Search results

The aim of this article is to demonstrate the development of environment friendly, low cost natural fibre composites by robust engineering approach. More specifically, the prime objective of the study is to optimise the composition of natural fibre reinforced polymer nanocomposites using a robust statistical approach.

In this research, the material is prepared using multi-walled carbon nanotubes (MWCNT), Cantala fibres and Epoxy Resin in accordance with the ASTM (American Society for Testing and Materials) standards. Further, the composition is prepared and optimised using the mixture-design approach for the flexural strength of the material.

The results of the study indicate that MWCNT plays a vital role in increasing the flexural strength of the composite. Moreover, it is observed that interactions between second order and third order parameters in the composition are statistically significant. This leads to proposing a special cubic model for the novel composite material with residual analysis. Moreover, the methodology assists in optimising the mixture component values to maximise the flexural strength of the novel composite material.

This article attempts to include both MWCNT and Cantala fibres to develop a novel composite material. In addition, it employs the mixture-design technique to optimise the composition and predict the model of the study in a step-by-step manner, which will act as a guideline for academicians and practitioners to optimise the material composition with specific reference to natural fibre reinforced nanocomposites.

This paper aims to provide a comprehensive review of the state-of–the-art design methods for additive manufacturing (AM) technologies to improve functional performance.

In this survey, design methods for AM to improve functional performance are divided into two main groups. They are design methods for a specific objective and general design methods. Design methods in the first group primarily focus on the improvement of functional performance, while the second group also takes other important factors such as manufacturability and cost into consideration with a more general framework. Design methods in each groups are carefully reviewed with discussion and comparison.

The advantages and disadvantages of different design methods for AM are discussed in this paper. Some general issues of existing methods are summarized below: most existing design methods only focus on a single design scale with a single function; few product-level design methods are available for both products’ functionality and assembly; and some existing design methods are hard to implement for the lack of suitable computer-aided design software.

This study is a useful source for designers to select an appropriate design method to take full advantage of AM.

In this survey, a novel classification method is used to categorize existing design methods for AM. Based on this classification method, a comprehensive review is provided in this paper as an informative source for designers and researchers working in this field.

By introducing the mass customization service mode into the cloud logistics environment, this paper studies the joint optimization of service provider selection and customer order decoupling point (CODP) positioning based on the mass customization service mode to provide customers with more diversified and personalized service content with lower total logistics service cost.

This paper addresses the general process of service composition optimization based on the mass customization mode in a cloud logistics service environment and constructs a joint decision model for service provider selection and CODP positioning. In the model, the two objective functions of minimum service cost and most satisfactory delivery time are considered, and the Pareto optimal solution of the model is obtained via the NSGA-II algorithm. Then, a numerical case is used to verify the superiority of the service composition scheme based on the mass customization mode over the general scheme and to verify the significant impact of the scale effect coefficient on the optimal CODP location.

(1) Under the cloud logistics mode, the implementation of the logistics service mode based on mass customization can not only reduce the total cost of logistics services by means of the scale effect of massive orders on the cloud platform but also make more efficient use of a large number of logistics service providers gathered on the cloud platform to provide customers with more customized and diversified service content. (2) The scale effect coefficient directly affects the total cost of logistics services and significantly affects the location of the CODP. Therefore, before implementing the mass customization logistics service mode, the most reasonable clustering of orders on the cloud logistics platform is very important for the follow-up service combination.

The originality of this paper includes two aspects. One is to introduce the mass customization mode in the cloud logistics service environment for the first time and summarize the operation process of implementing the mass customization mode in the cloud logistics environment. Second, in order to solve the joint decision optimization model of provider selection and CODP positioning, this paper designs a method for solving a mixed-integer nonlinear programming model using a multi-layer coding genetic algorithm.

User requests over the cloud are not achievable with one single service, multiple services need to be executed to fulfill what a user asks for. Typically, such services are composed and presented as one global service. Moreover, the same operation can be achieved by multiple services available at different clouds, which can result in different possibilities in composing them. This paper aims to decrease the number of clouds involved in the composition process, so that user requests are satisfied with minimal cost (communication costs, execution time and financial charges).

This paper investigates the use of an intelligent water drops (IWDs) optimization-based algorithm, and an integer linear programming model to optimize the number of cloud bases involved in the composition process. A comparison of the solutions found by these two techniques is presented in the paper.

The obtained results show that the number of cloud bases can be decreased without affecting user satisfaction.

The paper is a first attempt to use the IWDs algorithm for service composition, tested with big-size data.

An electrochemical method based on the open circuit potential (OCP) fluctuations was put forward. It can be used to optimize the alloy compositions for improving the corrosion resistance of rust layer.

The potential trends and potential fluctuations of carbon steels in seawater were separated by Hodrick–Prescott filter. The Spearman correlation coefficient and max information coefficient were used to explore the correlation of alloy compositions and potential fluctuations.

After long-term immersion, potential fluctuation resistance (PFR) can be used to characterize the corrosion resistance of metals and its rust layers. In the 1,500 to 2,500 h exposure period, Fe, C and S compositions have strong negative correlations, whereas PFR and P composition have weak negative correlations. Mn, Cu and Ti alloy compositions help the rust layer of carbon steels have higher PFRs. These elements that exhibit higher PFRs in this period have been confirmed to have the effect on improving the corrosion resistance of rust layer.

A new computing method for alloy composition optimization of carbon steels based on the OCP fluctuations was put forward. This method combines electrochemical monitoring with the long-term actual seawater environmental tests of various carbon steels.

This paper aims to solve the web service selection problem using an efficient meta-heuristic algorithm. The problem of selecting a set of web services from a large-scale service environment (web service repository) while maintaining Quality-of-Service (QoS), is referred to as web service selection (WSS). With the explosive growth of internet services, managing and selecting the proper services (or say web service) has become a pertinent research issue.

In this paper, to address WSS problem, the authors propose a new modified fruit fly optimization approach, called orthogonal array-based learning in fruit fly optimizer (OL-FOA). In OL-FOA, they adopt a chaotic map to initialize the population; they add the adaptive DE/best/2mutation operator to improve the exploration capability of the fruit fly approach; and finally, to improve the efficiency of the search process (by reducing the search space), the authors use the orthogonal learning mechanism.

To test the efficiency of the proposed approach, a test suite of 2500 web services is chosen from the public repository. To establish the competitiveness of the proposed approach, it compared against four other meta-heuristic approaches (including classical as well as state-of-the-art), namely, fruit fly optimization (FOA), differential evolution (DE), modified artificial bee colony algorithm (mABC) and global-best ABC (GABC). The empirical results show that the proposed approach outperforms its counterparts in terms of response time, latency, availability and reliability.

In this paper, the authors have developed a population-based novel approach (OL-FOA) for the QoS aware web services selection (WSS). To justify the results, the authors compared against four other meta-heuristic approaches (including classical as well as state-of-the-art), namely, fruit fly optimization (FOA), differential evolution (DE), modified artificial bee colony algorithm (mABC) and global-best ABC (GABC) over the four QoS parameter response time, latency, availability and reliability. The authors found that the approach outperforms overall competitive approaches. To satisfy all objective simultaneously, the authors would like to extend this approach in the frame of multi-objective WSS optimization problem. Further, this is declared that this paper is not submitted to any other journal or under review.

The purpose of this paper is to propose a novel quality of service (QoS)‐aware service composition approach, called SEQOIA, capable of defining at run‐time a service composition plan meeting both functional and non‐functional constraints and optimizing the overall quality of service.

SEQOIA is a semantic‐driven QoS‐aware dynamic composition approach leveraging on an integer linear programming technique (ILP). It exploits the expressiveness of an ontology‐based service profile model handling structural and semantic properties of service descriptions. It represents the service composition problem as a set of functional and non‐functional constraints and an objective function.

The authors developed a proof of concept implementing SEQOIA, as well as an alternative composition solution based on state‐of‐the‐art AI planning and ILP techniques. Results of testing activities show that SEQOIA performs better than the alternative solution over a limited set of candidate services. This behaviour was expected, as SEQOIA guarantees to find the service composition providing the optimal QoS value, while the alternative approach does not provide this guarantee, as it handles separately the specification of the functional service composition flow and the QoS‐based service selection step.

SEQOIA leverages on semantic annotations in order to make service composition feasible by coping with syntactic and structural differences typically existing across different, even similar, service implementations. To ease the adoption of SEQOIA in real enterprise scenarios, the authors chose to leverage on an XML‐based message model of services interfaces (including but not strictly requiring the use of WSDL).

This paper aims to study the effects of inorganic CaCO₃ nanoadditives in the polylactic acid (PLA) matrix and fused filament fabrication (FFF) process parameters on the mechanical characteristics of 3D-printed components.

The PLA filaments containing different levels of CaCO₃ nanoparticles have been produced by mix-blending/extrusion process and were used to fabricate tensile and three-point bending test samples in FFF process under various sets of printing speed (PS), layer thickness (LT), filling ratio (FR) and printing pattern (PP) under a Taguchi L27 orthogonal array design. The quantified values of mechanical characteristics of 3D-printed samples in the uniaxial and the three-point bending experiments were modeled and optimized using a hybrid neural network/particle swarm optimization algorithm. The results of this hybrid scheme were used to specify the FFF process parameters and the concentration of nanoadditive in the matrix that result in the maximum mechanical properties of fabricated samples, individually and also in an accumulative response scheme. Diffraction scanning calorimetry (DSC) tests were conducted on a number of samples and the results were used to interpret the variations observed in the response variables of fabricated components against the FFF parameters and concentration of CaCO₃ nanoadditives.

The results of optimization in an accumulative scheme showed that the samples of linear PP, fabricated at high PS, low LT and at 100% FR, while containing 0.64% of CaCO₃ nanoadditives in the matrix, would possess the highest mechanical characteristics of 3D-printed PLA components.

FFF is a widely accepted additive manufacturing technique in production of different samples, from prototypes to the final products, in various sectors of industry. The incorporation of chopped fibers and nanoparticles has been introduced recently in a few articles to improve the mechanical characteristics of produced components in FFF technique. However, the effectiveness of such practice is strongly dependent on the extrusion parameters and composition of polymer matrix.

The purpose of this paper is to propose an approach to achieve better accuracy in technology forecasting (TF) by providing the concepts of the service components and service composition based on the theory of the combining forecasts. Next, it adopts three quantitative analyses as service components to form service composition. This will support the need of more predictable TF, which raises the accuracy of the quantitative analysis and, at the same time, presents the service composition logic in a consistent manner in the form of customized TF.

This paper provides a systematic analysis of the technology forecasts for third‐generation (3G) telecommunication industry. This systematic approach mainly unifies the Bass model, logit model, and least squares analysis forecasting techniques, along with a reasonable assessment of the scope for the normal curve (±1 standard deviation), and attempts to find the maximum possibility frontier of the predictive value.

Through the integration and comparison of these three techniques, not only can the predicted values of the three forecasting methods be determined, but a preferred solution can also be derived through new methods, and in return, to investigate better accuracy and performances. Such an approach can also integrate the advantages of various methods to provide a prediction interval, as well as objective and realistic projections.

This envisaged concept of “service component and service composition” is an integration of backing up in TF instruments in selection and reselection, which in return, provide optimization of service composition and accuracy maximization, as well as better performance prediction. A well‐known limitation of this research is that sudden technology breakthroughs are often unforeseeable in the majority of main‐stream quantitative analyses.

Constructing a new effective approach as results of “service component and service composition” can be compared to the traditional research methods such as Delphi method or other mathematical algorithms. This method generally produces higher quality forecasts than those attained from a single source.

This paper is a synthesis paper which seeks to discuss an optimisation framework using software components, which is a new emerging paradigm in computer science.

The goal of this paper is to show the efficiency of the software component approach for the implementation of optimisation frameworks for engineering systems in general, and electromagnetic systems in particular.

This paper highlights the component standard, a generator based on analytical expressions of the system, and an optimization service. References and examples show application in the area of electromagnetic components and systems.

This paper presents CADES, a framework dedicated to system design, based on optimization needs. The framework is defined with a standard implementing the software component paradigm and a pattern to use it. Indeed, this pattern details how to create and use a component (the model of the device to design).

This paper shows how the new emerging paradigm of software components can be used for building new generations of optimisation environment allowing capitalisation and reuse by combination of software components containing models and optimisation algorithms.