Grain size effects in the forming of ultra-thin sheets
Introduction The number of micro scale applications of metals is growing rapidly. Progressive miniaturization has become a decisive characteristic of developments in electronic devices (Figure 1) or electronically controlled mechanical systems. Along with the miniaturization so-called size effects occur in sheet metal manufacturing processes. These effects are assumed to be caused by changes in the relative contribution of the different micro-structural events.
A crystallographic model for the study of local deformation processes in polycrystals
Abstract Most engineering materials possess a polycrystalline structure. Under load the anisotropy of the constituent grains leads to strong inhomogeneities of stresses and strains on the grain level. In order to investigate the local deformation processes, a new crystallographic model for pure fcc metals in the low temperature range has been developed. It is based on the framework of crystal plasticity and uses the finite element method (FEM). The rate dependent constitutive equations consider isotropic as well as kinematic hardening, whereby the mutual interactions of dislocation processes on the different slip systems are taken into account. Comprehensive calculations show that the essential features of both single crystals—which serve as a test object for the constitutive equations—and polycrystals are reproduced correctly. Moreover the simulations allow a deeper understanding of the mechanisms that control the local deformation behaviour of metals, especially of the mutual interactions of slip system activity, local hardening and resulting local strain. Furthermore, the model may serve as a physically motivated base for a later inclusion of damage terms which allow investigations of damage and fatigue on the local scale.
Surface roughness monitoring by singular spectrum analysis of vibration signals
Abstract This study assessed two methods for enhanced surface roughness (Ra) monitoring based on the application of singular spectrum analysis (SSA) to vibrations signals generated in workpiece-cutting tool interaction in CNC finish turning operations i.e., the individual analysis of principal components (I-SSA), and the grouping analysis of correlated principal components (G-SSA). Singular spectrum analysis is a non-parametric technique of time series analysis that decomposes a signal into a set of independent additive time series referred to as principal components. A number of experiments with different cutting conditions were performed to assess surface roughness monitoring using both of these methods. The results show that singular spectrum analysis of vibration signal processing discriminated the frequency ranges effective for predicting surface roughness. Grouping analysis of correlated principal components (G-SSA) proved to be the most efficient method for monitoring surface roughness, with optimum prediction and reliability results at a lower analytical-computational cost. Finally, the results show that singular spectrum analysis is an ideal method for analyzing vibration signals applied to the on-line monitoring of surface roughness.