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用人工智能预测增材制造的拉伸强度零件的能源消耗预测
Osman Ulkir1, Mehmet Said Bayraklılar2, Melih Kuncan3
1Department of Electric and Energy, Mus Alparslan University, Mus, Turkey.
3D printing and additive manufacturing
|January 1, 2025
概括
这项研究利用机器学习优化了增材制造 (AM) 的能源消耗. 高斯过程回归在化沉积模型中准确预测了能源消耗,实现了高精度的高效3D打印.
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
- 计算机科学 计算机科学
背景情况:
- 增材制造 (AM) 正在制造业中获得引力.
- 在快速原型设计中优化能源消耗至关重要,因为能源需求日益增加.
研究的目的:
- 通过确定最佳的生产参数来最大限度地降低3D打印中的能源消耗.
- 在化沉积建模 (FDM) 中开发精确的能源使用预测模型.
主要方法:
- 用四个机器学习算法来建模能源消耗.
- 来自测试样本的实时数据被用于训练预测模型.
- 用MSE,MAE,RMSE,R-squared和EVS来评估模型的性能.
主要成果:
- 高斯过程回归 (GPR) 在预测能源消耗方面表现出高准确度.
- 在GPR中,R平方=0.99,EVS=0.99,MAE=0.016,RMSE=0.022和MSE=0.00049.9等值得到了实现.
结论:
- 机器学习,特别是GPR,对于优化AM中的能源消耗是有效的.
- 精确的能源消耗预测可以实现更高效的3D打印工艺.
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