GOALI: Precision Measurement and Control of Machined Surfaces using Digital Holographic Data

全球制造业竞争促使企业大幅提高产品质量，导致公差越来越严格。即使是一个小错误也可能是产品发布成功和重大延迟之间的区别。精密加工表面在许多产品功能中都是至关重要的。例如，机械加工的汽车发动机甲板表面的质量可能会影响通过垫片区域的潜在漏油。它还可能影响发动机机体和发动机盖组件的变形，进而影响发动机的摩擦行为，从而影响发动机的燃油经济性。这个学术与工业联系机会(GOALI)研究项目将研究复杂表面形状产生的基本机制。研究结果将为利用数字激光全息测量中的最新创新开发精密表面测量和控制方法提供所需的知识。该项目的成果将极大地惠及美国制造业，并增强其核心竞争力。改善表面质量和实现主动过程控制可以通过降低生产中的废品率来提高制造质量，并将帮助工业建立对过程的主动控制。这项研究将为建立新的加工表面多尺度表面形状表征方法提供所需的知识。在这些方法中，将从工程表面的高清晰度数字全息测量数据中提取特征，并考虑加工过程参数。提取的特征将有助于在大、中尺度上识别表面质量问题的机理，即形状变形和波度变化。然后，可以在深入了解表面变化及其根本原因的基础上建立质量控制方法。通过利用数字激光全息面形测量的高清晰度数据和相关的重要工艺信息，本研究将为面形变化和控制提供新的见解。这些方法将使用工业合作伙伴提供的真实工厂数据进行测试和验证。

Global manufacturing competition drives companies to greatly improve the quality of their products, leading to increasingly strict tolerances. Even a small error can be the difference between a successful product launch and a major delay. Precision-machined surfaces are critical in many product functionalities. For example, the quality of machined automotive engine deck surface may have an impact on the potential oil leakage through gasket area. It may also affect the distortion of engine block and head assembly, which in turns affects the frictional behavior, and thus fuel economy of the engine. This Grant Opportunity for Academic Liaison with Industry (GOALI) research project will investigate the fundamental mechanism of complex surface shape generation. Results of the research will provide the knowledge needed to develop the methods for precision surface measurement and control utilizing the latest innovations in digital laser holographic measurement. The results of this project will greatly benefit US manufacturing sectors and enhance their core competencies. Improved surface quality and achieved proactive process control can improve the manufacturing quality by reducing the scrap rate in production and will help industry to establish proactive control over processes. This research will provide the knowledge needed to establish novel multi-scale surface shape characterization methods for machined surfaces. In these methods, features will be extracted from high-definition digital holographic measurement data of engineering surfaces with consideration of machining process parameters. The extracted features will help identify the mechanisms of surface quality issues in both large and medium scales, i.e., shape distortion and waviness variation. Quality control methods can then be established based on deep understanding of surface variations and their root causes. By leveraging the high definition data from digital laser holographic surface measurement and relevant important process information, this study will provide new insights into surface shape variation and control. The methodologies will be tested and validated using real factory data provided by the industrial partner.