A fourth industrial revolution is occurring in global manufacturing. It is based on the introduction ofInternet of thingsandservitizationconcepts into manufacturing companies, leading to vertically and horizontally integrated production systems. The resultingsmart factoriesare able to fulfill dynamic customer demands with high variability in small lot sizes while integrating human ingenuity and automation. To support the manufacturing industry in this conversion process and enhance global competitiveness, policy makers in several countries have established research and technology transfer schemes. Most prominently, Germany has enacted itsIndustrie 4.0program, which is increasingly affecting European policy, while the United States focuses onsmart manufacturing. Other industrial nations have established their own programs on smart manufacturing, notably Japan and Korea. This shows that manufacturing intelligence has become a crucial topic for researchers and industries worldwide. The main object of these activities are the so-called cyber-physical systems (CPS): physical entities (e.g., machines, vehicles, and work pieces), which are equipped with technologies such as RFIDs, sensors, microprocessors, telematics or complete embedded systems. They are characterized by being able to collect data of themselves and their environment, process and evaluate these data, connect and communicate with other systems, and initiate actions. In addition, CPS enabled new services that can replace traditional business models based solely on product sales. The objective of this paper is to provide an overview of the Industrie 4.0 and smart manufacturing programs, analyze the application potential of CPS starting from product design through production and logistics up to maintenance and exploitation (e.g., recycling), and identify current and future research issues. Besides the technological perspective, the paper also takes into account the economic side considering the new business strategies and models available.

The nature of manufacturing systems faces ever more complex, dynamic and at times even chaotic behaviors. In order to being able to satisfy the demand for high-quality products in an efficient manner, it is essential to utilize all means available. One area, which saw fast pace developments in terms of not only promising results but also usability, is machine learning. Promising an answer to many of the old and new challenges of manufacturing, machine learning is widely discussed by researchers and practitioners alike. However, the field is very broad and even confusing which presents a challenge and a barrier hindering wide application. Here, this paper contributes in presenting an overview of available machine learning techniques and structuring this rather complicated area. A special focus is laid on the potential benefit, and examples of successful applications in a manufacturing environment.

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An operative and versatile household energy management system is proposed to develop and implement demand response (DR) projects. These are under the hybrid generation of the energy storage system (ESS), photovoltaic (PV), and electric vehicles (EVs) in the smart grid (SG). Existing household energy management systems cannot offer its users a choice to ensure user comfort (UC) and not provide a sustainable solution in terms of reduced carbon emission. To tackle these problems, this research work proposes a heuristic-based programmable energy management controller (HPEMC) to manage the energy consumption in residential buildings to minimize electricity bills, reduce carbon emissions, maximize UC and reduce the peak-to-average ratio (PAR). We used our proposed hybrid genetic particle swarm optimization (HGPO) algorithm and existing algorithms like a genetic algorithm (GA), binary particle swarm optimization algorithm (BPSO), ant colony optimization (ACO), wind-driven optimization algorithm (WDO), bacterial foraging algorithm (BFA) to schedule smart appliances optimally to attain our desired objectives. In the proposed model, consumers use solar panels to produce their energy from microgrids. We also perform MATLAB simulations to validate our proposed HGPO-HPEMC (HHPEMC), and results confirm the efficiency and productivity of our proposed HPEMC based strategy. The proposed algorithm reduced the electricity cost by 25.55%, PAR by 36.98%, and carbon emission by 24.02% as compared to the case of without scheduling.

The adoption of advanced manufacturing intelligence technologies requires managing the interaction of information in Product-Service Systems (PSS) by combining Product (PLM) and Service Lifecycle Management (SLM). While up to now no sound methodology exists, there is a strong need to have bi-directional coordination and interaction between PLM and SLM in a systematic way. A further challenge is to close loops, for example feedback from service delivery to the beginning-of-life phase of products. The objective of this paper is therefore to identify the interactions between SLM and PLM in manufacturing firms, based on expert interviews and illustrated in PSS use cases.

Environmental contours are an established method in probabilistic engineering design, especially in ocean engineering. The contours help engineers to select the environmental states which are appropriate for structural design calculations. Defining an environmental contour means enclosing a region in the variable space which corresponds to a certain return period. However, there are multiple definitions and methods to calculate an environmental contour for a given return period. Here, we analyze the established approaches and present a new concept which we call highest density contour (HDC). We define this environmental contour to enclose the highest density region (HDR) of a given probability density. This region occupies the smallest possible volume in the variable space among all regions with the same included probability, which is advantageous for engineering design. We perform the calculations using a numerical grid to discretize the original variable space into a finite number of grid cells. Each cell's probability is estimated and used for numerical integration. The proposed method can be applied to any number of dimensions, i.e. number of different variables in the joint probability model. To put the highest density contour method in context, we compare it to the established inverse first-order reliability method (IFORM) and show that for common probability distributions the two methods yield similarly shaped contours. In multimodal probability distributions, however, where IFORM leads to contours which are dificult to interpret, the presented method still generates clearly defined contours.Comment: preprint of the accepted version, 14 pages, 10 figure