Evaluation of an FE Model for the Design of a Complex Thin-Wall CFRP Structure for a Scientific Instrument.

In this paper, the reliability of a finite element (FE) model including carbon-fibre reinforced plastics (CFRPs) is evaluated for a case of a complex thin-wall honeycomb structure designed for a scientific instrument, such as a calorimeter. Mechanical calculations were performed using FE models including CFRPs, which required a specific definition to describe the micro-mechanical behaviour of the orthotropic materials coupled to homogeneous ones. There are well-known commercial software packages used as powerful tools for analyzing structures; however, for complex (many-parts) structures, the models become largely time consuming for both definition and calculation, which limits the appropriate feedback for the structure's design.

Finite Element Simulation for Analysing the Design and Testing of an Energy Absorption System.

It is not uncommon to use profiles to act as energy absorption parts in vehicle safety systems. This work analyses an impact attenuator based on a simple design and discusses the use of a thermoplastic material. We present the design of the impact attenuator and a mechanical test for the prototype.

Application of a hybrid 3D-2D laser scanning system to the characterization of slate slabs.

Dimensional control based on 3D laser scanning techniques is widely used in practice. We describe the application of a hybrid 3D-2D laser scanning system to the characterization of slate slabs with structural defects that are difficult for the human eye to characterize objectively. Our study is based on automating the process using a 3D laser scanner and a 2D camera.

Identification of granite varieties from colour spectrum data.

The granite processing sector of the northwest of Spain handles many varieties of granite with specific technical and aesthetic properties that command different prices in the natural stone market. Hence, correct granite identification and classification from the outset of processing to the end-product stage optimizes the management and control of stocks of granite slabs and tiles and facilitates the operation of traceability systems. We describe a methodology for automatically identifying granite varieties by processing spectral information captured by a spectrophotometer at various stages of processing using functional machine learning techniques.