Predictive process mapping for laser powder bed fusion: A review of existing analytical solutions

One of the main challenges in the laser powder bed fusion (LPBF) process is making dense and defect-free components. These porosity defects are dependent upon the melt pool geometry and the processing conditions. Power-velocity (PV) processing maps can aid in visualizing the effects of LPBF processing variables and mapping different defect regimes such as lack-of-fusion, under-melting, balling, and keyholing. This work presents an assessment of existing analytical equations and models that provide an estimate of the melt pool geometry as a function of material properties. The melt pool equations are then combined with defect criteria to provide a quick approximation of the PV processing maps for a variety of materials. Finally, the predictions of these processing maps are compared with experimental data from the literature. The predictive processing maps can be computed quickly and can be coupled with dimensionless numbers and high-throughput (HT) experiments for validation. The present work provides a boundary framework for designing the optimal processing parameters for new metals and alloys based on existing analytical solutions.     

Crowd counting in complex scenes based on an attention aware CNN network

Crowd counting with density estimation has been an active research community due to its significant applications in the fields of public security, video surveillance, traffic monitoring. However, Crowd counting for congested scenes often suffers from some obstacles including severe occlusions, large scale variations, noise interference, etc. In this paper, using the first ten layers of a modified VGG16 and dilated convolution layers as the framework, we have proposed a CNN based crowd counting and density estimation model improved by the attention aware modules with residual connections. To tackle the problem of noise interference, convolutional block attention modules have been introduced into the deep network to segment the foreground and background to focus on interest information, refining deeper features of the input image. To improve information transmission and reuse, residual connections are utilized to link 3 attention blocks. Meanwhile, dilated convolution layers keep larger reception fields and obtain high-resolution density maps. The proposed method has been evaluated on three public benchmarks, i.e. Shanghai Tech A & B, UCF-QNRF and MALL, achieving the mean absolute errors of 64.6 & 8.3, 113.8 and 1.68, respectively. The results outperform some existing excellent approaches. This indicates that the proposed model has high accuracy and better robustness, which is suitable for crowd counting and density estimation in various congested scenes.     

Review on development of metal/ceramic interpenetrating phase composites and critical analysis of their properties

Metal/ceramic composites are in high demand in several industries because of their superior thermo-mechanical properties. Among various composite types, the interpenetrating phase composites (IPCs) with percolating metallic and ceramic phases offer manifold benefits, such as a good combination of strength, toughness, and stiffness, very good thermal properties, excellent wear resistance, as well as the flexibility of microstructure and processing route selection, etc. The fabrication of metal/ceramic IPCs typically involves two steps - i) processing of an open porous ceramic body, and ii) infiltration of metallic melt in the pores to fabricate the IPC. Although significant progress has been made in recent years for developing both porous ceramics and melt infiltration methods, to the best of the knowledge of the authors, no review article summarizing all the aspects of processing and properties of IPCs has been published till date. This review article is aimed at filling this gap. Starting with a brief introduction about the current status and applications of IPCs, the various processing routes for fabricating open porous ceramic preforms and melt infiltration techniques have been discussed. Subsequently, the data available for various important physical, mechanical, and thermal properties for IPCs have been critically analyzed to thoroughly understand their dependence on various structural and processing parameters. To compare the properties of IPCs with other relevant materials, seven different Ashby material property maps have been used, and the domains for IPCs have been created in them. For each map, the concept of material indices has been employed to critically discuss how IPCs perform in relation to other material classes for various optimum design conditions. Finally, a detailed future outlook for further research on IPCs has been provided.     

Automated classification of simulated wind field patterns from multiphysics ensemble forecasts

In this study, we have proposed an automated classification approach to identify meaningful patterns in wind field data. Utilizing an extensive simulated wind database, we have demonstrated that the proposed approach can identify low‐level jets, near‐uniform profiles, and other patterns in a reliable manner. We have studied the dependence of these wind profile patterns on locations (eg, offshore vs onshore), seasons, and diurnal cycles. Furthermore, we have found that the probability distributions of some of the patterns depend on the underlying planetary boundary layer schemes in a significant way. The future potential of the proposed approach in wind resource assessment and, more generally, in mesoscale model parameterization improvement is touched upon in this paper.     

Experimental study and simulation of a three-phase flow stirred bioreactor

In order to obtain the reasonable operating conditions and minimize the power consumption in the stirred bioreactor, the hydrodynamic experiments in the stirred bioreactor have been taken to obtain the basic data. Subsequently, an Eulerian model for the gas–liquid–solid three phase flow in the stirred bioreactor has been proposed and the CFD simulation has been conducted. By comparing the results of experiment and simulation, it can be concluded that the simulation results were consistent with the experimental data. The inner relationship between operating variables and indicators could be obtained by comparing the results of just suspension speed, gas holdup, power consumption and operational maps, further the reasonable operating conditions could be also determined under the minimum power consumption. The operational maps could provide the theoretical foundation for industrial application of the gas–liquid–solid stirred bioreactors under the low solid concentration(no more than 20 wt%).     

Study on the influence of maintenance operations on energy consumption and emissions in healthcare centres by fuzzy cognitive maps

The present work is aimed at performing a qualitative analysis of the effect of maintenance intensity on energy consumption, energy costs and emissions in healthcare centres. Most relevant variables involved in the maximization of the building efficiency were determined by means of fuzzy cognitive maps. Twelve variables were observed to show a direct connection to the energy and environmental efficiency as well as to its maintenance condition. The joint effect of these factors was seen to improve the overall performance of the building in terms of efficiency. However, even though maintenance appeared as one of the most highly-rated variables, it did not seem to affect the overall performance by itself, but it was rather due to the synergistic action of the remaining variables. The behaviour of those variables was evidenced to show a significant change for buildings below 1250?m² floor area as compared to those exceeding such threshold value.     

A review on some classes of algebraic systems

ABSTRACT

In this paper, we review some algebraic control system. Precisely, linear and bilinear systems on Euclidean spaces and invariant and linear systems on Lie groups. The fourth classes of systems have a common issue: to any class, there exists an associated subgroup. From this object, we survey the controllability property. Especially, from those coming from our contribution to the theory.     

Managing readiness-relevant activities for the organizational dimension of ERP implementation

An organization requires performing readiness-relevant activities to ensure successful implementation of an enterprise resource planning (ERP) system. This paper develops a novel approach to managing these interrelated activities to get ready for implementing an ERP system. The approach enables an organization to evaluate its ERP implementation readiness by assessing the degree to which it can achieve the interrelated readiness relevant activities using fuzzy cognitive maps. Based on the interrelationship degrees among the activities, the approach clusters the activities into manageable groups and prioritizes them. To help work out a readiness improvement plan, scenario analysis is conducted.     

A GIS plug-in for Bayesian belief networks: Towards a transparent software framework to assess and visualise uncertainties in ecosystem service mapping

The complexity and spatial heterogeneity of ecosystem processes driving ecosystem service delivery require spatially explicit models that take into account the different parameters affecting those processes. Current attempts to model ecosystem service delivery on a broad, regional scale often depend on indicator-based approaches that are generally not able to fully capture the complexity of ecosystem processes. Moreover, they do not allow quantification of uncertainty on their predictions. In this paper, we discuss a QGIS plug-in which promotes the use of Bayesian belief networks for regional modelling and mapping of ecosystem service delivery and associated uncertainties. Different types of specific Bayesian belief network output maps, delivered by the plug-in, are discussed and their decision support capacities are evaluated. This plug-in, used in combination with firmly developed Bayesian belief networks, has the potential to add value to current spatial ecosystem service accounting methods. The plug-in can also be used in other research domains dealing with spatial data and uncertainty.