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

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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.