通过机器学习优化鞋类生产中的能源,停机时间和吞吐量
P K Sudhakar1, R Muthucumaraswamy2
1Department of Mathematics, Sri Venkateswara College of Engineering (Autonomous), (Affiliated to Anna University), Sriperumbudur, Chennai, Tamil Nadu, 602117, India. sudhakarpk_susila@yahoo.com.
Scientific reports
|December 12, 2025
概括
在鞋类制造中优化机器学习模型显著提高了生产效率. 改进后勤回归模型提高了缺陷预测的准确性,减少了停机时间,能源使用和浪费.
科学领域:
- 工业工程 工业工程 工业工程
- 制造业 制造技术 制造技术
- 数据科学数据科学数据科学
背景情况:
- 鞋类制造业在优化生产效率和减少浪费方面面临着挑战.
- 机器学习为提高工业环境中的预测能力提供了潜在的解决方案.
研究的目的:
- 研究机器学习模型优化对鞋类生产效率的影响.
- 为了比较优化后勤回归模型与其他监督学习算法的性能.
主要方法:
- 系统地改进用于缺陷预测的物流回归模型.
- 与支持矢量机器,天真贝叶斯和多项式分类器进行优化模型的基准测试.
- 使用准确性,特异性,灵敏性,F1得分和平衡准确性等指标评估模型性能.
主要成果:
- 优化后勤回归模型实现了97.06%的预测准确度和100%的特异性.
- 该模型在灵敏度,F1得分和平衡精度方面超过了其他算法.
- 工业性能改善包括生产吞吐量增加7.2%,设备停机时间减少9%,能源消耗减少5.3%.
结论:
- 调整的机器学习分类模型与实时分析集成,可以显著提高鞋类制造.
- 数据驱动的优化框架对于提高智能生产环境中的生产率,能源效率和可持续性至关重要.
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