制造技术
计算科学计算科学

背景情况:

增材制造 (AM) 提供生产效率,但难以保持一致的产品质量.
表面粗度是一个关键的质量参数,影响汽车和航空航天等关键行业的性能.
预测和控制AM中的表面粗度对于满足行业标准至关重要.

研究的目的:

审查人工智能 (AI) 和机器学习 (ML) 的最新进展,以预测AM中的表面粗度.
确定人工智能应用在AM表面质量的挑战和未来方向.
突出AI在提高AM生产力和竞争力方面的潜力.

主要方法:

对人工智能和机器学习技术的现有研究进行了审查,这些技术应用于AM中的表面粗度预测.
分析用于表面质量评估的深度学习和机器学习模型.
对人工智能在降低成本和时间方面的有效性进行综合研究.

主要成果:

包括深度学习在内的AI和ML在预测AM组件的表面粗性方面是有效的.
这些人工智能方法显示出降低成本和节省时间的巨大潜力.
成功应用人工智能将再处理最小化,并确保符合技术规格.

结论:

人工智能集成对于提高增材制造的生产力和竞争力至关重要.
人工智能驱动的表面粗度预测解决了实现一致产品质量的关键挑战.
未来的研究应该专注于进一步开发和实施用于先进的AM应用的AI方法.

关键词:

添加剂制造添加剂制造添加剂制造机器学习是机器学习.粗度预测预测粗度预测表面的粗度表面的粗度

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