在钢铜激光接中检测裂的AI驱动的预测框架.
J Nagendra1, K S Prashanth2, Kavadiki Veerabhadrappa3
1Department of Robotics and Artificial Intelligence, Dayananda Sagar College of Engineering, Bengaluru, Karnataka, India.
Scientific reports
|November 25, 2025
概括
预测钢铜激光接中的裂至关重要. 这项研究使用机器学习,优化神经网络,在缺陷分类中达到94.9%的准确性,为智能接质量保证铺平了道路.
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
- 计算智能是一种计算智能.
背景情况:
- 钢铁和铜等不同材料的激光接通常会导致裂,损害关节的完整性.
- 传统的接缺陷检查方法效率低下,容易出现错误.
- 人们非常需要智能系统来预测接过程中的缺陷.
研究的目的:
- 开发和评估用于预测激光接钢铜接头缺陷的机器学习模型.
- 调查Orange数据挖掘工具在处理用于预测接缺陷的数据不平衡方面的有效性.
- 优化神经网络模型,以提高缺陷分类中的可靠性和预测性能.
主要方法:
- 利用通过最终选设计 (DSD) 生成的360个接截面的数据集.
- 使用Orange数据挖掘软件评估了多个机器学习分类器 (决策树,随机森林,梯度提升,SVM,神经网络).
- 应用了失衡处理策略,并优化了多层感知器 (MLP) 神经网络.
主要成果:
- 多层感知器 (MLP) 神经网络表现出卓越的性能.
- 获得了94.9%的准确性,86.3%的灵敏度和96.8%的特异性.
- 获得了0.961的曲线下的面积 (AUC),表明了强大的分类能力.
结论:
- 神经网络是用于分类钢铜激光接缺陷的有效和可扩展的工具.
- 该研究强调了Orange工具在接缺陷预测中的数据不平衡方面的成功应用.
- 这些发现支持在工业4.0制造业中实施智能流程监控和预测质量保证.
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