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动态加权合奏模型用于绿色砂的预测优化:推进工业4.0制造业
Rajesh V Rajkolhe1, Dr Sanjay S Bhagwat2, Dr Priyanka V Deshmukh3
1Babasaheb Naik College of Engineering, Pusad, Maharashtra, India.
MethodsX
|June 16, 2025
概括
一个新的动态加权组合 (DWE) 模型通过自适应组合算法来改善绿色砂造中的缺陷预测. 这种方法可以显著减少错误,提高精度,从而实现更智能的制造.
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
- 数据科学和机器学习
背景情况:
- 造缺陷显著影响产品质量,并增加制造中的拒绝率.
- 现有的预测模型在绿色砂中与相互依存的过程参数的非线性复杂性作斗争.
- 单个机器学习模型和静态组合方法的局限性需要先进的预测策略.
研究的目的:
- 引入一种新的动态加权合并 (DWE) 模型,用于在绿色砂中增强缺陷预测.
- 解决造过程中固有的非线性复杂性和相互依存的参数.
- 为了提高缺陷预测的准确性和一致性,超越单个机器学习模型.
主要方法:
- 开发了一个动态权重合集 (DWE) 模型,根据性能对算法进行适应性赋值.
- 通过十倍交叉验证评估了五种机器学习模型 (线性回归,回归,决策树,随机森林,梯度提升).
- 根据根平均平方误差 (RMSE) 选择了三大表现最好的模型进行整体集成.
- 将DWE模型应用于五倍未见的测试数据进行性能评估.
主要成果:
- 在未见测试数据上,DWE模型实现了8.07的平均RMSE.
- 与最佳个体模型相比,RMSE的2.1%改善和预测准确度的2.3%增加.
- 统计分析 (paired t-test) 证实了DWE模型的显著改善 (p < 0.05) 和优越的预测一致性.
- 确定了梯度提升 (RMSE:8.25),斜坡回归 (RMSE:8.30) 和线性回归 (RMSE:8.31) 作为表现最好的个体.
结论:
- 拟议的DWE模型为绿色砂造中缺陷预测提供了强大而适应性的解决方案.
- 增强的预测准确性和一致性支持实时优化和制造中的质量改进.
- 通过在智能制造环境中促进自动化,数据驱动的决策,DWE模型与工业4.0原则保持一致.
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