Virtual Manufacturing of Composite Structures Applying Implicit and Explicit FE Techniques

Virtual manufacturing of composites can yield an initial early estimation of the induced residual thermal stresses that affect component fatigue life, and deformations that affect required tolerances for assembly. Based on these estimation, the designer can make early decisions, which can help in reducing cost, regarding changes in part design or material properties. In this paper, an approach is proposed to simulate the autoclave manufacturing technique for unidirectional composites. The proposed approach consists of three modules. The first module is a Thermochemical model to estimate temperature and the degree of cure distributions in the composite part during the cure cycle. The second and third modules are stress analysis using FE-Implicit and FE-Explicit respectively. User-material subroutine will be used to model the Viscoelastic properties of the material based on micromechanical theory. Estimated deformation of the composite part can be corrected during the autoclave process by modifying the process-tool design. The deformed composite surface is sent to CATIA for design modification of the process-tool.

Micro-Satellite Structure Fracture Investigation techniques

Mounting accuracy of satellite payload and
ADCS (attitude determination and control subsystem) seats
is one of the requirements to achieve the satellite mission
with satisfactory performance. Deviation of the position of
the mounting seat for Multi-Band-Earth-Imager (MBEI) is
caused by cracks in the plate of the basis unit and bracket
for attachment of MBEI. These cracks were detected during
inspection of the satellite strength mock-up after vibration
testing for air transportation phase. Most probable reason of
the cracking is fatigue damage as strength mock-up
structure was subjected to prolonged vibration loading
during various loading cases. Total vibration duration
during testing is about 56 hours. In order to study the
cracking reasons, finite element modeling of the structural
parts of the basis unit including MBEI bracket and
instrument MBEI is subjected to harmonic response to
simulate vibration loading for the case of air transportation.
Numerical results are compared with the experimental ones,
and mechanical design of the basis-plate unit is modified

Using Noise Techniques to Destabilize Composition and Improvisation

A description of the uses of computer-generated noise in my instrumental scores and guided improvisations.

Predicting organic acid concentration from UV/vis spectrometry measurements - A comparison of machine learning techniques

The concentration of organic acids in anaerobic digesters is one of the most critical parameters for monitoring and advanced control of anaerobic digestion processes. Thus, a reliable online-measurement system is absolutely necessary. A novel approach to obtaining these measurements indirectly and online using UV/vis spectroscopic probes, in conjunction with powerful pattern recognition methods, is presented in this paper. An UV/vis spectroscopic probe from S::CAN is used in combination with a custom-built dilution system to monitor the absorption of fully fermented sludge at a spectrum from 200 to 750 nm. Advanced pattern recognition methods are then used to map the non-linear relationship between measured absorption spectra to laboratory measurements of organic acid concentrations. Linear discriminant analysis, generalized discriminant analysis (GerDA), support vector machines (SVM), relevance vector machines, random forest and neural networks are investigated for this purpose and their performance compared. To validate the approach, online measurements have been taken at a full-scale 1.3-MW industrial biogas plant. Results show that whereas some of the methods considered do not yield satisfactory results, accurate prediction of organic acid concentration ranges can be obtained with both GerDA and SVM-based classifiers, with classification rates in excess of 87% achieved on test data.