Hasil untuk "Industrial directories"

José R. Rodríguez, J. Dixon, J. Espinoza et al.

X. Dai, Zhiwei Gao

Lida Xu

Y. Epstein, D. Moran

Abid Haleem, M. Javaid

Additive manufacturing (AM) is a set of technologies and are vital to fulfilling different requirements of Industry 4.0. So, there is a need to study different additive manufacturing applications toward its achievement. From the Scopus database, different research articles on “Industry 4.0” and “additive manufacturing applications in Industry 4.0” are identified and studied through a bibliometric analysis. It shows that there is an increasing trend of publications in this new area. Industry 4.0 has entered new markets which focus on customer delight by adding values in product and services. It supports automation, interoperability, actionable insights and information transparency. There are different components vital to implement Industry 4.0 requirements. Through this extensive literature review based work, we identified different components of Industry 4.0 and explained the critical ones briefly. Finally, 13 important AM applications in Industry 4.0 are identified. The main limitation of the AM manufactured part is of comparable low strength and associated quality, coupled with a high cost of the printing machine system. In this upcoming industrial revolution, AM is a crucial technology which has become the main component of product innovation and development. This disruptive technology can fulfil different challenges in the future manufacturing system and help the industry to produce innovative products. For this futuristic manufacturing system, additive manufacturing is an upcoming paradigm, and Industry 4.0 will use its potential to achieve required goals.

Aniekan Essien, C. Giannetti

Time-series forecasting is applied to many areas of smart factories, including machine health monitoring, predictive maintenance, and production scheduling. In smart factories, machine speed prediction can be used to dynamically adjust production processes based on different system conditions, optimize production throughput, and minimize energy consumption. However, making accurate data-driven machine speed forecasts is challenging. Given the complex nature of industrial manufacturing process data, predictive models that are robust to noise and can capture the temporal and spatial distributions of input time-series signals are prerequisites for accurate forecasting. Motivated by recent deep learning studies in smart manufacturing, in this article, we propose an end-to-end model for multistep machine speed prediction. The model comprises a deep convolutional LSTM encoder–decoder architecture. Extensive empirical analyses using real-world data obtained from a metal packaging plant in the United Kingdom demonstrate the value of the proposed method when compared with the state-of-the-art predictive models.

J. Wan, Baotong Chen, Shiyong Wang et al.

Due to the development of modern information technology, the emergence of the fog computing enhances equipment computational power and provides new solutions for traditional industrial applications. Generally, it is impossible to establish a quantitative energy-aware model with a smart meter for load balancing and scheduling optimization in smart factory. With the focus on complex energy consumption problems of manufacturing clusters, this paper proposes an energy-aware load balancing and scheduling (ELBS) method based on fog computing. First, an energy consumption model related to the workload is established on the fog node, and an optimization function aiming at the load balancing of manufacturing cluster is formulated. Then, the improved particle swarm optimization algorithm is used to obtain an optimal solution, and the priority for achieving tasks is built toward the manufacturing cluster. Finally, a multiagent system is introduced to achieve the distributed scheduling of manufacturing cluster. The proposed ELBS method is verified by experiments with candy packing line, and experimental results showed that proposed method provides optimal scheduling and load balancing for the mixing work robots.

Heiko Gebauer, M. Paiola, N. Saccani et al.

Abstract For over three decades now, several product companies around the world have been undertaking servitization paths. They have been devoting growing and substantial efforts to expand their service business. Expanding the service business in addition to their traditional core product business secures long-term growth and strengthens competitive advantages in business-to-business marketplaces. Recently, service business expansion has taken up many of the new digital technologies offered through the digital transformation. Thus, the servitization literature has progressed toward a dialogue on digital servitization . Against this background, the present article introduces the reader to this special issue. It first recalls key aspects of the emerging digital servitization discussion, and then depicts, through illustrative case studies, the growth paths utilized by industrial product companies when they take advantage of the digital servitization process. After discussing how the articles included in this special issue advance the literature, the article develops a number of directions for future research on digital servitization.

R. Muthuraj, C. Lacoste, P. Lacroix et al.

Abstract Building materials derived from agricultural and industrial waste are becoming more attractive in the civil engineering and architectural applications because of their sustainability and lower environmental impact. In addition, substantial value can be added to the wastes by producing value added products from them. Therefore, four different types of locally available by-products (rice husk, wheat husk, wood fibers and textile waste fibers) were used to produce composites with a biodegradable poly(butylene adipate-co-terephthalate)/poly(lactic acid) (PBAT/PLA) blend binder by hot pressing. The morphological analysis of the composites revealed that the PBAT/PLA binder had more affinity with wood and textile fibers than with wheat and rice husks. The prepared composites showed thermal stability until 250 °C. All the prepared biodegradable composites exhibited good compressive strength (11–40 MPa) and flexural strength (0.80–2.25 MPa). The observed mechanical properties allow easy handling without risk of breaking them when positioned in the buildings. The biodegradable composites were characterized for their thermal conductivity, diffusivity, effusivity and heat capacity. The density and thermal conductivity of the produced composite was in the range of 378–488 kg/m3 and 0.08-0.14 W/m.K, respectively. The least thermal conductivity i.e. 0.08 W/m.K was observed for the rice husk composite with a density of 378 kg/m3. A minimum water absorption (42%) was found in the rice husk composites after 24 h immersion in water. The composite samples were still cohesive after 24 h immersion in the water because of the water resistance nature of the binder. The prepared biodegradable composites meet most of the required properties for the indoor building insulation applications and show great potential to replace the conventional building material in current use.

Tom Maus, Stephan Frank, Tobias Glasmachers

Reinforcement learning (RL) is still rarely applied in industrial control, partly due to the difficulty of training reliable agents for real-world conditions. This work investigates how evolution strategies can support RL in such settings by introducing a continuous-control adaptation of an industrial sorting benchmark. The CMA-ES algorithm is used to generate high-quality demonstrations that warm-start RL agents. Results show that CMA-ES-guided initialization significantly improves stability and performance. Furthermore, the demonstration trajectories generated with the CMA-ES provide a strong oracle reference performance level, which is of interest in its own right. The study delivers a focused proof of concept for hybrid evolutionary-RL approaches and a basis for future, more complex industrial applications.

Ali Mohammad-Djafari

Digital Twins (DTs) are virtual representations of physical systems synchronized in real time through Internet of Things (IoT) sensors and computational models. In industrial applications, DTs enable predictive maintenance, fault diagnosis, and process optimization. This paper explores the mathematical foundations of DTs, hybrid modeling techniques, including Physics Informed Neural Networks (PINNs), and their implementation in industrial scenarios. We present key applications, computational tools, and future research directions.

Anwar Ahmed Khan, Shama Siddiqui, Indrakshi Dey

Managing delay is one of the core requirements of industrial automation applications due to the high risk associated for equipment and human lives. Using efficient Media Access Control (MAC) schemes guarantees the timely transmission of critical data, particularly in the industrial environments where heterogeneous data is inherently expected. This paper compares the performance of Fragmentation based MAC (FROG-MAC) against Fuzzy Priority Scheduling based MAC (FPS-MAC), both of which have been designed to optimize the performance of heterogenous wireless networks. Contiki has been used as a simulation platform and a single hop star topology has been assumed to resemble the industrial environment. It has been shown that FROG-MAC has the potential to outperform FPS-MAC in terms of energy efficiency and delay both, due to its inherent feature of interrupting ongoing lower priority transmission on the channel.

Di Wen, Kunyu Peng, Junwei Zheng et al.

Industrial workflows demand adaptive and trustworthy assistance that can operate under limited computing, connectivity, and strict privacy constraints. In this work, we present MICA (Multi-Agent Industrial Coordination Assistant), a perception-grounded and speech-interactive system that delivers real-time guidance for assembly, troubleshooting, part queries, and maintenance. MICA coordinates five role-specialized language agents, audited by a safety checker, to ensure accurate and compliant support. To achieve robust step understanding, we introduce Adaptive Step Fusion (ASF), which dynamically blends expert reasoning with online adaptation from natural speech feedback. Furthermore, we establish a new multi-agent coordination benchmark across representative task categories and propose evaluation metrics tailored to industrial assistance, enabling systematic comparison of different coordination topologies. Our experiments demonstrate that MICA consistently improves task success, reliability, and responsiveness over baseline structures, while remaining deployable on practical offline hardware. Together, these contributions highlight MICA as a step toward deployable, privacy-preserving multi-agent assistants for dynamic factory environments. The source code will be made publicly available at https://github.com/Kratos-Wen/MICA.

Alexander Windmann, Benedikt Stratmann, Mariya Lyashenko et al.

Industrial AI practitioners face vague robustness requirements in emerging regulations and standards but lack concrete, implementation ready protocols. This paper introduces the Industrial AI Robustness Card (IARC), a lightweight, task agnostic protocol for documenting and evaluating the robustness of AI models on industrial time series. The IARC specifies required fields and an empirical measurement and reporting protocol that combines drift monitoring, uncertainty quantification, and stress tests, and it maps these to relevant EU AI Act obligations. A soft sensor case study on a biopharmaceutical fermentation process illustrates how the IARC supports reproducible robustness evidence and continuous monitoring.

Edda Maria Capodaglio, Federica Amitrano, Armando Coccia et al.

Industrial wool textile production exposes workers mainly to the biomechanical loading of the shoulder joint. In this work context, which is characterized by poor machine ergonomics, exposure to biomechanical risk factors, and variable work organization, exoskeletons could facilitate work processes or could be a valuable means to protect workers from overuse injuries. Field evaluation is essential to verify the suitability of specific devices and their acceptance by users. As part of a pilot study, we examined the short-term subjective effects of a passive Arm-Support Exoskeleton (ASE) on workers performing repetitive overhead tasks. In a textile factory, eight workers participated in the study, answering questionnaires after carrying out a work session with (ASE) and without an exoskeleton (FREE). Participants had been using the Paexo exoskeleton for <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>4.2</mn><mo>±</mo><mn>5.8</mn></mrow></semantics></math></inline-formula> months (min 0–max 12). Subjective evaluations were collected regarding the workload (NASA-TLX) and relief (Borg’s CR10 scale) obtained from the use of the exoskeleton, satisfaction (Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST)), usability (System Usability Scale (SUS)), and opinions on the ergonomics of the device (Ergonomics questionnaire). Workers reported a high workload (NASA <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>7.2</mn><mo>±</mo><mn>1.0</mn></mrow></semantics></math></inline-formula>) and assessed a 46% reduction in effort on the CR10 in ASE conditions compared to FREE. They expressed high satisfaction with most characteristics of the ASE (100% satisfied with durability and effectiveness), high level of usability (62% of scores above 80, out of a maximum score of 100), and ergonomics of the device (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>88</mn><mo>±</mo><mn>12</mn></mrow></semantics></math></inline-formula>, out of a maximum score of 110). In addition to the objective effects (electromyography (EMG) reduction) already demonstrated in a previous publication, these qualitative results demonstrate a positive perception by textile workers regarding the effectiveness, usability, and suitability of the exoskeleton. The adoption of ASE in the textile industry appears beneficial in the short term, but the impact associated with individual variables and long-term effects remains to be explored.

Thomas Rosenstatter, Christian Schäfer, Olaf Saßnick et al.

As Industry 4.0 and the Industrial Internet of Things continue to advance, industrial control systems are increasingly adopting IT solutions, including communication standards and protocols. As these systems become more decentralized and interconnected, a critical need for enhanced security measures arises. Threat modeling is traditionally performed in structured brainstorming sessions involving domain and security experts. Such sessions, however, often fail to provide an exhaustive identification of assets and interfaces due to the lack of a systematic approach. This is a major issue, as it leads to poor threat modeling, resulting in insufficient mitigation strategies and, lastly, a flawed security architecture. We propose a method for the analysis of assets in industrial systems, with special focus on physical threats. Inspired by the ISO/OSI reference model, a systematic approach is introduced to help identify and classify asset interfaces. This results in an enriched system model of the asset, offering a comprehensive overview visually represented as an interface tree, thereby laying the foundation for subsequent threat modeling steps. To demonstrate the proposed method, the results of its application to a programmable logic controller (PLC) are presented. In support of this, a study involving a group of 12 security experts was conducted. Additionally, the study offers valuable insights into the experts' general perspectives and workflows on threat modeling.

Sheng Tian, Xintan Zeng, Yifei Hu et al.

Graph-based patterns are extensively employed and favored by practitioners within industrial companies due to their capacity to represent the behavioral attributes and topological relationships among users, thereby offering enhanced interpretability in comparison to black-box models commonly utilized for classification and recognition tasks. For instance, within the scenario of transaction risk management, a graph pattern that is characteristic of a particular risk category can be readily employed to discern transactions fraught with risk, delineate networks of criminal activity, or investigate the methodologies employed by fraudsters. Nonetheless, graph data in industrial settings is often characterized by its massive scale, encompassing data sets with millions or even billions of nodes, making the manual extraction of graph patterns not only labor-intensive but also necessitating specialized knowledge in particular domains of risk. Moreover, existing methodologies for mining graph patterns encounter significant obstacles when tasked with analyzing large-scale attributed graphs. In this work, we introduce GraphRPM, an industry-purpose parallel and distributed risk pattern mining framework on large attributed graphs. The framework incorporates a novel edge-involved graph isomorphism network alongside optimized operations for parallel graph computation, which collectively contribute to a considerable reduction in computational complexity and resource expenditure. Moreover, the intelligent filtration of efficacious risky graph patterns is facilitated by the proposed evaluation metrics. Comprehensive experimental evaluations conducted on real-world datasets of varying sizes substantiate the capability of GraphRPM to adeptly address the challenges inherent in mining patterns from large-scale industrial attributed graphs, thereby underscoring its substantial value for industrial deployment.

Tanureza Jaya, Benjamin Michalak, Marcel Radke et al.

Cabling tasks (pulling, clipping, and plug insertion) are today mostly manual work, limiting the cost-effectiveness of electrification. Feasibility for the robotic grasping and insertion of plugs, as well as the manipulation of cables, have been shown in research settings. However, in many industrial tasks the complete process from picking, insertion, routing, and validation must be solved with one system. This often means the cable must be directly manipulated for routing, and the plug must be manipulated for insertion, often in cluttered environments with tight space constraints. Here we introduce an analysis of the complete industrial cabling tasks and demonstrate a solution from grasp, plug insertion, clipping, and final plug insertion. Industrial requirements are summarized, considering the space limitations, tolerances, and possible ways that the cabling process can be integrated into the production process. This paper proposes gripper designs and general robotic assembly methods for the widely used FASTON and a cubical industrial connector. The proposed methods cover the cable gripping, handling, routing, and inserting processes of the connector. Customized grippers are designed to ensure the reliable gripping of the plugs and the pulling and manipulation of the cable segments. A passive component to correct the cable orientation is proposed, allowing the robot to re-grip the plug before insertion. In general, the proposed method can perform cable assembly with mere position control, foregoing complex control approaches. This solution is demonstrated with an industrial product with realistic space requirements and tolerances, identifying difficult aspects of current cabling scenarios and potential to improve the automation-friendliness in the product design.

Yuxuan Lin, Yang Chang, Xuan Tong et al.

In the advancement of industrial informatization, unsupervised anomaly detection technology effectively overcomes the scarcity of abnormal samples and significantly enhances the automation and reliability of smart manufacturing. As an important branch, industrial image anomaly detection focuses on automatically identifying visual anomalies in industrial scenarios (such as product surface defects, assembly errors, and equipment appearance anomalies) through computer vision techniques. With the rapid development of Unsupervised industrial Image Anomaly Detection (UIAD), excellent detection performance has been achieved not only in RGB setting but also in 3D and multimodal (RGB and 3D) settings. However, existing surveys primarily focus on UIAD tasks in RGB setting, with little discussion in 3D and multimodal settings. To address this gap, this artical provides a comprehensive review of UIAD tasks in the three modal settings. Specifically, we first introduce the task concept and process of UIAD. We then overview the research on UIAD in three modal settings (RGB, 3D, and multimodal), including datasets and methods, and review multimodal feature fusion strategies in multimodal setting. Finally, we summarize the main challenges faced by UIAD tasks in the three modal settings, and offer insights into future development directions, aiming to provide researchers with a comprehensive reference and offer new perspectives for the advancement of industrial informatization. Corresponding resources are available at https://github.com/Sunny5250/Awesome-Multi-Setting-UIAD.

Ali Beikmohammadi, Mohammad Hosein Hamian, Neda Khoeyniha et al.

The rapid influx of data-driven models into the industrial sector has been facilitated by the proliferation of sensor technology, enabling the collection of vast quantities of data. However, leveraging these models for failure detection and prognosis poses significant challenges, including issues like missing values and class imbalances. Moreover, the cost sensitivity associated with industrial operations further complicates the application of conventional models in this context. This paper introduces a novel cost-sensitive transformer model developed as part of a systematic workflow, which also integrates a hybrid resampler and a regression-based imputer. After subjecting our approach to rigorous testing using the APS failure dataset from Scania trucks and the SECOM dataset, we observed a substantial enhancement in performance compared to state-of-the-art methods. Moreover, we conduct an ablation study to analyze the contributions of different components in our proposed method. Our findings highlight the potential of our method in addressing the unique challenges of failure prediction in industrial settings, thereby contributing to enhanced reliability and efficiency in industrial operations.