GOALI: Engineering-Driven Modeling of Multi-Resolution Data for Surface Variation Control

该GOALI项目开发了减少表面形状变化的方法和算法，通过将过程物理与多分辨率表面数据相结合，实现高成本效益的高精度加工，以进行质量诊断和监测。过程物理模型和多分辨率数据的集成将提高地表数据建模、变化预测精度和可解释性。基于改进的数据模型，将开发一种具有成本效益的表面测量策略和监测方案，通过该方案可以检测和识别异常过程变化，并降低误报率。该模型还将改进地表变化诊断。通过建模和监测相结合的方法，建立了两级表面变化控制方法，以提高多阶段加工过程的表面质量。PI和co-PI将与美国主要汽车制造商和/或动力系统供应商合作，进行数据采集、实验、算法验证和验证以及技术转让。本研究的结果为多阶段加工过程中的表面变化提供了新的见解，并将把工厂内的质量控制实践从减少尺寸变化转变为表面形状变化控制。如果成功，该项目将通过提高动力总成的加工精度和质量来增强美国制造业的竞争力。在本研究中建立的表面建模和控制框架侧重于面铣削，并可能有益于其他表面加工应用。此外，本研究建立的表面变化控制方法将有助于开发一种新型的光学表面测量系统，该系统可以智能地选择测量分辨率并确定缺陷表面区域。表面变化监测方案还可以通过减少原子力显微镜和三维轮廓术等高清计量学的测量时间，扩展到微制造过程的质量控制。

This GOALI project develops methodologies and algorithms to reduce surface shape variation, enabling cost-effective high-precision machining by integrating process physics with multi-resolution surface data for quality diagnosis and monitoring. The integration of the process physics model and multi-resolution data will improve surface data modeling, variation prediction accuracy, and interpretability. Based on the improved data model, a cost-effective surface measurement strategy and monitoring scheme will be developed by which abnormal process variations can be detected and identified with reduced false alarm rates. The model will also improve surface variation diagnosis. By combining the modeling and monitoring approaches, a two-level surface variation control methodology is established to improve surface quality for multistage machining processes. The PI and co-PIs will collaborate with major US automakers and/or powertrain system suppliers for data acquisition, experiments, algorithm verification and validation, and technology transfer.The outcome of this research provides new insights into surface variations in multistage machining processes and will transform in-plant quality control practice from dimensional variation reduction to surface shape variation control. If successful, this project will enhance US manufacturing competitiveness by improving powertrain machining precision and quality. The surface modeling and control framework established in this research focuses on face milling and can potentially benefit other surface machining applications. In addition, the methodology of surface variation control established in this research will contribute to development of a novel optical surface measurement system that can intelligently select metrology resolution and determine defective surface regions. The surface variation monitoring scheme can also be extended to quality control for micro manufacturing processes by reducing the measurement time of high-definition metrology such as atomic force microscopy and 3D profilometry.