Deep Learning to Reveal the Distribution and Diffusion of Water Molecules in Fuel Cell Catalyst Layers

Water management in the catalyst layers (CLs) of proton-exchange membrane fuel cells is crucial for its commercialization and popularization. However, the high experimental or computational cost in obtaining water distribution and diffusion remains a bottleneck in the existing experimental methods and simulation algorithms, and further mechanistic exploration at the nanoscale is necessary. Herein, we integrate, for the first time, molecular dynamics simulation with our customized analysis framework based on a multiattribute point cloud dataset and an advanced deep learning network. This was achieved through our workflow that generates simulated transport data of water molecules in the CLs as the training and test dataset. Deep learning framework models the multibody solid-liquid system of CLs on a molecular scale and completes the mapping from the Pt/C substrate structure and Nafion aggregates to the density distribution and diffusion coefficient of water molecules. The prediction results are comprehensively analyzed and error evaluated, which reveals the highly anisotropic interaction landscape between 50,000 pairs of interacting nanoparticles and explains the structure and water transport property relationship in the hydrated Nafion film on the molecular scale. Compared to the conventional methods, the proposed deep learning framework shows computational cost efficiency, accuracy, and good visual display. Further, it has a generality potential to model macro- and microscopic mass transport in different components of fuel cells. Our framework is expected to make real-time predictions of the distribution and diffusion of water molecules in CLs as well as establish statistical significance in the structural optimization and design of CLs and other components of fuel cells.

Control System Development and Implementation of a CNC Laser Engraver for Environmental Use with Remote Imaging

This article is aimed at studying the features of the control systems development for a small-sized Computer Numerical Control (CNC) portative laser engraver. The CNC is implemented in mobile maintenance and repair platforms for remote sensing of the environment where the wild environment may not allow us to access the animals and places. The proposed work in this paper is based on recent research, which shows that applying the automated CNC speeds up the processes of repair, modernizes the equipment size, and significantly reduces the economic costs; accordingly, the authors developed a block diagram of a portable CNC laser engraver. The choice of the hardware was also made, taking into account the possibility of quick replacement in the field, which reduces the repair time and the cost of the developed layout. A control system based on the selected modules was synthesized, and a stability check was carried out using MatLab tools. To check the correctness of the developed control system, the authors developed and assembled an experimental layout to illustrate the results of engraving on such a layout. Finally, the stability and sensitivity of the proposed system have been obtained and proved that the system works in a comfortable zone of stability. The obtained results show that the proposed CNC laser engraver has achieved the expected improvements (high speed, small size, short production and repairing time, minimum human influence factor, and achieving a better outcome).

Disentangling Capabilities for Industry 4.0 - an Information Systems Capability Perspective

Digital technologies revolutionise the manufacturing industry by connecting the physical and digital worlds. The resulting paradigm shift, referred to as Industry 4.0, impacts manufacturing processes and business models. While the 'why' and 'what' of Industry 4.0 have been extensively researched, the 'how' remains poorly understood. Manufacturers struggle with exploiting Industry 4.0's full potential as a holistic understanding of required Information Systems (IS) capabilities is missing. To foster such understanding, we present a holistic IS capability framework for Industry 4.0, including primary and support capabilities. After developing the framework based on a structured literature review, we refined and evaluated it with ten Industry 4.0 experts from research and practice. We demonstrated its use with a German machinery manufacturer. In sum, we contribute to understanding and analysing IS capabilities for Industry 4.0. Our work serves as a foundation for further theorising on Industry 4.0 and for deriving theory-led design recommendations for manufacturers.

Critical Obstacles Affecting Adoption of Industrial Big Data Solutions in Smart Factories

Industrial big data is the key to realize the vision of smart factories. This research aims to identify and explore potential barriers that prevent organizations from deploying industrial big data solutions in the development of smart factories through a socio-technical perspective. The research follows an inductive qualitative approach. Twenty-seven semi-structured interviews were conducted with the CEO, smart factory manager, IT managers, departmental heads, and IS consultants in the selected case company. The interview data were analyzed using a thematic analysis method. Derived from a thematic analysis, six sets of barriers including technical, data, technical support, organization, individual, and social issues were identified, as well as the relationships between them. An empirical framework was developed to highlight the relationship between these barriers. This study contributes to the knowledge of industrial big data in general and provides constructive insight into industrial big data implementation in smart factory development particularly.

Knowledge mapping of research on Industry 4.0: A visual analysis using CiteSpace

This study aims to explore thematic networks in research of Industry 4.0 in recent years. The analysis presented in the paper is based on the data retrieved from the Web of Science about publications that included the terms "fourth industrial revolution" and "Industry 4.0" within the domain of business application. The research consisted of a general analysis of publications and a more detailed analysis conducted using CiteSpace. CiteSpace, one of the very popular visual analysis tools for mapping the scientific networks, was used to analyze extracted articles and identify existing networks, clusters, and most influential authors. The findings indicate that Industry 4.0 represents a well-developed research field with distinctive but complementary research topics and also points out the emerging research topics. The study results can be helpful in further research on Industry 4.0 and relating technologies because it indicates the direction of recent research development.