| A new approach is presented for mapping the acoustic emission (AE) signal generated during ultraprecision machining; the graphic representation of several features of the interaction between tool and workpiece and its relation to microstructural features in the workpiece, particularly, grain boundaries and grain orientation. In ultraprecision scale machining, characteristics such as surface finish, chip topology, and the fundamental physics of machining are highly influenced by the tool and workpiece interaction. The crystallographic orientation of the workpiece is a major factor in determining final process outcomes; surface and edge conditions in particular. Hence, the intent of this dissertation is to address, at least partially, potential answers to the following two questions relevant to ultraprecision manufacturing. (1) Are process-induced defects, such as variation in surface finish, detectable during the machining operation? (2) How and why do surface and edge topography vary due to crystallographic orientation?; This dissertation will focus primarily on potential in-situ monitoring techniques for ultraprecision machining and the fundamental theory behind the effect of microstructure on both process outcome (such as surface and edge topography) and the sensor signals acquired during the process. For further process optimization, fundamental understanding of the material removal processes at the microscale must be developed. This understanding must be coupled with tools to give information feedback before, during, and after the manufacturing process, and ultimately serve as a means of monitoring and controlling the process, and ultimately, the resulting quality of the manufactured part. To reach this goal, a series of experiments were conducted to closely examine the effect of crystallographic orientation of both single and polycrystalline workpieces on the machining physics, resultant surface and edge condition, and sensor signal collected during the machining operation. Fundamental modeling work was also conducted to model the sensor signal as a function of crystallographic orientation, and a high degree of correlation between the experimental and theoretically modeled signal was found. The collected sensor signal was used to detect specific crystallographic orientation in the machining of single crystal workpieces, and graphically represent the microstructure of polycrystalline machined workpieces. When collected in a real-time fashion, the acquired sensor signal can serve as a basis for process monitoring and verification in a fully-automated and controlled manufacturing environment. |
