Specification of an Operator for the Design of Architectural Forms: “Pleating”
During the creative phase, the architect introduces hypotheses and validates them by trying out various adjustments during the process of conception. The use of existing CAD tools during the conception phase is not compatible with the iterative aspect of this process. So the challenge is to define a model that takes into account the entire creative process in a trial and error framework during the conceptual phase. In this article we will define the “pleating” operator.
A Study for Parametric Morpho-Semantic Operators to Assist Architectural Conception at the Drafting Stage
In this paper, we make the assumption that a shape modelling process can rely on the application of a set of morpho-semantic operators to initial shapes. In this paper, we refer to several researches which have attempted to identify such operators. We will attempt to validate this concept through the analysis of some ''innovative" buildings. Thus, in different projects of contemporary architects, we can find the trace of some transformations (e.g. "twist", "merge", "overlay", etc.) recorded in the final geometry. These transformations have a symbolic representation. A design system based on the combination of these operators could enable the designer to quickly explore a great number of spatial solutions
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.