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

Regents Of The University Of Michigan - Ann Arbor, Ann Arbor MI

Investigators

Abstract

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.