建立基于机器学习的模型,为中小型压制造商的最佳造条件管理
1Department of Industrial and Systems Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul, 04620, South Korea.
Scientific reports
|October 11, 2023
概括
这项研究使用机器学习来优化压参数,通过分析智能工厂数据来提高零件质量. 定制模型对于每个公司来说都是必不可少的,因为它具有独特的特征重要性.
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
- 人工智能的人工智能
背景情况:
- 压生产复杂,高精度的零件,但由于缺陷而遭受质量问题.
- 造参数控制通常依赖于操作员的经验,缺乏系统的优化.
- 中小型压企业正在实施智能工厂 (制造执行系统 - MES) 并收集生产数据.
研究的目的:
- 开发一种机器学习模型,利用收集的MES数据优化压参数.
- 通过识别和控制关键的造变量来提高产品质量.
- 调查一个通用的与公司特定的优化模型的可行性.
主要方法:
- 在中小型压公司从MES收集数据.
- 机器学习模型的开发,以预测和优化造参数.
- 功能重要性分析,以确定质量控制的关键参数.
主要成果:
- 对每家公司来说,都确定了影响质量的显著重要特征.
- 该研究表明,通用模型的效果不如量身定制的模型.
- 成功构建基于机器学习的最佳造参数模型.
结论:
- 机器学习有效地优化压参数,以提高质量.
- 由于独特的运营特征和特征的重要性,公司特定的模型是必要的.
- 定制优化与订制制造 (MTO) 制造环境保持一致.
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