Search results

This paper aims to present a methodology to help end-users to find appropriate part candidates for the use of the additive manufacturing (AM) technology. These shall be capable of bringing AM into their businesses. The concept furthermore includes approaches for redesigning current available parts and helps to estimate the economic implications of the use of the technology.

The approach starts to discuss general economic aspects for the successful use of AM. While describing the introduction of new technologies into existing businesses, the importance of an appropriate part selection for AM is pointed out. A methodology for a part selection process is presented, and the different criteria are developed. An approach for a redesign of the selected parts, including the gathering of requirements, is given based on different sample parts. A variation of criteria to include measures for product piracy is highlighted.

The methodology has proven applicability in several research and industry projects in aerospace applications. Independent part selections from experts analyzed within a project of the European Space Agency had a 90 per cent overlap with the results. It allows companies with only basic AM knowledge to start a part screening for applicable AM candidates in their own company with a reasonable effort.

The methodology for the redesign process helps to identify the main functions of the products targeted and the relevant environment, so one can benefit from the various advantages that AM has to offer. The selection methodology helps to ask the right questions and to reduce the effort.

In the last a few years a number of highly publicized incidents of Distributed Denial of Service (DDoS) attacks against high‐profile government and commercial websites have made people aware of the importance of providing data and services security to users. A DDoS attack is an availability attack, which is characterized by an explicit attempt from an attacker to prevent legitimate users of a service from using the desired resources. This paper introduces the vulnerability of web applications to DDoS attacks, and presents an active distributed defense system that has a deployment mixture of sub‐systems to protect web applications from DDoS attacks. According to the simulation experiments, this system is effective in that it is able to defend web applications against attacks. It can avoid overall network congestion and provide more resources to legitimate web users.

Large supply and computer networks contain heterogeneous information and correlation among their components, and are distributed across a large geographical region. This paper aims to investigate and develop a generic knowledge integration framework that can handle the challenges posed in complex network management. It also seeks to examine this framework in various applications of essential management tasks in different infrastructures.

Efficient information and knowledge integration technologies are key to capably handling complex networks. An adaptive fusion framework is proposed that takes advantage of dependency modelling, active configuration planning and scheduling, and quality assurance of knowledge integration. The paper uses cases of supply network risk management and computer network attack correlation (NAC) to elaborate the problem and describe various applications of this generic framework.

Information and knowledge integration becomes increasingly important, enabled by technologies to collect and process data dynamically, and faces enormous challenges in handling escalating complexity. Representing these systems into an appropriate network model and integrating the knowledge in the model for decision making, directed by information and complexity measures, provide a promising approach. The preliminary results based on a Bayesian network model support the proposed framework.

First, the paper discussed and defined the challenges and requirements faced by knowledge integration in complex networks. Second, it proposed a knowledge integration framework that systematically models various network structures and adaptively integrates knowledge, based on dependency modelling and information theory. Finally, it used a conceptual Bayesian model to elaborate the application to supply chain risk management and computer NAC of this promising framework.

The purpose of this qualitative, multiple-case study was to explore the successful strategies that managers of light and high-tech small and medium-sized manufacturing companies in the Netherlands, use to adopt additive manufacturing (AM) technology into their business models.

A qualitative, multiple-case study approach was used. The participants for this study consisted of executive-level managers of light and high-tech manufacturing companies in the Netherlands. Company documents were studied, and individual interviews were undertaken with participants to gain an understanding of the strategies they used to adopt AM technology into their business models.

Three significant themes emerged from the data analysis: identify business opportunities for AM technology, experiment with AM technology and embed AM technology.

The findings of this study could be of advantage to industry leaders and manufacturing managers who are contemplating to adopt AM in their business models.

This study may contribute to the further proliferation of AM technology. Industry leaders may also gain a clearer understanding of the effects of 3DP on local employment. The results of the study may also work as a catalyst for increased awareness for manufacturing firm leaders who have not yet considered the opportunities and threats AM technology presents to their organizations.

The study aims to investigate the impact of additive manufacturing (AM) on the performance of a spare parts supply chain with a particular focus on underlying spare part demand patterns.

This work evaluates various AM-enabled supply chain configurations through Monte Carlo simulation. Historical demand simulation and intermittent demand forecasting are used in conjunction with a mixed integer linear program to determine optimal network nodal inventory policies. By varying demand characteristics and AM capacity this work assesses how to best employ AM capability within the network.

This research assesses the preferred AM-enabled supply chain configuration for varying levels of intermittent demand patterns and AM production capacity. The research shows that variation in demand patterns alone directly affects the preferred network configuration. The relationship between the demand volume and relative AM production capacity affects the regions of superior network configuration performance.

This research makes several simplifying assumptions regarding AM technical capabilities. AM production time is assumed to be deterministic and does not consider build failure probability, build chamber capacity, part size, part complexity and post-processing requirements.

This research is the first study to link realistic spare part demand characterization to AM supply chain design using quantitative modeling.

This study aimed to investigate the effects of accessibility, quality perception, and price perception on consumer satisfaction with imported fruit, specifically imported Asian pears. The moderating role of the country image was also assessed.

A total of 413 Americans aged over 19 years who had purchased imported Asian pears were surveyed through an online questionnaire. Structural equation modeling and multiple group analysis were conducted to test the hypotheses.

Accessibility, quality perception, and price perception exhibited a significant impact on consumer satisfaction with imported Asian pears and influenced behavioral intention through satisfaction. Multiple group analysis results revealed a moderating effect of the country image on these relationships. Accessibility had a significant impact on consumer satisfaction of consumers with a high country image of Korea, whereas prices had no significant impact on them. In contrast, price was determined as a key satisfaction factor for consumers with a low country image of Korea, whereas accessibility did not have a significant impact.

This study contributes to the literature on imported fruits and provides practical implications for promoting the consumption of imported Asian pears in the US.

This paper aims to review recent research in design for additive manufacturing (DfAM), including additive manufacturing (AM) terminology, trends, methods, classification of DfAM methods and software. The focus is on the design engineer’s role in the DfAM process and includes which design methods and tools exist to aid the design process. This includes methods, guidelines and software to achieve design optimization and in further steps to increase the level of design automation for metal AM techniques. The research has a special interest in structural optimization and the coupling between topology optimization and AM.

The method used in the review consists of six rounds in which literature was sequentially collected, sorted and removed. Full presentation of the method used could be found in the paper.

Existing DfAM research has been divided into three main groups – component, part and process design – and based on the review of existing DfAM methods, a proposal for a DfAM process has been compiled. Design support suitable for use by design engineers is linked to each step in the compiled DfAM process. Finally, the review suggests a possible new DfAM process that allows a higher degree of design automation than today’s process. Furthermore, research areas that need to be further developed to achieve this framework are pointed out.

The review maps existing research in design for additive manufacturing and compiles a proposed design method. For each step in the proposed method, existing methods and software are coupled. This type of overall methodology with connecting methods and software did not exist before. The work also contributes with a discussion regarding future design process and automation.

For more than ten years, the value of additive manufacturing (AM) for after-sales service logistics has been propagated. Today, however, only few applications are observed in practice. The purpose of this paper is to discuss possible reasons for this discrepancy and to develop a method to simplify the identification of economically valuable and technologically feasible business cases.

The approach is based on the analytic hierarchy process and relies on spare part information, that is easily retrievable from the company databases. This has two advantages: first, the approach can be customized toward specific company characteristics, and second, a very large number of spare parts may be assessed simultaneously. A field study is discussed in order to demonstrate and validate the approach in practice. Furthermore, sensitivity analyses are performed to evaluate the robustness of the method.

Results provide evidence that the method allows a valid prioritization of a large spare part assortment. Also, sensitivity analyses clarify the robustness of the approach and illustrate the flexibility of applying the method in practice. More than 1,000 positive business cases of AM for after-sales service logistics have been identified based on the method.

The developed method enables companies to rank spare parts according to their potential value when produced with AM. As a result, companies can evaluate the most promising spare parts first. This increases the effectiveness and efficiency of identifying business cases and thus may support the adoption of AM in after-sales service supply chains.

Adopting additive manufacturing (AM) on a large-scale requires an adoption in company value chains. This may happen through product innovation and require interorganizational cooperation, but the value-adding potential of cooperation and application recognition is still poorly understood. This study aims to investigate the progress of AM adoption in innovation projects featuring AM application recognition and interorganizational cooperation in the value chain.

A multiple-case study was implemented in successful metallic AM adoption examples to increase the understanding of AM adoption in value chains. Primary data were collected through interviews and documents in three AM projects, and the data were analyzed qualitatively.

All three AM projects showed evidence of successful AM value chain adoption. Identifying the right application and the added value of AM within it were crucial starting points for finding new value chains. Interorganizational collaboration facilitated both value-based designs and experimentation with new supply chains. Thereby, the focal manufacturing company did not need to invest in AM machines. The key activities of the new value chain actors are mapped in the process of AM adoption.

The cases are set in a business-to-business context, which narrows the transferability of the results. As a theoretical contribution, this paper introduces the concept of AM value chain adoption. The value-adding potential of AM is identified, and the required value-adding activities in collaborative innovation are reported. As a practical implication, the study reveals how companies can learn of AM and adopt AM value chains without investing in AM machines. They can instead leverage relationships with other companies that have the AM knowledge and infrastructure.

This paper introduces AM value chain adoption as a novel, highly interactive phase in the industry-wide adoption of metallic AM. AM value chain adoption is characterized in multi-company collaboration settings, which complements the single-company view dominant in previous research. Theory elaboration is offered through merging technology adoption with external integration from the information processing view, emphasizing the necessity of interorganizational cooperation in AM value chain adoption. Companies can benefit each other during AM adoption, starting with identifying the value-creating opportunities and applications for AM.

Although many researchers have widely studied additive manufacturing (AM) as one of the most important industrial revolutions, few have presented a bibliometric analysis of the published studies in this area. This paper aims to evaluate AM research trends based on 4607 publications most cited from year 2010 to 2020.

The research methodology is bibliometric indicators and network analysis, including analysis based on keywords, citation analysis, productive journal, related published papers and authors indicators. Two free available software were employed VOSviewer and Bibexcel.

Keywords analysis results indicate that among the AM processes, Selective Laser Melting and Fused Deposition Modeling techniques, are the two processes ranked on top of the techniques employed and studied with 35.76% and 20.09% respectively. The citation analysis by VOSviewer software, reveals that the medical applications field and the fabrication of metal parts are the areas that interest researchers greatly. Different new research niches, as pharmaceutical industry, digital construction and food fabrication are growing topics in AM scientific works. This study reveals that journals “Materials & design”, “Advanced materials”, “Acs applied materials & interfaces”, “Additive manufacturing”, “Advanced functional materials” and “Biofabrication” are the most productive and influential in AM scientific research.

The results and conclusions of this work can be used as indicators of trends in AM research and/or as prospects for future studies in this area.