It is highly challenging to predict the dynamics of (assembled) structures that are too large or complex to be analyzed as a whole. To address this issue, we are working on developing an spectral Tchebychev simulation framework to analyze highly complex structures. To predict the dynamics of (assembled) structures, a substructuring algorithm will be applied to divide the complex structure into simpler geometries. Then, each of these simpler substructures will be solved using the ST method. Depending on the geometry of each substructure and also to increase the numerical efficiency, either a 1D-, 2D-, or 3D-ST approach will be implemented to obtain dynamic behavior. Lastly, to obtain the overall dynamics of the assembly structure, substructures will be combined via a component mode synthesis approach or a frequency based coupling technique.
Modeling and Experimentation of Dynamics of Mechanical Micromachining
Mechanical micromachining is an emerging technique for producing three dimensional complex micro scale geometries on a broad range of materials. In particular, it finds applications in biomedical and analytical devices, tribological surfaces, and medical devices, to name a few. Effectively addressing the strict accuracy requirements of the micromachining application necessitates understanding and control of dynamic behavior of micromachining system, including motion actuators, spindle, and the tool, as well as their coupling with the mechanics of the material removal process. The dynamic behavior of the tool-holder-spindle-machine assembly, as reflected at cutting tip of a micro-tool, often determines the achievable process efficiency and quality. However, the existing (macro-scale) techniques cannot be used to accurately model micromachining dynamics. Furthermore, new experimental techniques are needed to determine the speed-dependent modal characteristics of the ultra-high-speed spindles that are used during micromachining. The overarching objective of this research is to derive and validate models for the micromachining process dynamics to enable prediction of micromachining process accuracy and efficiency (throughput).
