Surface Variation Characterization and Control Using High-Definition Metrology.

Abstract

The surface shape of a machined part plays a significant role in affecting assembly performance. For example, surface variation on a deck face of a combustion engine block can impact the performance of the engine assembly, resulting in sealing problems and cam bore distortions. Control of such surface shape variation becomes a key enabler of high-precision machining and requires the characterization of surface shapes with fine lateral resolution. Past research on machined surface quality includes surface error characterization, flatness prediction, and error diagnosis. However, conventional metrology systems cannot capture the shape variation of large machined surfaces with sufficient lateral resolution due to the limited capability in measurement resolution and range. Recently, a new type of surface measurement system based on laser holographic interferometry (LHI) is made available for high-definition surface metrology. Such a measurement system can reveal shape variations of a large surface with fine resolution, providing opportunity for surface variation characterization and control. Using the LHI, this dissertation develops models and algorithms for surface shape variation characterization and reduction. Based on face milling, three research topics are addressed in this dissertation: 1. Characterization of surface variation induced by cutter-workpiece relative displacement: The surface patterns are extracted by modeling the impacts of cutting force variation on relative cutter-workpiece displacements and correlating such displacement with LHI measurements. 2. Characterization of surface variation induced by cutter-spindle deflection: The cutting force variation due to part geometry also causes the cutter-spindle to deflect, resulting in the surface variation along the cutter path. This research investigates the impact of cutter-spindle deflection on surface variation and develops algorithms for machine tool health monitoring. 3. Surface variation reduction: Based on the extracted surface patterns and derived cutting force models, a machining method for surface variation reduction is developed by optimizing cutting conditions and cutter path to redistribute cutting force real time. The research presented in this dissertation provides an in-depth understanding of the relationship between machined surface shape patterns and process conditions such as cutting forces and machine setup. The outcome of this research will lead to methodologies for cost-effective monitoring and control of surface variations.