Cloud-Based Automated Design and Additive Manufacturing: A Usage Data-Enabled Paradigm Shift

A Review of the Latest Developments in the Field of Additive Manufacturing Techniques for Nuclear Reactors

This review paper provides insights the into current developments in additive manufacturing (AM) techniques. The comprehensive presentations about AM methods, material properties (i.e., irradiation damage, as-built defects, residual stresses and fatigue fracture), experiments, numerical simulations and standards are discussed as well as their advantages and shortages for the application in the field of nuclear reactor. Meanwhile, some recommendations that need to be focused on are presented to advance the development and application of AM techniques in nuclear reactors. The knowledge included in this paper can serve as a baseline to tailor the limitations, utilize the superiorities and promote the wide feasibilities of the AM techniques for wide application in the field of nuclear reactors.

Performance Analysis of Selected Programming Languages in the Context of Supporting Decision-Making Processes for Industry 4.0

This study analyzes the possibility of using Go (Golang) in the context of Java and Python in decision-making processes, with particular emphasis on their use in industry-specific solutions for Industry 4.0. The authors intentionally compared Go with Java and Python, which have been widely used for many years for data analysis in many areas. The research work was based on decision trees data mining algorithms, and especially on classification trees, in which the measure of entropy as a heuristics to choose an attribute was taken into account. The tests were carried out on various parameters describing calculation time, RAM usage, and CPU usage. The source data, which were the basis for the computing of the decision tree algorithm implemented using these three languages, were obtained from a commercial remote prototyping system and were related to the target customers’ choice of methods and means of the full design-creation process.

Process of Creating an Integrated Design and Manufacturing Environment as Part of the Structure of Industry 4.0

This paper presents the process for creating an integrated design and manufacturing environment supporting 3D printing as part of the structure of Industry 4.0. This process is based on a developed framework for the design of modern automated and computerized infrastructure. The task of the described system is to combine all the steps included in the operating range of incremental systems based on an IT platform by integrating data from individual areas, such as IT systems supporting remote 3D printing. The proposed framework for incremental processes is a universal solution that can be defined in detail by a single organizational unit running 3D printing, as well as by a cluster of entities related to 3D printing. In the initial phase, the framework design includes a set of guidelines for IT (Information Technology) systems that facilitate the construction of individual elements and the creation of communication interfaces. In subsequent stages, the framework may already implement elements of the access and communication program interface, as well as guidelines for the industrial components to be included. The proposed framework for additive technologies is based on modern IT tools that enable the creation of geographically and functionally possible prototyping systems that can be integrated into the structure of Industry 4.0. To create optimal processes and economic systems, the principles of the construction and integration of individual services and equipment were developed. This new comprehensive approach is proposed in the present paper as a coherent framework. Moreover, the proposed solution has great potential for use in the design and production processes of various industries, such as chemicals, materials and construction.

Successful approaches for implementing additive manufacturing

Purpose

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.

Design/methodology/approach

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.

Findings

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

Research limitations/implications

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.

Originality/value

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 Potential of Additive Manufacturing in the Smart Factory Industrial 4.0: A Review

Additive manufacturing (AM) or three-dimensional (3D) printing has introduced a novel production method in design, manufacturing, and distribution to end-users. This technology has provided great freedom in design for creating complex components, highly customizable products, and efficient waste minimization. The last industrial revolution, namely industry 4.0, employs the integration of smart manufacturing systems and developed information technologies. Accordingly, AM plays a principal role in industry 4.0 thanks to numerous benefits, such as time and material saving, rapid prototyping, high efficiency, and decentralized production methods. This review paper is to organize a comprehensive study on AM technology and present the latest achievements and industrial applications. Besides that, this paper investigates the sustainability dimensions of the AM process and the added values in economic, social, and environment sections. Finally, the paper concludes by pointing out the future trend of AM in technology, applications, and materials aspects that have the potential to come up with new ideas for the future of AM explorations.

A Mission Reliability-Driven Manufacturing System Health State Evaluation Method Based on Fusion of Operational Data

The rapid development of complexity and intelligence in manufacturing systems leads to an increase in potential operational risks and therefore requires a more comprehensive system-level health diagnostics approach. Based on the massive multi-source operational data collected by smart sensors, this paper proposes a mission reliability-driven manufacturing system health state evaluation method. Characteristic attributes affecting the mission reliability are monitored and analyzed based on different sensor groups, including the performance state of the manufacturing equipment, the execution state of the production task and the quality state of the manufactured product. The Dempster-Shafer (D-S) evidence theory approach is used to diagnose the health state of the manufacturing system. Results of a case study show that the proposed evaluation method can dynamically and effectively characterize the actual health state of manufacturing systems.

Incremental Distributed Learning with JavaScript Agents for Earthquake and Disaster Monitoring

Ubiquitous computing and The Internet-of-Things (IoT) grow rapidly in today's life and evolving to Self-organizing systems (SoS). A unified and scalable information processing and communication methodology is required. In this work, mobile agents are used to merge the IoT with Mobile and Cloud environments seamless. A portable and scalable Agent Processing Platform (APP) provides an enabling technology that is central for the deployment of Multi-Agent Systems (MAS) in strong heterogeneous networks including the Internet. A large-scale use-case deploying Multi-agent systems in a distributed heterogeneous seismic sensor and geodetic network is used to demonstrate the suitability of the MAS and platform approach. The MAS is used for earthquake monitoring based on a new incremental distributed learning algorithm applied to seismic station data, which can be extended by ubiquitous sensing devices like smart phones. Different (mobile) agents perform sensor sensing, aggregation, local learning and prediction, global voting and decision making, and the application.

An Efficient Interactive Model for On-Demand Sensing-As-A-Servicesof Sensor-Cloud

This paper proposes an efficient interactive model for the sensor-cloud to enable the sensor-cloud to efficiently provide on-demand sensing services for multiple applications with different requirements at the same time. The interactive model is designed for both the cloud and sensor nodes to optimize the resource consumption of physical sensors, as well as the bandwidth consumption of sensing traffic. In the model, the sensor-cloud plays a key role in aggregating application requests to minimize the workloads required for constrained physical nodes while guaranteeing that the requirements of all applications are satisfied. Physical sensor nodes perform their sensing under the guidance of the sensor-cloud. Based on the interactions with the sensor-cloud, physical sensor nodes adapt their scheduling accordingly to minimize their energy consumption. Comprehensive experimental results show that our proposed system achieves a significant improvement in terms of the energy consumption of physical sensors, the bandwidth consumption from the sink node to the sensor-cloud, the packet delivery latency, reliability and scalability, compared to current approaches. Based on the obtained results, we discuss the economical benefits and how the proposed system enables a win-win model in the sensor-cloud.