可解释的剩余工具寿命预测用于使用自动机器学习的个性化生产
Lukas Krupp1, Christian Wiede1, Joachim Friedhoff2
1Fraunhofer Institute for Microelectronic Circuits and Systems, 47057 Duisburg, Germany.
Sensors (Basel, Switzerland)
|October 28, 2023
概括
这项研究介绍了自动机器学习 (AutoML) 用于预测个性化制造中的切削工具寿命. 新方法优化了资源使用和经济效率,优于传统方法.
科学领域:
- 制造业自动化 制造业自动化
- 工业4.0 工业4.0 工业4.0 工业4.0 工业4.0 是什么?
- 机器学习 机器学习
背景情况:
- 复杂零件的个性化制造面临着由于不同的工艺条件和有限的自动化而带来的挑战.
- 在线监控和工具寿命预测对于自动化至关重要,但由于不明确的传感器与工具状况相关性而复杂化.
- 车间有限的机器学习 (ML) 专业知识阻碍了模型适应不断变化的条件.
研究的目的:
- 开发一种使用自动机器学习 (AutoML) 在个性化生产中预测剩余切削刀具寿命的新方法.
- 为了更好的适应性,使机械加工专家知识能够集成到ML模型中.
- 在复杂的制造环境中提高自动化程度和优化资源利用.
主要方法:
- 开发一种AutoML方法,用于端到端的ML管道创建,使用优化的回归和预测模型集.
- 通过模型输入和输出结合加工专家知识.
- 实施可解释性算法,以可视化输入对决策的相关性.
主要成果:
- 拟议的AutoML方法在应用于可变的工艺条件时,显著优于系列生产的最先进方法.
- 一个新的削数据集捕捉了在不断变化的参数下逐渐磨损的工具被用于评估.
- 该研究强调了将系列生产方法直接转移到可变制造场景中的困难.
结论:
- 开发的AutoML方法有效地优化了个性化生产在经济和资源方面.
- 具有有限的ML知识的机械加工专家可以利用他们的领域专业知识来开发,验证和调整工具寿命预测模型.
- 该方法促进了复杂的自动化和改进的决策在复杂的,定制制造设置.
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