The “Right” recipes for security culture: a competing values model perspective

This study argues that the effect of perceived organizational culture on the formation of security-related subjective norms and the level of compliance pressure will vary based on how the employees perceive their organization’s cultural values. These perceptions reflect on the assumptions and principles that organizations use to guide their security-related behaviors. To make these arguments, we adopt the competing values model (CVM), which is a model used to understand the range of organizational values and resulting cultural archetypes

Construction of Multi-Dimensional Functions for Optimization of Additive-Manufacturing Process Parameters

The authors present a generic framework for parameter optimization of additive manufacturing (AM) processes, one tailored to a high-throughput experimental methodology (HTEM). Given the large number of parameters, which impact the quality of AM-metallic components, the authors advocate for partitioning the AM parameter set into stages (tiers), based on their relative importance, modeling one tier at a time until successful, and then systematically expanding the framework. The authors demonstrate how the construction of multi-dimensional functions, based on neural networks (NN), can be applied to successfully model relative densities and Rockwell hardness obtained from HTEM testing of the Inconel 718 superalloy fabricated, using a powder-bed approach. The authors analyze the input data set, assess its suitability for predictions, and show how to optimize the framework for the multi-dimensional functional construction, such as to obtain the highest degree of fit with the input data. The novelty of the research work entails the versatile and scalable NN framework presented, suitable for use in conjunction with HTEM, for the AM parameter optimization of superalloys, and beyond.Comment: Submitted to the Journal of Additive Manufacturing on November 10, 202

Reconstructing Cell Complexes From Cross-sections

Abstract. Many interesting segmentations take the form of cell complexes. We present a method to infer a 3D cell complex from of a series of 2D cross-sections. We restrict our attention to the class of complexes whose duals resemble triangulations. This class includes microstructures of polycrystalline materials, as well as other cellular structures found in nature. Given a prescribed matching of 2D cells in adjacent cross-sections we produce a 3D complex spanning these sections such that matched 2-cells are contained in the interior of the same 3-cell. The reconstruction method considers only the topological structure of the input. After an initial 3D complex is recovered, the structure is altered to accommodate geometric properties of the dataset. We evaluate the method using ideal, synthetic datasets as well as serial-sectioned micrographs from a sample of tantalum metal

Multi-principal element alloy discovery using directed energy deposition and machine learning

Multi-principal element alloys open large composition spaces for alloy development. The large compositional space necessitates rapid synthesis and characterization to identify promising materials, as well as predictive strategies for alloy design. Additive manufacturing via directed energy deposition is demonstrated as a high-throughput technique for synthesizing alloys in the Cr-Fe-Mn-Ni quaternary system. More than 100 compositions are synthesized in a week, exploring a broad range of compositional space. Uniform compositional control to within +/-5 at% is achievable. The rapid synthesis is combined with conjoint sample heat treatment (25 samples vs 1 sample), and automated characterization including X-ray diffraction, energy-dispersive X-ray spectroscopy, and nano-hardness measurements. The datasets of measured properties are then used for a predictive strengthening model using an active machine learning algorithm that balances exploitation and exploration. A learned parameter that represents lattice distortion is trained using the alloy compositions. This combination of rapid synthesis, characterization, and active learning model results in new alloys that are significantly stronger than previous investigated alloys

Machine Learning Prediction of Critical Cooling Rate for Metallic Glasses From Expanded Datasets and Elemental Features

We use a random forest model to predict the critical cooling rate (RC) for glass formation of various alloys from features of their constituent elements. The random forest model was trained on a database that integrates multiple sources of direct and indirect RC data for metallic glasses to expand the directly measured RC database of less than 100 values to a training set of over 2,000 values. The model error on 5-fold cross validation is 0.66 orders of magnitude in K/s. The error on leave out one group cross validation on alloy system groups is 0.59 log units in K/s when the target alloy constituents appear more than 500 times in training data. Using this model, we make predictions for the set of compositions with melt-spun glasses in the database, and for the full set of quaternary alloys that have constituents which appear more than 500 times in training data. These predictions identify a number of potential new bulk metallic glass (BMG) systems for future study, but the model is most useful for identification of alloy systems likely to contain good glass formers, rather than detailed discovery of bulk glass composition regions within known glassy systems

Numerical Unsaturated Flow Model of Railway Drainage Systems

Substandard drainage assets are considered to be a major cause of flooding, earthwork failures, and deficient track geometry. Considering the deterioration of track materials due to cyclic loads and tamping forces, the impact of more frequent extreme rainfall events is likely to lead towards higher rates of hydraulic overloads in the drainage system, earthwork failures, and service disruptions. Therefore, the development of a numerical model could be able to describe the ageing track bed materials and provide an alternative tool for the simulation of the flow through the porous media used in the construction of railway tracks. In this paper the model HYDRUS is tested to simulate the drainage of trackbed materials under laboratory controlled conditions prior its application on actual railway drainage case studies

Unanticipated results in the uranium niobium alloy system

The uranium niobium binary alloy system exhibits a rich collection of phenomena for study. The composition range from 0 wt.% Nb to 10 wt.% Nb exhibits multiple crystallographic phases with interesting properties such as superconductivity, charge density waves and shape memory effects. We have measured the resistivity and heat capacity as a function of temperature from 2 to 325K in the above composition range in an effort to map out the phase boundaries of interest. Surprisingly the temperature dependence of the resistivity transitions from metallic (decreasing with decreasing temperature) to nonmetallic (increasing with decreasing temperature). It is not clear if the nonmetallic resistivity is caused by strongly correlated electronic effects or is the result of some other effect such as disorder driven scattering

Magnetism and superconductivity of uranium and intermetallic compounds

Heat capacity, resistivity, and phonon density of states have been measured on uranium and reported already. Many of the results are on single crystals of purity that has been unavailable before. Some intermetallic compounds have been measured that are in the class of so-called heavy-fermion materials. We present here the latest results along with a discussion of the occurrence of superconductivity or magnetism in these materials