Understanding Manufacturing Process Dynamics and Machine Tool Anomaly Detection Through Process Sensing and Machine Learning

有效和高效的机床维护在保证制造生产率、产品质量、操作安全和盈利能力方面发挥着重要作用。随着过程传感、物联网、数据分析和云计算的发展，越来越多的制造工厂倾向于预测性维护而不是传统的预防性维护。预测性维护包括监控和预测机床状态和性能。它避免了不必要的维护，防止了机床的灾难性故障，从而节省了运行成本，提高了生产可靠性。然而，现有技术对机床异常或故障检测的准确性和可靠性不足，阻碍了预测性维修的全面实施。该奖项支持设计下一代工艺传感-机器学习架构的基础研究，以捕捉制造工艺动态，揭示产品质量对工艺设置和机器条件的潜在依赖。这项研究让工业界参与评估这种新型机床健康监测技术在实际制造工厂的性能和可扩展性，其结果为美国制造业在全球市场上提供了竞争优势。这项研究涉及先进制造、传感器网络、机器学习和计算等学科。该项目将所获得的知识应用于制造课程的开发，以使下一代工程师掌握制造和数据科学方面的新技能。该项目通过创新的机器学习技术发现过程观察因果关系，促进对复杂制造过程动力学的基本理解，以用于过程传感变异的根本原因分析和机器异常发生的检测。为了提高实际制造工厂数据驱动分析和决策的可信度和计算效率，设计了一种新一代过程感知-机器学习体系结构，该体系结构具有以下功能：1)在最优体系结构上进行高精度建模和高效计算，而无需大量的人工调整；2)通过模型训练与制造领域知识的集成，从物理上可解释地发现系统输入-输出因果关系和过程动力学；3)允许从不平衡数据建模和从不断变化的机器状态增量学习，而不需要重复模型训练，以实现健壮和可扩展的异常检测。开发了具有自动体系结构搜索的物理可解释卷积神经网络，以将从车削和铣削过程中获得的工艺参数、多变量传感数据(例如力、振动、功率)和零件质量规格(例如强度、表面质量)关联起来。然后，通过用于机器故障检测和分类的增量式稀疏分类器来诊断发现的关系。该项目的成果为系统地实现机床异常检测和预测性维护奠定了科学基础，这是现有技术无法实现的。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Effective and efficient machine tool maintenance plays a significant role in ensuring manufacturing productivity, product quality, operational safety, and profitability. With the advancement of process sensing, the Internet of Things, data analytics and cloud computing, more manufacturing plants are favoring predictive maintenance over traditional preventive maintenance. Predictive maintenance involves monitoring and predicting machine tool condition and performance. It avoids unnecessary maintenance and prevents catastrophic failure of machine tools, thereby saving operational costs and improving production reliability. However, there are barriers to fully implement predictive maintenance such as insufficient accuracy and reliability of machine tool anomaly or fault detection by existing techniques. This award supports fundamental research on designing a next-generation process sensing-machine learning architecture for capturing manufacturing process dynamics that reveals the underlying dependency of product quality on process settings and machine conditions. This research engages industry in assessing the performance and scalability of this novel machine tool health-monitoring technique at actual manufacturing plants, with the outcomes offering a competitive edge to the U.S. manufacturing sector in the global market. The research involves disciplines such as advanced manufacturing, sensor networks, machine learning, and computing. Knowledge gained is applied to developing manufacturing curricula to equip the next generation of engineers with new skills in manufacturing and data sciences.This project advances the fundamental understanding of complex manufacturing process dynamics for root cause analysis of process sensing variation and detection of machine anomaly occurrences, through discovering the process-observation causal relationships by an innovative machine learning technique. To improve the trustworthiness and computational efficiency of data-driven analysis and decision making in actual manufacturing plants, a next-generation process sensing-machine learning architecture is designed with capabilities in: 1) high-accuracy modeling and high-efficiency computation upon an optimal architecture without extensive manual tuning; 2) physically interpretable discovery of system input-output causal relationships and process dynamics through the integration of model training with manufacturing domain knowledge; 3) allowing for modeling from unbalanced data and incremental learning from evolving machine conditions without repeated model training for robust and scalable anomaly detection. A physically interpretable convolutional neural network with automated architecture search is developed to correlate process parameters, multivariate sensing data (e.g., force, vibration, power), and part quality specifications (e.g., strength, surface quality) that are acquired from turning and milling processes. The discovered relationships are then diagnosed by an incremental sparse classifier for machine fault detection and classification. The outcomes from this project establish a scientific foundation for the systemic realization of machine tool anomaly detection and predictive maintenance that is not achievable with existing techniques.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.