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Selective electron beam melting (SEBM) is one of the popular powder-bed additive manufacturing (AM) technologies. The purpose of this paper is to develop a simulation strategy for SEBM process to get data which are vital for realistic failure prediction and process parameters control for real complex components.

Focusing on the SEBM process of tantalum, this paper presents a three-dimensional thermo-mechanical modeling strategy based on ABAQUS and its subroutines. The simulation strategy used in this paper is developed for SEBM process of pure tantalum but could be extended to other AM fabrication technologies and other metals without difficulties.

The simulation of multi-track multi-layer SEBM process of tantalum was carried out to predict the temperature field, the molten pool evolution and the residual stress distribution. The key information such as inter-track molten pool overlapping ratio and inter-layer refusion state can be extracted from the obtained molten pool morphologies, which are vital for realistic failure prediction and process parameters control for real components. The authors finally demonstrate the capability of the strategy used by simulating a 2 mm × 2 mm × 10 mm lattice structure with total 200 layers.

The simulation of multi-track multi-layer SEBM process of tantalum was carried out. The key information such as inter-track molten pool overlapping ratio and inter-layer refusion state can be extracted. The authors finally demonstrate the capability of the strategy used by simulating a lattice structure. Not only temperature distribution but also stress evolution are captured. Our simulation strategy is developed for the SEBM process of pure tantalum, but it could be extended to other AM fabrication technologies and other metals without difficulties.

Depending on an experimental approach to find optimal parameters for producing fully dense (relative density > 99%) Inconel 718 (IN718) components in the selective laser melting (SLM) process is expensive and offers no guarantee of success. Accordingly, this study aims to propose a multi-scale simulation framework to guide the choice of processing parameters in a more pragmatic manner.

In the proposed approach, a powder layer, ray tracing and heat transfer simulation models are used to calculate the melt pool dimensions and evaporation volume corresponding to a small number of laser power and scanning speed conditions within the input design space. A layer-heating model is then used to determine the inter-layer idle time required to maximize the temperature convergence rate of the solidified layer beneath the power bed. The simulation results are used to train surrogate models to construct SLM process maps for 3,600 pairs of the laser power and scanning speed within the input design space given three different values of the underlying solidified layer temperature (i.e., 353 K, 673 K and 873 K). The ideal selection of laser power and scanning speed of each process map is chosen based on four quality-related criteria listed as follows: without the appearance of key-hole melting; an evaporation volume less than the volume of the d90 powder particles; ensuring the stability of single scan tracks; and avoiding a weak contact between the melt pool and substrate. Finally, the optimal laser power and scanning speed parameters for the SLM process are determined by superimposing the optimal regions of the individual process maps.

The feasibility of the proposed approach is demonstrated by fabricating IN718 test specimens using the optimal processing conditions identified by the simulation framework. It is shown that the maximum density of the fabricated parts is 99.94%, while the average density is 99.88% and the standard deviation is less than 0.05%.

The present study proposed a multi-scale simulation model which can efficiently predict the optimal processing conditions for producing fully dense components in the SLM process. If the geometry of the three-dimensional printed part is changed or the machine and powder material is altered, users can use the proposed method for predicting the processing conditions that can produce the high-density part.

This study aims to provide a comprehensive overview of the current state of the art in powder bed fusion (PBF) techniques for additive manufacturing of multiple materials. It reviews the emerging technologies in PBF multimaterial printing and summarizes the latest simulation approaches for modeling them. The topic of “multimaterial PBF techniques” is still very new, undeveloped, and of interest to academia and industry on many levels.

This is a review paper. The study approach was to carefully search for and investigate notable works and peer-reviewed publications concerning multimaterial three-dimensional printing using PBF techniques. The current methodologies, as well as their advantages and disadvantages, are cross-compared through a systematic review.

The results show that the development of multimaterial PBF techniques is still in its infancy as many fundamental “research” questions have yet to be addressed before production. Experimentation has many limitations and is costly; therefore, modeling and simulation can be very helpful and is, of course, possible; however, it is heavily dependent on the material data and computational power, so it needs further development in future studies.

This work investigates the multimaterial PBF techniques and discusses the novel printing methods with practical examples. Our literature survey revealed that the number of accounts on the predictive modeling of stresses and optimizing laser scan strategies in multimaterial PBF is low with a (very) limited range of applications. To facilitate future developments in this direction, the key information of the simulation efforts and the state-of-the-art computational models of multimaterial PBF are provided.

Due to the increasing size and complexity of construction projects, construction engineering and management involves the coordination of many complex and dynamic processes and relies on the analysis of uncertain, imprecise and incomplete information, including subjective and linguistically expressed information. Various modelling and computing techniques have been used by construction researchers and applied to practical construction problems in order to overcome these challenges, including fuzzy hybrid techniques. Fuzzy hybrid techniques combine the human-like reasoning capabilities of fuzzy logic with the capabilities of other techniques, such as optimization, machine learning, multi-criteria decision-making (MCDM) and simulation, to capitalise on their strengths and overcome their limitations. Based on a review of construction literature, this chapter identifies the most common types of fuzzy hybrid techniques applied to construction problems and reviews selected papers in each category of fuzzy hybrid technique to illustrate their capabilities for addressing construction challenges. Finally, this chapter discusses areas for future development of fuzzy hybrid techniques that will increase their capabilities for solving construction-related problems. The contributions of this chapter are threefold: (1) the limitations of some standard techniques for solving construction problems are discussed, as are the ways that fuzzy methods have been hybridized with these techniques in order to address their limitations; (2) a review of existing applications of fuzzy hybrid techniques in construction is provided in order to illustrate the capabilities of these techniques for solving a variety of construction problems and (3) potential improvements in each category of fuzzy hybrid technique in construction are provided, as areas for future research.

This study proposes targeted modernization of the Department of Defense (DoD's) Joint Forces Ammunition Logistics information system by implementing the optimized innovative information technology open architecture design and integrating Radio Frequency Identification Device data technologies and real-time optimization and control mechanisms as the critical technology components of the solution. The innovative information technology, which pursues the focused logistics, will be deployed in 36 months at the estimated cost of $568 million in constant dollars. We estimate that the Systems, Applications, Products (SAP)-based enterprise integration solution that the Army currently pursues will cost another $1.5 billion through the year 2014; however, it is unlikely to deliver the intended technical capabilities.

This paper describes a practical approach to implement computational steering for problem solving environments (PSEs) by using WBCSim as an example. WBCSim is a Web based simulation system designed to increase the productivity of wood scientists conducting research on wood‐based composites manufacturing processes. WBCSim serves as a prototypical example for the design, construction, and evaluation of small‐scale PSEs.

Various changes have been made to support computational steering across the three layers – client, server, developer – comprising the WBCSim system. A detailed description of the WBCSim system architecture is presented, along with a typical scenario of computational steering usage.

The set of changes and components are: design and add a very simple steering module at the legacy simulation code level, provide a way to monitor simulation execution (alert users when it is time to steer), add an interface to access and visualize simulation results, and perhaps to compare intermediate results across multiple steering attempts. These simple changes and components have a relatively low cost in terms of increasing software complexity.

The novelty lies in designing and implementing a practical approach to enable computational steering capability for PSEs embedded with legacy simulation code.

This study presents finite element (FE) and generalized regression neural network (GRNN) approaches for modeling multiple crack growth problems and predicting crack-growth directions under the influence of multiple crack parameters.

To determine the crack-growth direction in aluminum specimens, multiple crack parameters representing some degree of crack propagation complexity, including crack length, inclination angle, offset and distance, were examined. FE method models were developed for multiple crack growth simulations. To capture the complex relationships among multiple crack-growth variables, GRNN models were developed as nonlinear regression models. Six input variables and one output variable comprising 65 training and 20 test datasets were established.

The FE model could conveniently simulate the crack-growth directions. However, several multiple crack parameters could affect the simulation accuracy. The GRNN offers a reliable method for modeling the growth of multiple cracks. Using 76% of the total dataset, the NN model attained an R2 value of 0.985.

The models are presented for static multiple crack growth problems. No material anisotropy is observed.

In practical crack-growth analyses, the NN approach provides significant benefits and savings.

The proposed GRNN model is simple to develop and accurate. Its performance was superior to that of other NN models. This model is also suitable for modeling multiple crack growths with arbitrary geometries. The proposed GRNN model demonstrates its prediction capability with a simpler learning process, thus producing efficient multiple crack growth predictions and assessments.

Briefly reviews previous literature by the author before presenting an original 12 step system integration protocol designed to ensure the success of companies or countries in their efforts to develop and market new products. Looks at the issues from different strategic levels such as corporate, international, military and economic. Presents 31 case studies, including the success of Japan in microchips to the failure of Xerox to sell its invention of the Alto personal computer 3 years before Apple: from the success in DNA and Superconductor research to the success of Sunbeam in inventing and marketing food processors: and from the daring invention and production of atomic energy for survival to the successes of sewing machine inventor Howe in co‐operating on patents to compete in markets. Includes 306 questions and answers in order to qualify concepts introduced.

The purpose of this paper is to analyze a signal propagation in highly metalized environments, which has not been extensively studied in the literature. Having in mind a large number of such applications, better understanding and possibly finding a way of improving communication in these conditions would be highly beneficial.

The analysis is performed in a simulation environment developed by the authors, based on finite difference time domain (FDTD) method, to identify effects that have decisive influence on electromagnetic (EM) wave propagation in the aforementioned conditions. The analysis of the EM field at the reception is modified so that a multiple-field sampling is performed, and maximal values are further used. In practical realizations, this procedure could be implemented by using multiple antennas and selective combining at the reception.

Results show that the existence of metal objects (in the observed case, these are railway tracks), in combination with the appropriate choice of antenna parameters, can be favorably used to improve signal reception. The contribution is manifested through the reduction of the pathloss.

In the performed analysis, one should be aware of the limitations that the FDTD method brings. Those limitations are related to the size of the computational domain and discretization mesh refinement (possibility of modeling geometry in fine details).

Originality of this paper consists of the introduced modification in the analysis of signal propagation in heavily metalized environment. Namely, a multiple-field sampling in the reception zone (in one plane, dimensions λ × λ = 12.5 cm X 12.5 cm) is performed using several probes in simulation environment. In this way, a qualitative analysis is performed more efficiently, which made it is possible to distinguish and identify different propagation mechanisms.

Contemporary literature reveals that, to date, the poultry livestock sector has not received sufficient research attention. This particular industry suffers from unstructured supply chain practices, lack of awareness of the implications of the sustainability concept and failure to recycle poultry wastes. The current research thus attempts to develop an integrated supply chain model in the context of poultry industry in Bangladesh. The study considers both sustainability and supply chain issues in order to incorporate them in the poultry supply chain. By placing the forward and reverse supply chains in a single framework, existing problems can be resolved to gain economic, social and environmental benefits, which will be more sustainable than the present practices.

The theoretical underpinning of this research is ‘sustainability’ and the ‘supply chain processes’ in order to examine possible improvements in the poultry production process along with waste management. The research adopts the positivist paradigm and ‘design science’ methods with the support of system dynamics (SD) and the case study methods. Initially, a mental model is developed followed by the causal loop diagram based on in-depth interviews, focus group discussions and observation techniques. The causal model helps to understand the linkages between the associated variables for each issue. Finally, the causal loop diagram is transformed into a stock and flow (quantitative) model, which is a prerequisite for SD-based simulation modelling. A decision support system (DSS) is then developed to analyse the complex decision-making process along the supply chains.

The findings reveal that integration of the supply chain can bring economic, social and environmental sustainability along with a structured production process. It is also observed that the poultry industry can apply the model outcomes in the real-life practices with minor adjustments. This present research has both theoretical and practical implications. The proposed model’s unique characteristics in mitigating the existing problems are supported by the sustainability and supply chain theories. As for practical implications, the poultry industry in Bangladesh can follow the proposed supply chain structure (as par the research model) and test various policies via simulation prior to its application. Positive outcomes of the simulation study may provide enough confidence to implement the desired changes within the industry and their supply chain networks.