Inferring causal molecular networks: empirical assessment through a community-based effort

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

Estimation of dilution in laser cladding based on energy balance approach using regression analysis

Laser cladding is a complex manufacturing process involving more than 19 variables related to laser source, workpiece movement, powder-substrate material combinations, clad geometry, powder flow dynamics, shrouding gas flow and so on. Significant research efforts have been directed to analytical-numerical-empirical modelling of laser cladding and also in-process monitoring and control of the process. Still, due to complicated physics there is a dearth of simple analytical model for estimation of dilution in laser cladding. Its experimental measurement requires suitable micrographs of the clad cross section perpendicular to the clad path. This is a time-consuming and destructive way of measurement. Numerical models are time consuming to evaluate and hence not suitable for fast decision making or real-time control implementation. The analytical models available, despite having many approximations, are a little complicated, require fair amount computer programming and often need suitable prior guessing of range of output parameters for adjustment of constant values in the models. This poses some challenges for use and having an intuitive guidance, for a beginner/unskilled operator. Besides, their complexity may erect barrier in the way of their implementation for real time monitoring and control. This work proposes a simple linear regression model, formed based on energy balance approach, to estimate dilution in laser cladding. After fitting to a set of data, within a suitable process parameter-window, for a particular clad-substrate material combination, this model can estimate dilution as a function of input/easily measureable parameters, viz. laser power, scan speed, clad width and clad height. The model fitted well to the experimental data taken from literature

On-machine tool prediction of flank wear from machined surface images using texture analyses and support vector regression

In this paper, a method for on-machine tool condition monitoring by processing the turned surface images has been proposed. Progressive monitoring of cutting tool condition is inevitable to maintain product quality. Thus, image texture analyses using gray level co-occurrence matrix, Voronoi tessellation and discrete wavelet transform based methods have been applied on turned surface images for extracting eight useful features to describe progressive tool flank wear. Prediction of cutting tool flank wear has also been performed using these eight features as predictors by utilizing linear support vector machine based regression technique with a maximum 4.9% prediction error

Tool Condition Monitoring in Turning by Applying Machine Vision

In this paper, a method for predicting progressive tool flank wear using extracted features from turned surface images has been proposed. Acquired turned surface images are analyzed by using texture analyses, viz., gray level co-occurrence matrix (GLCM), Voronoi tessellation (VT), and discrete wavelet transform (DWT) based methods to obtain information about waviness, feed marks, and roughness from machined surface images for describing tool flank wear. Two features from each texture analyses are extracted and fed into support vector machine (SVM) based regression models for predicting progressive tool flank wear. Mean correlation coefficient between the measured and predicted tool flank wear is found as 0.991

Progressive tool condition monitoring of end milling from machined surface images

Indirect tool condition monitoring in end milling is inevitable to produce high-quality finished products due to the complexity of end-milling process. Among the various indirect tool condition monitoring techniques, monitoring based on image processing by analyzing the surface images of final product is gaining high importance due to its non-tactile and flexible nature. The advances in computing facilities, texture analysis techniques and learning machines make these techniques feasible for progressive tool flank wear monitoring. In this article, captured end-milled surface images are analyzed using gray level co-occurrence matrix–based and discrete wavelet transform–based texture analyses to extract features which have a good correlation with progressive tool flank wear. Contrast and second diagonal moment are extracted from gray level co-occurrence matrix and root mean square and energy are extracted from discrete wavelet decomposition of end-milled surface images as features. Finally, these four features are utilized to build support vector machine–based regression models for predicting progressive tool flank wear with 94.8% average correlation between predicted and measured tool flank wear values

Progressive tool flank wear monitoring by applying discrete wavelet transform on turned surface images

In this paper, a method for on-machine tool progressive monitoring of tool flank wear by processing the turned surface images in micro-scale has been proposed. Micro-scale analysis of turned surface has been performed by using discrete wavelet transform. A novel methodology for proper selection of mother wavelets and its decomposition level dependent on the feed rate parameter has also been shown in this research. The selected mother wavelets are utilized to decompose the turned surface images at the chosen decomposition level and two features, namely, GRMS and Energy are extracted as the highly repeatable descriptors of tool flank wear. An exponential correlation of GRMS and Energy values with progressive tool flank wear are found with average coefficient of determination values as 0.953 and 0.957, respectively

Evaluation of primary phase morphology of cooling slope cast Al-Si-Mg alloy samples using image texture analysis

Rheopressure die casting is one of the newest casting processes of present era for manufacturing of near-net shaped cast components with improved mechanical properties and high dimensional accuracy. Rheopressure die casting demands especially prepared semi-solid alloy slurry having nearly globular primary Al phase. In this study, a cooling slope has been employed to produce semi-solid slurry of Al-Si-Mg (A356) alloy and successively cast in a metallic mould. Image texture analysis techniques have been implemented for accurate evaluation of the primary phase morphology of cast samples. In this research, efforts have been made to apply fractal analysis and run-length statistical analysis techniques for automatic characterization of optical micrographs of cast samples produced at different processing conditions

Automatic estimation of mechanical properties from fractographs using optimal anisotropic diffusion and Voronoi tessellation

With the advent of materials informatics there is a high demand of establishing automatic structure-property correlationship of materials which is a popular research topics due to the advancement of image processing and pattern recognition algorithms. Therefore, in this work, an attempt is made to estimate mechanical properties (i.e. yield strength and ductility) of AISI 304LN stainless steel from the SEM images of fracture surfaces obtained from tensile tests carried out at different strain rates using image processing techniques. As the void morphologies of fracture surfaces change systematically with the change in strain rates, the automatic detection of voids and geometrical features estimation from detected voids from the obtained are key goals of this present study. Therefore, in this work, a novel method of optimal anisotropic diffusion technique along with contrast limited adaptive histogram equalization (CLAHE), Otsu's optimal thresholding and morphological thinning operation are applied over the fractographs for edge enhancement, overcoming inhomogeneous illumination, edge segmentation and thinning, respectively, to detect voids, automatically. Then, Voronoi tessellation technique, which is a geometrical texture analysis, is utilized on these edge images of fractographs to extract four features viz. Voronoi edges, mean area, mean elongation and mean perimeter of Voronoi polygons where the linear correlation values (R2) with mechanical properties are found in the range of 0.90–0.99. A high linear correlation of features (i.e. 0.97–0.99) with the ductility is noticed as ductility is a geometrical parameter measured during fracture

Comparison between three tuning methods of PID control for high precision positioning stage

Advances in micro and nano metrology are inevitable to satisfy the need to maintain product quality of miniaturized components by the utilization of well controlled positioning stage. Proportional, integral and derivative (PID) control has been proved to have most robust and simpler performance. However, tuning of the key parameters of a PID controller is most inevitable to build a robust controller to accomplish high precision positioning performance. Therefore, many tuning methods are proposed for PID controllers. In this work, three tuning methods, namely, Ziegler–Nichols step response method, Chien–Hrones–Reswick method and Cohen–Coon method are compared for PID control of a single axis of a XY stage of a 3D surface profiler. Positional errors are also measured using a miniature plane mirror interferometer. Cohen–Coon method is found to be the best technique to minimize the controller error