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The industrial progress over these years has led to fast, goodquality production. Despite this progress, the impacts related to such type of production, whether these are social, economic, or environmental, have not been, at times, studied extensively. The industry realized the importance of a greener approach and as a result, new sustainable technologies, such as additive manufacturing (AM), have emerged. To generalize the environmental benefits of AM over traditional manufacturing, methodologies like Life Cycle Assessment (LCA) are used. The proposed work has the intent of understanding and quantifying the environmental impacts associated with a particular AM technique for the fabrication of metal parts, Wire Arc Additive Manufacturing (WAAM). An LCA is conducted and, considering the same circumstances, the environmental impacts related to the production of 3 different metal parts are analysed. In order to understand the results obtained, Computer Numerical Control (CNC) milling, which is also used for the fabrication of metal parts, is equally considered. In this particular application, when compared to CNC Milling, WAAM proves to have a 12%-47% in the environmental impact, depending upon the geometry considered. The environmental hotspot identified for both processes is the production of the raw material.

Additive manufacturing (AM) is one of the technologies of Industry 4.0 that has been contributing to the development of different manufacturing industries. The integration of sustainability concepts into additive manufacturing has been gaining attention among researchers. This integration is essential in the development of AM technologies and can be a significant asset in terms of decision making for organizations. This work aims to present a concise literature review on the integration of decision making, especially multi-criteria decision making, and sustainability into the AM environment. The literature on this topic currently possesses a total of fifteen documents, which were analyzed in this work. Some developments on this topic have been achieved in domains such as material selection, process selection and challenges, and drivers' analysis of sustainable AM. This review shows that even though there has been an effort in recent years to integrate sustainability into additive manufacturing, there is still a long road to the development of this topic for the future, and so some recommendations for future research paths are presented. [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue of the journal Electronics titled "Recent Advanced Applications of Virtual Industrial Informatics and Robotics." The special issue focuses on the integration of digital technologies, such as artificial intelligence, virtual/augmented reality, the Internet of Things, automation, 5G, and robotics, in industrial production. The articles in the special issue cover topics such as industrial informatics, unmanned aerial vehicles for inventory management, stable grasping of objects using robotic hands, test-case generation technologies for electromechanical systems, trajectory planning algorithms for robotic manipulators, winding processes of carbon fiber, spectrum sensing techniques for wireless applications, defense methods against adversarial attacks on deep neural network models, and the use of virtual reality technology in combination with intelligent chatbots for customer service. The articles provide insights into the current research and applications in these areas and highlight the challenges and future trends in industrial informatics and robotics. [Extracted from the article]

Industrial Symbiosis (IS) involves a network of organizations that exchange energy, materials, and by-products to lower production costs, reduce environmental impact, and conserve natural resources. Despite over two decades of extensive research into IS, its benefits are well known, but implementation remains challenging. This paper proposes utilizing blockchain technology (BCT) to digitize IS, making it more secure and transparent. First, drivers and barriers of BCT implementation in IS are identified. A smart contract architecture framework using Hyperledger Fabric is then proposed using the constructed theoretical background and abductive method. Finally, the paper discusses how this framework supports the implementation of BCT in IS by addressing its drivers and attempting to overcome its barriers. It is a resource for those seeking a comprehensive grasp of the foundational elements necessary for constructing a successful IS blockchain design, which is adaptable to all types of IS network configurations. [ABSTRACT FROM AUTHOR]

In the domain of Marine Education and Training (MET), simulators have been utilized for the purpose of training seafarers in the norms for avoiding collisions or for developing the skill of ship manoeuvrability, and even the operation of machinery in the engine room, as well as for conducting research on the subject matter of ship structure, specialized vessel operation, working principle of equipment, and shipboard safety training. These tools are even more important when facing disruptive events such as the COVID-19 pandemic. In MET institutions, full-mission bridge and engine room simulators have been utilized for teaching seafarers for more than a decade. A Systematic Literature Review (SLR) was conducted to identify immersive and non-immersive simulator applications produced over the previous ten years to improve seafarers' experiential teaching and learning, in the maritime domain. We retrieved 27 articles using the four stages of PRISMA paradigm: Identification, Screening, Eligibility, and Inclusion. The selected papers were read and analyzed according to the training type, the area of training, and the technologies used. The utilization of immersive and non-immersive simulators in the context of the MET domain has been identified and mapped. A few research studies (9 out of 27) compared immersive and non-immersive simulator-based training with conventional training. The quality and efficacy of immersive and non-immersive simulator training at MET institutions have been studied. A model from the learner's perspective is essential and recommended for future research to assess efficiency and efficacy. [ABSTRACT FROM AUTHOR]

The growing competitiveness in the automotive industry and the strict standards to which it is subject, require high quality standards. For this, quality tools such as the failure mode and effects analysis (FMEA) are applied to quantify the risk of potential failure modes. However, for qualitative defects with subjectivity and associated uncertainty, and the lack of specialized technicians, it revealed the inefficiency of the visual inspection process, as well as the limitations of the FMEA that is applied to it. The fuzzy set theory allows dealing with the uncertainty and subjectivity of linguistic terms and, together with the expert systems, allows modeling of the knowledge involved in tasks that require human expertise. In response to the limitations of FMEA, a fuzzy FMEA system was proposed. Integrated in the design, measure, analyze, improve and control (DMAIC) cycle, the proposed system allows the representation of expert knowledge and improves the analysis of subjective failures, hardly detected by visual inspection, compared to FMEA. The fuzzy FMEA system was tested in a real case study at an industrial manufacturing unit. The identified potential failure modes were analyzed and a fuzzy risk priority number (RPN) resulted, which was compared with the classic RPN. The main results revealed several differences between both. The main differences between fuzzy FMEA and classical FMEA come from the non-linear relationship between the variables and in the attribution of an RPN classification that assigns linguistic terms to the results, thus allowing a strengthening of the decision-making regarding the mitigation actions of the most "important" failure modes. [ABSTRACT FROM AUTHOR]

Industrial assistive systems result from a multidisciplinary effort that integrates IoT (and Industrial IoT), Cognetics, and Artificial Intelligence. This paper evaluates the Prediction by Partial Matching algorithm as a component of an assembly assistance system that supports factory workers, by providing choices for the next manufacturing step. The evaluation of the proposed method was performed on datasets collected within an experiment involving trainees and experienced workers. The goal is to find out which method best suits the datasets in order to be integrated afterwards into our context-aware assistance system. The obtained results show that the Prediction by Partial Matching method presents a significant improvement with respect to the existing Markov predictors. [ABSTRACT FROM AUTHOR]

The technological advances brought forth by the Industry 4.0 paradigm have renewed the disruptive potential of artificial intelligence in the manufacturing sector, building the data-driven era on top of concepts such as Cyber–Physical Systems and the Internet of Things. However, data availability remains a major challenge for the success of these solutions, particularly concerning those based on deep learning approaches. Specifically in the quality inspection of structural adhesive applications, found commonly in the automotive domain, defect data with sufficient variety, volume and quality is generally costly, time-consuming and inefficient to obtain, jeopardizing the viability of such approaches due to data scarcity. To mitigate this, we propose a novel approach to generate synthetic training data for this application, leveraging recent breakthroughs in training generative adversarial networks with limited data to improve the performance of automated inspection methods based on deep learning, especially for imbalanced datasets. Preliminary results in a real automotive pilot cell show promise in this direction, with the approach being able to generate realistic adhesive bead images and consequently object detection models showing improved mean average precision at different thresholds when trained on the augmented dataset. For reproducibility purposes, the model weights, configurations and data encompassed in this study are made publicly available. [ABSTRACT FROM AUTHOR]

This paper proposes a new network topology design method that considers all the road nodes, energy stations and load centers to ensure the distribution of pipes along the road. The traditional graph theory and Prim Minimum Spanning Tree (MST) are used to simplify the map and minimize the length of the pipeline. After analyzing the limitations of the traditional network topology model, Point-to-Point (PTP), we present a new model, Energy Station-to-Load Point (ESLP). The model is optimized by minimum cost, not the shortest path. Finally, Pipe Diameter Grading (PDG) is proposed based on ESLP by solving for the pipe diameter that gives the minimum cost under different load demands in the process of optimization. The network design method is effectively applied in a case, and the results show that the path of the optimized plan is 1.88% longer than that of the pre-optimized plan, but the cost is 2.38% lower. The sensitivity analysis shows that the cost of pipeline construction, project life and electricity price all have an impact on the optimization results, and the cost of pipeline construction is the most significant. The difference between the different classifications of pipelines affects whether PDG is effective or not. [ABSTRACT FROM AUTHOR]

In this paper, a new way to mitigate the current interactions is proposed. The problem of current interactions arises when a modular three-phase (3-phase) rectifier (three single-phase modules) with boost converter for power factor correction (PFC) is used. A new differential-mode choke filter is implemented in the developed boost converter. The choke here is a specially made differential inductor in the input of the boost converter that eliminates the known current interactions. To prove the new concept, a study of the level of mitigation of the current interactions is presented. The control is operated in continuous driving mode (CCM), and the popular UC3854B circuit was used for this. The rectifier proposal is validated through a set of simulations performed on the PSIM 12.0 platform, as well as the construction of a prototype. With the results obtained, it is confirmed that the differential-mode choke filter eliminates the current interactions. It is observed that at the input of the rectifier, a sinusoidal alternating current with a low level of harmonic distortion is consumed from the grid. The sinusoidal shape of the phase current proves that a better power factor capable of meeting the international standards is obtained, and that the circuit in its initial version is operational. This proven result promises a good PFC operation, to guarantee the better quality of the electrical energy, being able to be applied in systems that require a high PFC, e.g., in battery charging, wind systems, or in aeronautics and spacecrafts. [ABSTRACT FROM AUTHOR]

Non-synchronous renewable energy sources (RESs) have strong volatility and low inertia, which brings about great challenges on the accommodation of RESs and the security and stability of power systems. This paper proposes a bi-level power system dispatch and control architecture based on the grid-friendly virtual power plant (GVPP), so as to accommodate RESs flexibly and securely. The typical dispatch and control system of the power system in China is presented, and the particular challenges stemming from non-synchronous RESs are analyzed. The functional requirements, concept, and fundamental design of the GVPP are provided, which is distinguished from traditional virtual power plants (VPPs) for its active participation in power system stability control. Based on the cloud platform, a bi-level dispatch and control architecture considering two objectives is established. First, in the inner level, the GVPP operates to promote the accommodation of RESs under normal condition. Then, from the perspective of out-level power systems, GVPPs serve as spinning reserves for power support under contingencies. Besides, the key problems to be solved in the development of the GVPP-based architecture are summarized. Although the architecture is proposed for the power system in China, it can be applied to any power systems with similar challenges. [ABSTRACT FROM AUTHOR]

Microgrids constitute complex systems that integrate distributed generation (DG) and feature different operational modes. The optimal coordination of directional over-current relays (DOCRs) in microgrids is a challenging task, especially if topology changes are taken into account. This paper proposes an adaptive protection approach that takes advantage of multiple setting groups that are available in commercial DOCRs to account for network topology changes in microgrids. Because the number of possible topologies is greater than the available setting groups, unsupervised learning techniques are explored to classify network topologies into a number of clusters that is equal to the number of setting groups. Subsequently, optimal settings are calculated for every topology cluster. Every setting is saved in the DOCRs as a different setting group that would be activated when a corresponding topology takes place. Several tests are performed on a benchmark IEC (International Electrotechnical Commission) microgrid, evidencing the applicability of the proposed approach. [ABSTRACT FROM AUTHOR]