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基于可融合转移学习的大型工件激光制造的能源消耗预测

Linxuan Wang¹, Jinghua Xu^2,3,4, Shuyou Zhang^5,1

¹Engineering Research Center for Design Engineering and Digital Twin of Zhejiang Province, Zhejiang University, Hangzhou, Zhejiang, 310058, China.

Visual computing for industry, biomedicine, and art

|December 2, 2024

概括

本研究介绍了可融合转移学习 (FTL) 以准确预测激光金属制造中的能源消耗. 这种方法有助于扩大金属文物生产的预测,支持成本估计和可持续性目标.

科学领域:

制造业工程制造工程
材料科学材料科学材料科学
人工智能的人工智能

背景情况:

精确的能源消耗 (EC) 预测对于优化制造工艺和实现可持续性目标至关重要.
由于复杂的可变相互依存关系,在激光融中预测EC在不同尺度 (缩放到扩大) 中存在重大挑战.

研究的目的:

开发和验证一种新的能源消耗预测方法,用于金属制品的激光炼制造.
通过可扩展的方法,为小规模和大规模的文物生产提供准确的EC预测.
通过改进增材制造中的能源管理来支持碳中和倡议.

主要方法:

建立了一个将制造过程分为三个子步骤的一般范式.
运行电力被建模为一个组合函数,使用运营商学习网络用于非线性关系.
并行神经网络分析了制造变量和设备影响,并合输出用于联合电力预测.
大型文物结构被分解成依赖时间的激光扫描轨迹,通过神经网络转化为可融合的信息,灵感来自大型语言模型.

主要成果:

拟议的可融合转移学习 (FTL) 框架在 EC 预测规模结构方面表现出高准确性.
使用激光粉床融合的物理制造实验证实了FTL的有效性.
使用FTL的平均和整体EC预测的相对误差始终低于0.83%.

结论:

FTL框架为激光造制造业的能源消耗预测提供了可扩展和准确的解决方案.
这种方法对于大型金属产品的价格估计和报价特别有价值,有助于实现碳峰值和碳中和目标.
该研究验证了转移学习的潜力,灵感来自于大型语言模型,用于解决增材制造能源预测中的复杂挑战.

关键词:

碳峰值和碳中和性的碳峰值.预测能源消耗的预测融合式转移学习是可以实现的.大型金属文物大型金属文物激光粉末床融合激光粉末床融合

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