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

该 GOALI 项目开发方法和算法来减少表面形状变化，通过将过程物理与多分辨率表面数据集成以进行质量诊断和监控，从而实现经济高效的高精度加工。过程物理模型和多分辨率数据的集成将提高表面数据建模、变化预测的准确性和可解释性。基于改进的数据模型，将开发具有成本效益的表面测量策略和监测方案，通过该策略和监测方案可以检测和识别异常过程变化，并降低误报率。该模型还将改进表面变化诊断。通过结合建模和监控方法，建立了两级表面变化控制方法，以提高多级加工过程的表面质量。 PI和共同PI将与美国主要汽车制造商和/或动力总成系统供应商合作，进行数据采集、实验、算法验证和验证以及技术转让。这项研究的成果为多级加工过程中的表面变化提供了新的见解，并将将厂内质量控制实践从尺寸变化减少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加工精度和质量来增强美国制造业的竞争力。本研究中建立的表面建模和控制框架侧重于面铣削，并且可能有利于其他表面加工应用。此外，本研究中建立的表面变化控制方法将有助于开发一种新型光学表面测量系统，该系统可以智能地选择计量分辨率并确定有缺陷的表面区域。通过减少原子力显微镜和 3D 轮廓测量等高清计量的测量时间，表面变化监测方案还可以扩展到微制造工艺的质量控制。

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.