Design and implementation issues for a class of distribution-free Phase II EWMA exceedance control charts

Distribution-free (nonparametric) control charts can play an essential role in process monitoring when there is dearth of information about the underlying distribution. In this paper, we study various aspects related to an efficient design and execution of a class of nonparametric Phase II exponentially weighted moving average (denoted by NPEWMA) charts based on exceedance statistics. The choice of the Phase I (reference) sample order statistic used in the design of the control chart is investigated. We use the exact time-varying control limits and the median run-length as the metric in an in-depth performance study. Based on the performance of the chart, we outline implementation strategies and make recommendations for selecting this order statistic from a practical point of view and provide illustrations with a data-set. We conclude with a summary and some remarks.     

Self-organizing maps for pattern recognition in design of alloys

A combined experimental–computational methodology for accelerated design of AlNiCo-type permanent magnetic alloys is presented with the objective of simultaneously extremizing several magnetic properties. Chemical concentrations of eight alloying elements were initially generated using a quasi-random number generator so as to achieve a uniform distribution in the design variable space. It was followed by manufacture and experimental evaluation of these alloys using an identical thermo-magnetic protocol. These experimental data were used to develop meta-models capable of directly relating the chemical composition with desired macroscopic properties of the alloys. These properties were simultaneously optimized to predict chemical compositions that result in improvement of properties. These data were further utilized to discover various correlations within the experimental dataset by using several concepts of artificial intelligence. In this work, an unsupervised neural network known as self-organizing maps was used to discover various patterns reported in the literature. These maps were also used to screen the composition of the next set of alloys to be manufactured and tested in the next iterative cycle. Several of these Pareto-optimized predictions out-performed the initial batch of alloys. This approach helps significantly reducing the time and the number of alloys needed in the alloy development process.     

Neural Networks Analysis of Steel Plate Processing Augmented by Multi‐objective Genetic Algorithms

An earlier neural network analysis of processing of steel plates through hot rolling was subjected to a further refined analysis through some flexible neural networks that evolved using a multi‐objective predator‐prey genetic algorithm. The original data set expressing the Yield Strength and Ultimate Tensile Strength of the rolled slabs in terms of a total of 108 process variables were subjected to a systematic pruning through this evolutionary approach, till the nitrogen content of the steel emerged as the most significant input variable. A theoretical explanation is provided for this slightly unexpected result.     

Using Singular Value Decomposition to Characterize Protein–Protein Interactions by In‐cell NMR Spectroscopy

Distinct differences between how model proteins interact in‐cell and in vitro suggest that the cytosol might have a profound effect in modulating protein–protein and/or protein–ligand interactions that are not observed in vitro. Analyses of in‐cell NMR spectra of target proteins interacting with physiological partners are further complicated by low signal‐to‐noise ratios, and the long overexpression times used in protein–protein interaction studies may lead to changes in the in‐cell spectra over the course of the experiment. To unambiguously resolve the principal binding mode between two interacting species against the dynamic cellular background, we analyzed in‐cell spectral data of a target protein over the time course of overexpression of its interacting partner by using single‐value decomposition (SVD). SVD differentiates between concentration‐dependent and concentration‐independent events and identifies the principal binding mode between the two species. The analysis implicates a set of amino acids involved in the specific interaction that differs from previous NMR analyses but is in good agreement with crystallographic data.     

Boxplot‐based phase I control charts for time between events

To monitor the quality/reliability of a (production) process, it is sometimes advisable to monitor the time between certain events (say occurrence of defects) instead of the number of events, particularly when the events occur rarely. In this case it is common to assume that the times between the events follow an exponential distribution. In this paper, we propose a one‐ and a two‐sided control chart for phase I data from an exponential distribution. The control charts are derived from a modified boxplot procedure. The charting constants are obtained by controlling the overall Type I error rate and are tabulated for some configurations. A numerical example is provided for illustration. The in‐control robustness and the out‐of‐control performance of the proposed charts are examined and compared with those of some existing charts in a simulation study. It is seen that the proposed charts are considerably more in‐control robust and have out‐control properties comparable to the competing charts. Copyright © 2011 John Wiley & Sons, Ltd.     

Dynamic process modelling of iron ore sintering

A mathematical model was developed for the iron ore sintering process considering all the major thermochemical phenomena in the system, assuming both the static and moving bed configurations. The model predicted a large number of parameters pertinent to the sintering process including the temperatures of the gas and solid, concentration of various species, amount of solid melted etc. The results were tested against the data existing in literature, and a limited number of pot tests conducted in a pilot plant. Despite high complexity of the problem, the agreement between the experimental and simulated data was reasonably good.