A Blockchain-Based Architecture for Securing Industrial IoTs Data in Electric Smart Grid

There are numerous internet-connected devices attached to the industrial process through recent communication technologies, which enable machine-to-machine communication and the sharing of sensitive data through a new technology called the industrial internet of things (IIoTs). Most of the suggested security mechanisms are vulnerable to several cybersecurity threats due to their reliance on cloud-based services, external trusted authorities, and centralized architectures; they have high computation and communication costs, low performance, and are exposed to a single authority of failure and bottleneck. Blockchain technology (BC) is widely adopted in the industrial sector for its valuable features in terms of decentralization, security, and scalability. In our work, we propose a decentralized, scalable, lightweight, trusted and secure private network based on blockchain technology/smart contracts for the overhead circuit breaker of the electrical power grid of the Al-Kufa/Iraq power plant as an industrial application. The proposed scheme offers a double layer of data encryption, device authentication, scalability, high performance, low power consumption, and improves the industry’s operations; provides efficient access control to the sensitive data generated by circuit breaker sensors and helps reduce power wastage. We also address data aggregation operations, which are considered challenging in electric power smart grids. We utilize a multi-chain proof of rapid authentication (McPoRA) as a consensus mechanism, which helps to enhance the computational performance and effectively improve the latency. The advanced reduced instruction set computer (RISC) machines ARM Cortex-M33 microcontroller adopted in our work, is characterized by ultra-low power consumption and high performance, as well as efficiency in terms of real-time cryptographic algorithms such as the elliptic curve digital signature algorithm (ECDSA). This improves the computational execution, increases the implementation speed of the asymmetric cryptographic algorithm and provides data integrity and device authenticity at the perceptual layer. Our experimental results show that the proposed scheme achieves excellent performance, data security, real-time data processing, low power consumption (70.880 mW), and very low memory utilization (2.03% read-only memory (RAM) and 0.9% flash memory) and execution time (0.7424 s) for the cryptographic algorithm. This enables autonomous network reconfiguration on-demand and real-time data processing.     

智能制造装备的发展现状与趋势

智能制造装备是先进制造技术、信息技术以及人工智能技术在制造装备上的集成和深度融合,是实现高效、高品质、节能环保和安全可靠生产的下一代制造装备。在综述了智能制造装备国内外发展现状的基础上,重点论述了智能制造装备的内涵及其发展重点,并得出结论,认为德国的"工业4.0"和美国的工业互联网装备将是智能制造装备未来的发展方向。     

Evaluation of neutron nuclear data on xenon isotopes

Neutron nuclear data of Xe isotopes have been evaluated in the energy region, including the resolved resonance one, from 1 keV to 20 MeV by using the theoretical nuclear reaction models. The phenomenological optical model potential was employed to calculate the total cross section for natural Xe with the coupled-channels method. The cross sections for channels of capture, (n, 2n), (n, p) and (n, α) reactions were calculated and compared with available experimental results including recently measured data. The elastic scattering angular distributions and particle emission spectra were calculated, although there is no experimental information available. Reaction cross sections of evaluated libraries were considered for comparison with the calculated results. The presently calculated cross sections reproduce better the available experimental data.     

From Industry 4.0 to Research 4.0. Development of High Performance Fibers in the Light of Digitization

We associate a variety of innovations with the term ＂Industry 4.0＂. The pioneer of many 4.0 modifications forms the basisfor the trend towards the integrated di...     

Machine learning enabled integrated formulation and process design framework for a pharmaceutical 3D printing platform

The pharmaceutical manufacturing sector needs to rapidly evolve to absorb the next wave of disruptive industrial innovations—Industry 4.0. This involves incorporating technologies like artificial intelligence and 3D printing (3DP) to automate and personalize the drug production processes. This study aims to build a formulation and process design (FPD) framework for a pharmaceutical 3DP platform that recommends operating (formulation and process) conditions at which consistent drop printing can be obtained. The platform used in this study is a displacement-based drop-on-demand 3D printer that manufactures dosages by additively depositing the drug formulation as droplets on a substrate. The FPD framework is built in two parts: the first part involves building a machine learning model to simulate the forward problem—predicting printer operation for given operating conditions and the second part seeks to solve and experimentally validate the inverse problem—predicting operating conditions that can yield desired printer operation.     

Exploring a cobot risk assessment approach combining FMEA and PRAT

Collaborative robots are an emerging technology falling within the scope of Industry 4.0 and based on the concept of Human-Robot Collaboration (HRC). Unlike traditional industrial robots, collaborative robots are used in shared workspaces with no safety fences. Hence, prospective hazardous contacts need to be avoided or mitigated through a risk assessment. The normative standards such as ISO TS 15066 suggest a list of common hazards, but do not guide the robot system user through the risk assessment process. To address this shortcoming, this paper proposes a practical eight steps risk assessment approach, resulting in a risk priority list. In order to provide an accurate, practical, quantitative and supportive tool for HRC environments, the Failure Mode and Effects Analysis (FMEA) and the Proportional Risk Assessment technique (PRAT) techniques are proposed for risk assessment. The two techniques mentioned above are combined in the suggested new methodology, highlighting both their benefits and disadvantages. The proposed methodology is applied with positive results to a collaborative brick-lifter case study.     

The future of manufacturing: Utopia or dystopia?

Digital manufacturing technologies (DMTs) have the potential to transform industry productivity, but their introduction into the workplace is often a complex process, requiring not only technical expertise but also an awareness of ethical and societal challenges surrounding human–system integration. Concerns about the introduction of new technology have been prevalent throughout history, and exploring public perceptions of these technologies can provide insight to help address such cultural anxieties. However, evaluating user perceptions of futuristic technology is difficult, requiring novel approaches to provide context and understanding. To explore users' perceptions of future DMTs, we applied the ContraVision technique in a questionnaire-based study. Participants viewed films, representing fictionalized utopic and dystopic visions of what the future of these DMTs might involve, and a questionnaire probed the perceptions of the technologies afterward. Findings showed that irrespective of the way technology was portrayed, participants had concerns about the ethical and responsible implementation of these tools. Participant responses were analyzed to identify key challenges for policy surrounding DMT implementation in the future of manufacturing.     

ACHEMA 2022 – Digital Twins

Process development and quality assurance through interconnected structures between the digital and real worlds are often reflected in digital twins. This article summarizes current trends and developments, their special features, but also the challenges of digital twins. The report includes a selection of different new and further developments, possible applications and research projects that were exhibited at ACHEMA 2022.     

流程工业智能制造准备度模型及应用实践

近年来，绝大多数制造业企业为应对新一轮工业革命带来的巨大挑战，都在积极寻求改造提升的机会。然而，企业在进行智能制造提升与数字化转型的过程中，往往很难对自身所处的发展阶段和转型目标有清晰准确的认识。本文基于流程工业生产运营特点，提出了一个流程工业智能制造准备度评估模型，从业务、组织、技术和智能四个维度对企业开展智能制造建设所需具备的要素条件进行评估。模型涵盖了9个评估类、25个评估域和249个特征项要求，旨在帮助流程工业企业厘清发展现状，摸清短板差距，明确建设方向。此外，本文还提出了智能制造准备度水平的量化指标——准备度指数，并对其计算方法和评估流程进行了详细描述，帮助企业进行智能制造准备度水平的自评估。最后，通过本模型在山东省35家流程工业企业智能制造评估的应用案例，论述了模型的应用效果及价值。     

A design of predictive manufacturing system in IoT-assisted Industry 4.0 using heuristic-derived deep learning

The predictive maintenance function is ensured with the earlier detection of errors and faults in the machinery before reaching its critical stages. On the other hand, the challenges faced by Internet of things (IoT) devices are the security problem because they can be easily attacked by comparing the other devices such as computers or portable devices. It cannot solve the high-dimensional issues and imbalanced data. The computation cost is very expensive when using the modern sampling method. In addition, the conventional methods for predictive maintenance are incorporated with a single method. So, the maintenance and prognostic tasks are very hard to address simultaneously. Thus, a new predictive manufacturing system in Industry 4.0 for examining the machines is proposed. In the initial stage, the data are collected from IoT industry sensors. Considerably, the data cleaning is carried out, and deep features are extracted through the “Multi-Scale Dilation Attention Convolutional Neural Network (MSDA-CNN).” Further, the deep, weighted features are extracted, where the weight is optimized using the hybrid algorithm named Probabilistic Beetle Swarm-Butterfly Optimization (PBS-BO). In the end, the weighted features are given to the Optimized Hybrid Fault Detection (OHFD) that is performed by the “Deep Neural Network (DNN) and Deep Belief Network (DBN).” Finally, if any faults in machines are predicted, then the system sends alerts to the industrialists for suitable decision-making. The efficiency of the suggested model is evaluated on a set of real measurements in Industry 4.0.