An Integration Methodology for Automated Recurring Cost Prediction using Digital Manufacturing Technology

The need to account for the effect of design decisions on manufacture and the impact of manufacturing cost on the life cycle cost of any product are well established. In this context, digital design and manufacturing solutions have to be further developed to facilitate and automate the integration of cost as one of the major driver in the product life cycle management. This article is to present an integration methodology for implementing cost estimation capability within a digital manufacturing environment. A digital manufacturing structure of knowledge databases are set out and the ontology of assembly and part costing that is consistent with the structure is provided. Although the methodology is currently used for recurring cost prediction, it can be well applied to other functional developments, such as process planning. A prototype tool is developed to integrate both assembly time cost and parts manufacturing costs within the same digital environment. An industrial example is used to validate this approach.

A multi-fibre multi-layer representative volume element (M2RVE) for prediction of matrix and interfacial damage in composite laminates

Multiscale micro-mechanics theory is extensively used for the prediction of the material response and damage analysis of unidirectional lamina using a representative volume element (RVE). Th is paper presents a RVE-based approach to characterize the materi al response of a multi-fibre cross-ply laminate considering the effect of matrix damage and fibre-matrix interfacial strength. The framework of the homogenization theory for periodic media has been used for the analysis of a 'multi-fibre multi-layer representative volume element' (M2 RVE) representing cross-ply laminate. The non-homogeneous stress-strain fields within the M2RVE are related to the average stresses and strains by using Gauss theorem and the Hill-Mandal strain energy equivalence principle. The interfacial bonding strength affects the in-plane shear stress-strain response significantl y. The material response predicted by M2 RVE is in good agreement with the experimental results available in the literature. The maximum difference between the shear stress predicted using M2 RVE and the experimental results is ~15% for the bonding strength of 30MPa at the strain value of 1.1%

Prediction of ionic liquid properties. II. Volumetric properties as a function of temperature and pressure

The density of ionic liquids (ILs) as a function of pressure and temperature has been modeled using a group contribution model. This model extends the calculations previously reported (Jacquemin et al. J. Chem. Eng. Data 2008) which used 4000 IL densities at 298.15 K and 600 IL densities as a function of temperature up to 423 K at 0.1 MPa to pressures up to 207 MPa by using described data in the literature and presented in this study. The densities of two different ionic liquids (butyltrimethylammonium bis(trifluoromethylsulfonyl)imide, [N][NTf], and 1-butyl-l-methyl-pyrrolidiniumbis(trifluoromethylsulfonyl)imide, [C mPyrro]-[NTf]) were measured as a function of temperature from (293 to 415) K and over an extended pressure range from (0.1 to 40) MPa using a vibrating-tube densimeter. The model is able to predict the ionic liquid densities of over 5080 experimental data points to within 0.36%. In addition, this methodology allows the calculation of the mechanical coefficients using the calculated density as a function of temperature and pressure with an estimated uncertainty of ± 20%. © 2008 American Chemical Society.

Low pressure carbon dioxide solubility in lithium-ion batteries based electrolytes as a function of temperature. Measurement and prediction

We present in this study the effect of nature and concentration of lithium salt, such as the lithium hexafluorophosphate, LiPF6; lithium tris(pentafluoroethane)-trifluorurophosphate LiFAP; lithium bis(trifluoromethylsulfonyl)imide, LiTFSI, on the CO2 solubility in four electrolytes for lithium ion batteries based on pure solvent that include ethylene carbonate (EC), dimethyl carbonate (DMC), ethyl methyl carbonate (EMC), diethyl carbonate (DEC), as well as, in the EC:DMC, EC:EMC and EC:DEC (50:50) wt.% binary mixtures as a function of temperature from (283 to 353) K and atmospheric pressure. Based on experimental solubility values, the Henry’s law constant of the carbon dioxide in these solutions with the presence or absence of lithium salt was then deduced and compared with reported values from the literature, as well as with those predicted by using COSMO-RS methodology within COSMOThermX software. From this study, it appears that the addition of 1 mol · dm-3 LiPF6 salt in alkylcarbonate solvents decreases their CO2 capture capacity. By using the same experimental conditions, an opposite CO2 solubility trend was generally observed in the case of the addition of LiFAP or LiTFSI salts in these solutions. Additionally, in all solutions investigated during this work, the CO2 solubility is greater in electrolytes containing the LiFAP salt, followed by those based on the LiTFSI case. The precision and accuracy of the experimental data reported therein, which are close to (1 and 15)%, respectively. From the variation of the Henry’s law constant with temperature, the partial molar thermodynamic functions of dissolution such as the standard Gibbs energy, the enthalpy, and the entropy, as well as the mixing enthalpy of the solvent with CO2 in its hypothetical liquid state were calculated. Finally, a quantitative analysis of the CO2 solubility evolution was carried out in the EC:DMC (50:50) wt.% binary mixture as the function of the LiPF6 or LiTFSI concentration in solution to elucidate how ionic species modify the CO2 solubility in alkylcarbonates-based Li-ion electrolytes by investigating the salting effects at T = 298.15 K and atmospheric pressure.

IO Performance Prediction in Consolidated Virtualized Environments

We propose a trace-driven approach to predict the performance degradation of disk request response times due to storage device contention in consolidated virtualized environments. Our performance model evaluates a queueing network with fair share scheduling using trace-driven simulation. The model parameters can be deduced from measurements obtained inside Virtual Machines (VMs) from a system where a single VM accesses a remote storage server. The parameterized model can then be used to predict the effect of storage contention when multiple VMs are consolidated on the same virtualized server. The model parameter estimation relies on a search technique that tries to estimate the splitting and merging of blocks at the the Virtual Machine Monitor (VMM) level in the case of multiple competing VMs. Simulation experiments based on traces of the Postmark and FFSB disk benchmarks show that our model is able to accurately predict the impact of workload consolidation on VM disk IO response times.

In silico prediction of the effects of mutations in the human UDP-galactose 4'-epimerase gene:Towards a predictive framework for type III galactosemia

The enzyme UDP-galactose 4'-epimerase (GALE) catalyses the reversible epimerisation of both UDP-galactose and UDP-N-acetyl-galactosamine. Deficiency of the human enzyme (hGALE) is associated with type III galactosemia. The majority of known mutations in hGALE are missense and private thus making clinical guidance difficult. In this study a bioinformatics approach was employed to analyse the structural effects due to each mutation using both the UDP-glucose and UDP-N-acetylglucosamine bound structures of the wild-type protein. Changes to the enzyme's overall stability, substrate/cofactor binding and propensity to aggregate were also predicted. These predictions were found to be in good agreement with previous in vitro and in vivo studies when data was available and allowed for the differentiation of those mutants that severely impair the enzyme's activity against UDP-galactose. Next this combination of techniques were applied to another twenty-six reported variants from the NCBI dbSNP database that have yet to be studied to predict their effects. This identified p.I14T, p.R184H and p.G302R as likely severely impairing mutations. Although severely impaired mutants were predicted to decrease the protein's stability, overall predicted stability changes only weakly correlated with residual activity against UDP-galactose. This suggests other protein functions such as changes in cofactor and substrate binding may also contribute to the mechanism of impairment. Finally this investigation shows that this combination of different in silico approaches is useful in predicting the effects of mutations and that it could be the basis of an initial prediction of likely clinical severity when new hGALE mutants are discovered.