Component-specific Parameters Research Articles

Numerical and experimental study on protective film removal towards the automation of flexible electronics assembly

Flexible electronics is one of the most promising trends in the electronics industry, with increasing implementations in several application fields. However, in industrial applications, the assembly of film-based coverlays is still performed manually, representing a bottleneck in the whole production cycle, a source of defects caused by human errors, and introducing fatiguing tasks, such as the removal of the protective film covering the base material. In a novel methodology, this latter challenge is achieved by relying on the mechanical action of a rotating tool impacting the protective film. Such a process is typically stochastic and dependent on several parameters related to the tool-coverlay interaction, and the flexibility of film-type introduces further complexity. The aim of this paper is to investigate the influence of working conditions on the reliability of the process (i.e., success rate of the removal of the protective film). Finite element method (FEM) simulations are used to investigate and assess the stiffness exhibited by the component in response to the impacting force; therefore, a favorable gripping configuration is identified. An experimental campaign of the automated process is presented, aimed at assessing the effects of process parameters (tool rotating speed, adhesive thickness, approaching speed) on the protective film detachment. The results show that the process is predominantly affected by component-specific parameters, which, in turn, are significantly dependent on material supply conditions. Finally, useful insights are drawn to optimize the process and improve the design of the gripper of the robotized workcell.

Entropy Scaling-Based Correlation for Estimating the Self-Diffusion Coefficients of Pure Fluids

A new model for correlating self-diffusion coefficients is proposed here. It is based on the entropy scaling concept and makes it possible to quantitatively estimate self-diffusion coefficients in any fluid state (subcritical gas and liquid as well as supercritical states). A general relationship between a dimensionless form of the self-diffusion coefficient and a function of the Tv-residual entropy is proposed. This relationship involves thermodynamic properties that were straightforwardly estimated using the I-PC-SAFT and tc-PR equations of state (EoSs). First, the proposed model was parameterized using component-specific parameters. To do so, 72 pure components and ∼2400 pieces of experimental data were considered. Second, a chemical-family parametrization method was used. Four chemical families were defined, and parameter sets valid for all of the compounds of a given family were estimated. Regardless of the considered EoS, the component-specific parametrization enables us to describe data with an average deviation of 7.5%, while when using a chemical-family parametrization, this deviation reaches 9.5%.

On the inferential implications of decreasing weight structures in mixture models

Bayesian estimation of nonparametric mixture models strongly relies on available representations of discrete random probability measures. In particular, the order of the mixing weights plays an important role for the identifiability of component-specific parameters which, in turn, affects the convergence properties of posterior samplers. The geometric process mixture model provides a simple alternative to models based on the Dirichlet process that effectively addresses these issues. However, the rate of decay of the mixing weights for this model may be too fast for modeling data with a large number of components. The need for different decay rates arises. Some variants of the geometric process featuring different decay behaviors, while preserving the decreasing structure, are presented and investigated. An asymptotic characterization of the number of distinct values in a sample from the corresponding mixing measure is also given, highlighting the inferential implications of different prior specifications. The analysis is completed by a simulation study in the context of density estimation. It shows that by controlling the decaying rate, the mixture model is able to capture data with a large number of components.

Accounting for cross association in non-self-associating species using a physically consistent SAFT-VR Mie approach

This work presents a formalised, physically consistent approach to account for cross association in the SAFT framework. The focus of the approach was to account for solvation of non-self-associating components, using members of the ketone and ether functional groups as model components. Physical consistency is captured by only considering a single negative site in these components, with the association mechanisms termed the “N scheme” as a result. An accurate polar parameter set is a prerequisite for the approach, where polar and dispersion interactions are already accounted for in pure component properties and those of mixtures where solvation is absent. Using SAFT-VR Mie-GV as the example framework, the approach considers the discretisation of the two association parameters, and it applies the resulting matrix of parameter sets to mixture VLE data for the ketone or ether with alcohols in the C2 to C4 range. Analysis of the resulting AAD contour plots demonstrate that the use of average solvation parameters for each functional groups are appropriate, rather than component-specific parameters, and they offer excellent pure predictions for alcohol mixtures and good predictions for aqueous mixtures. As a final test, the approach was applied to chloroform, considering a single positive site and using the proposed parameterisation method. The resulting “P scheme” for chloroform, in conjunction with the N scheme for acetone, yields excellent predictions for the quintessential chloroform/acetone mixture and emphasises the suitability and predictive strength of the approach.

Telemetry Fault-Detection Algorithms: Applications for Spacecraft Monitoring and Space Environment Sensing

Algorithms have been developed that identify unusual behavior in satellite health telemetry. Telemetry from solid-state power amplifiers and amplifier thermistors from 32 geostationary Earth orbit communications satellites from 1991 to 2015 are examined. Transient event detection and change-point event detection techniques that use a sliding window-based median are used, statistically evaluating the telemetry stream compared to the local norm. This approach allows application of the algorithms to any spacecraft platform because there is no reliance in the algorithms on satellite- or component-specific parameters, and it does not require a priori knowledge about the data distribution. Individual telemetry data streams are analyzed with the event detection algorithms, resulting in a compiled list of unusual events for each satellite. This approach identifies up to six events of up to six events that affect 51 of 53 telemetry streams at once, indicative of a spacecraft system-level event. In two satellites, the same top event date (4 December 2008) occurs over more than 10 years of telemetry from both satellites. Of the five spacecraft with known maneuvers, the algorithms identify the maneuvers in all cases. Event dates are compared to known operational activities, space weather events, and available anomaly lists to assess the use of event detection algorithms for spacecraft monitoring and sensing of the space environment.

Estimation of Vapor Pressures and Enthalpies of Vaporization of Biodiesel-Related Fatty Acid Alkyl Esters. Part 2. New Parameters for Classic Vapor Pressure Correlations

In Part I of this series of articles, predictive models capable of estimating vapor pressures and enthalpies of vaporization of biodiesel-related esters have been reviewed. In this study, we propose component-specific parameters for classic vapor pressure correlations. First, the previously collected database was evaluated and used in the regression of Antoine coefficients for all compounds. Then, a constrained regression methodology was employed to generate extrapolatable Wagner parameters for the esters whose experimental critical properties were available. Finally, expressions derived from the Clausius–Clapeyron equation have been investigated for the estimation of enthalpies of vaporization. A comparative analysis of the results produced by our parameters and by others available in the literature encourages the use of our values in future applications.

Combinatorial Mixtures of Multiparameter Distributions: An Application to Bivariate Data.

We introduce combinatorial mixtures - a flexible class of models for inference on mixture distributions whose components have multidimensional parameters. The key idea is to allow each element of the component-specific parameter vectors to be shared by a subset of other components. This approach allows for mixtures that range from very flexible to very parsimonious and unifies inference on component-specific parameters with inference on the number of components. We develop Bayesian inference and computational approaches for this class of distributions, and illustrate them in an application. This work was originally motivated by the analysis of cancer subtypes: in terms of biological measures of interest, subtypes may be characterized by differences in location, scale, correlations or any of the combinations. We illustrate our approach using publicly available data on molecular subtypes of lung and prostate cancers.

A Short-Cut Method for the Quantification of Crystallization Kinetics. 1. Method Development

Population balance models are of high interest for the efficient design, control and optimization of crystallization processes. They usually contain mathematical submodels for the description of the relevant kinetic phenomena, such as growth, dissolution, and nucleation of particles. Commonly, component-specific parameters included in these submodels have to be determined for every substance system of interest or even for the specific experimental setup. Thus, a short-cut method is suggested that is based on analyzing the evolution of the crystal size distribution during a few batch crystallization experiments to efficiently parametrize kinetic submodels required to predict and evaluate the performance of crystallization processes. To illustrate the overall procedure and to evaluate the accuracy of the proposed approach, in silico data with predefined kinetics corresponding to hypothetical non-isothermal batch runs are analyzed. It is shown that it is possible to recover prespecified submodel parameters w...

Relabeling and Summarizing Posterior Distributions in Signal Decomposition Problems When the Number of Components is Unknown

This paper addresses the problems of relabeling and summarizing posterior distributions that typically arise, in a Bayesian framework, when dealing with signal decomposition problems with an unknown number of components. Such posterior distributions are defined over union of subspaces of differing dimensionality and can be sampled from using modern Monte Carlo techniques, for instance the increasingly popular RJ-MCMC method. No generic approach is available, however, to summarize the resulting variable-dimensional samples and extract from them component-specific parameters. We propose a novel approach, named Variable-dimensional Approximate Posterior for Relabeling and Summarizing (VAPoRS), to this problem, which consists in approximating the posterior distribution of interest by a simple---but still variable-dimensional---parametric distribution. The distance between the two distributions is measured using the Kullback-Leibler divergence, and a Stochastic EM-type algorithm, driven by the RJ-MCMC sampler, is proposed to estimate the parameters. Two signal decomposition problems are considered, to show the capability of VAPoRS both for relabeling and for summarizing variable dimensional posterior distributions: the classical problem of detecting and estimating sinusoids in white Gaussian noise on the one hand, and a particle counting problem motivated by the Pierre Auger project in astrophysics on the other hand.

Failure prognostic of systems with hidden degradation process

Systems with a hidden degradation process are pervasive in the real world. Degrading critical components will undermine system performance and pose potential failures in the future. Prognostic aims at predicting potential failures before it evolves into faults. A prognostic procedure based on expectation maximization and unscented Kalman filter is proposed. System state, sensor measurement and hidden degradation process are viewed as data (incomplete or missing) in the expectation maximization method. System state and hidden degradation process are estimated by a unscented Kalman filter upon sensor measurements. Component-specific parameters in a degradation process are identified on the estimation of the degradation process. Residual life is measured by the median of estimated residual life distribution. The proposed procedure is verified by simulations on a first-order capacitor-resistance circuit with degrading resistance. Residual life estimation consists conservatively with the trend and is evaluated in terms of relative errors. Simulation results are reasonable. The proposed prognostic method expects applications in practice.

Modelling time course gene expression data with finite mixtures of linear additive models

Summary: A model class of finite mixtures of linear additive models is presented. The component-specific parameters in the regression models are estimated using regularized likelihood methods. The advantages of the regularization are that (i) the pre-specified maximum degrees of freedom for the splines is less crucial than for unregularized estimation and that (ii) for each component individually a suitable degree of freedom is selected in an automatic way. The performance is evaluated in a simulation study with artificial data as well as on a yeast cell cycle dataset of gene expression levels over time.Availability: The latest release version of the R package flexmix is available from CRAN (http://cran.r-project.org/).Contact: Bettina.Gruen@jku.at

Probabilistic relabelling strategies for the label switching problem in Bayesian mixture models

The label switching problem is caused by the likelihood of a Bayesian mixture model being invariant to permutations of the labels. The permutation can change multiple times between Markov Chain Monte Carlo (MCMC) iterations making it difficult to infer component-specific parameters of the model. Various so-called ‘relabelling’ strategies exist with the goal to ‘undo’ the label switches that have occurred to enable estimation of functions that depend on component-specific parameters. Existing deterministic relabelling algorithms rely upon specifying a loss function, and relabelling by minimising its posterior expected loss. In this paper we develop probabilistic approaches to relabelling that allow for estimation and incorporation of the uncertainty in the relabelling process. Variants of the probabilistic relabelling algorithm are introduced and compared to existing deterministic relabelling algorithms. We demonstrate that the idea of probabilistic relabelling can be expressed in a rigorous framework based on the EM algorithm.

Rescaling of three-parameter equations of state: PC-SAFT and SPHCT

Two-parameter equations of state like PR or SRK predict a universal critical compressibility factor for pure compounds, being intrinsically unable to reasonably describe the PVT properties of different fluids and their asymmetric mixtures, which require at least three component specific parameters in the density dependence. In this paper, we have explored the capabilities of the Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) and the Simplified Perturbed Hard Chain Theory (SPHCT) equations of state to represent pure compound properties. Original parameters published by their respective authors overestimate critical temperatures, which is detrimental for the study and design of supercritical separation processes. Therefore, we have investigated here the performance of rescaled parameters for each model, i.e. those matching the experimental critical temperature and pressure of pure compounds. The rescaling of the size and energetic parameters as implemented in recent works in the literature was found to provide poor results, and instead a simple procedure to obtain optimized rescaled parameters from only T c, P c and the acentric factor is proposed. These parameters show a regular behaviour for n-alkanes with PC-SAFT and their performance is tested in this work also for carbon dioxide, using the recent equations by Span and Wagner as reference data. The proposed parameters reproduce vapour pressures with the same accuracy of PC-SAFT original parameters but assure an exact representation of the experimental critical pressure and temperature. However, this is achieved at the expense of underestimated liquid densities, this limitation being common to different models and becoming more pronounced for larger molecules.

Prediction and Correlation of the Thermal Conductivity of Pure Fluids and Mixtures, Including the Critical Region

An engineering correlation is presented for the prediction and correlation of the thermal conductivity of pure fluids and mixtures. The model is valid over the thermodynamic states from the dilute gas to the dense liquid and is applicable to nonpolar as well as polar fluids. A key feature of the model is that it provides a practical method for describing the strong critical enhancements of thermal conductivity in the broad vicinity of the vapor−liquid critical point. Quantitative correlations for the thermal conductivity of pure fluids and mixtures require a separation into background contributions and critical enhancements (Sengers, J. V. Int. J. Thermophys. 1985, 6, 203. Sengers, J. V.; Luettmer-Strathmann, J. In Transport Properties of Fluids: Their Correlation, Prediction and Estimation; Cambridge University Press: New York, 1996; p 113). For the background term, we use the model of Mathias and Parekh (Mathias, P. M.; Parekh, V. S. AIChE Annual Meeting, Chicago, IL, Nov 1996), which uses the principle of extended corresponding states, as previously developed by Ely and Hanley (Ely, J. F.; Hanley, H. J. M. Ind. Eng. Chem. Fundam. 1983, 22, 90). We propose a new phenomenological model to describe the strong critical enhancements. The model has been evaluated and validated through data for a variety of pure fluids and mixtures. The critical-point enhancements have been studied and validated for several pure fluids (methane, ethane, carbon dioxide, and R134a) and the methane−ethane mixture. In the noncritical region, the correlation can be used in the predictive as well as correlative modes, but it is recommended that the correlative model be used if data are available to tune the model parameters. In the critical region, component-specific parameters must be adjusted to quantitatively describe the strong enhancement in thermal conductivity.

On Protection of Freedom’s Solar Dynamic Radiator From the Orbital Debris Environment: Part I—Preliminary Analysis and Testing

A great deal of experimentation and analysis has been performed to quantify penetration thresholds of components which will experience orbital debris impacts. Penetration has been found to depend upon mission-specific parameters such as orbital altitude, inclination, and orientation of the component; and upon component specific parameters such as material, density, and the geometry particular to its shielding. Experimental results are highly dependent upon shield configuration and cannot be extrapolated with confidence to alternate shield configurations. Also, current experimental capabilities are limited to velocities which only approach the lower limit of predicted orbital debris velocities. Therefore, prediction of the penetrating particle size for a particular component having a complex geometry remains highly uncertain. This paper describes the approach developed to assess on-orbit survivability of the solar dynamic radiator due to micrometeroid and space debris impacts. Preliminary analyses are presented to quantify the solar dynamic radiator survivability, and include the type of particle and particle population expected to defeat the radiator bumpering (i.e., penetrate a fluid flow tube). Results of preliminary hypervelocity impact testing performed on radiator panel samples (in the 6 to 7 km/sec velocity range) are also presented. Plans for further analyses and testing are discussed. These efforts are expected to lead to a radiator design which will perform to Space Station Freedom requirements over the expected lifetime.