基于不同代理模型的TP2铜管壁厚在线预测的比较研究
Fengli Yue1, Zhuo Sha1, Hongyun Sun1
1Automotive and Transportation College, Shenyang Ligong University, Shenyang 110159, China.
Materials (Basel, Switzerland)
|December 17, 2024
概括
滚动后TP2铜管支架的壁厚不均会影响最终产品的质量. 优化粒子群支持矢量机 (PSO-SVM) 模型准确预测关联拉伸壁厚度,改善生产指导.
科学领域:
- 材料科学 材料科学 材料科学
- 制造业 工程 制造工程
- 计算建模 计算建模
背景情况:
- 在三卷行星旋转滚动后TP2铜管造片的不均壁厚影响了随后的铜管加工.
- 确保墙壁厚度的均性对于铜管的完整性和性能至关重要.
研究的目的:
- 为了研究初始墙壁厚度变化对铜管在接头绘制后最终墙壁厚度的影响.
- 使用实验和模拟数据开发和比较联合拉伸壁厚度的预测模型.
主要方法:
- 在线超声波测试平台,用于测量绑定后的铜管壁厚度.
- 有限元模型 (FEM) 用于联合绘图的数值模拟.
- 应用和比较各种神经网络模型,包括支持矢量机 (SVM),用于预测.
- 使用粒子集群优化 (PSO-PSO-SVM) 优化SVM参数的优化.
主要成果:
- 超声波测试和FEM提供了用于预测建模的数据.
- SVM模型显示出良好的预测和合适性,但遭受过度合适.
- 与传统的SVM相比,PSO-SVM模型在预测关节抗拉壁厚度方面表现出更好的适用性.
- 优化的PSO-SVM有效地解决了过问题.
结论:
- PSO-SVM模型提供了一种更准确,更可靠的方法来预测TP2铜管的联合拉伸壁厚度.
- 这种预测能力可以显著指导生产流程,以减轻因墙壁厚度不均而产生的问题.
- 该研究强调了整合实验数据,FEM和优化机器学习模型用于制造质量控制的潜力.
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