Conceiving a Digital Twin for a Flexible Manufacturing System

Digitization and virtualization represent key factors in the era of Industry 4.0. Digital twins (DT) can certainly contribute to increasing the efficiency of various productive sectors as they can contribute to monitoring, managing, and improvement of a product or process throughout its life cycle. Although several works deal with DTs, there are gaps regarding the use of this technology when a Flexible Manufacturing System (FMS) is used. Existing work, for the most part, is concerned with simulating the progress of manufacturing without providing key production data in real-time. Still, most of the solutions presented in the literature are relatively expensive and may be difficult to implement in most companies, due to their complexity. In this work, the digital twin of an FMS is conceived. The specific module of an ERP (Enterprise Resources Planning) system is used to digitize the physical entity. Production data is entered according to tryouts performed in the FMS. Sensors installed in the main components of the FMS, CNC (computer numerical control) lathe, robotic arm, and pallet conveyor send information in real-time to the digital entity. The results show that simulations using the digital twin present very satisfactory results compared to the physical entity. In time, information such as production rate, queue management, feedstock, equipment, and pallet status can be easily accessed by operators and managers at any time during the production process, confirming the MES (manufacture execution system) efficiency. The low-cost hardware and software used in this work showed its feasibility. The DT created represents the initial step towards designing a metaverse solution for the manufacturing unit in question, which should operate in the near future as a smart and autonomous factory model.Thanks are due to Elkartek 2022 project LANVERSO, and in some sections (simulations) to Basque government university group IT 1573-22

Developing sensor signal-based digital twins for intelligent machine tools

Abstract Digital twins can assist machine tools in performing their monitoring and troubleshooting tasks autonomously from the context of smart manufacturing. For this, a special type of twin denoted as sensor signal-based twin must be constructed and adapted into the cyber-physical systems. The twin must (1) machine-learn the required knowledge from the historical sensor signal datasets, (2) seamlessly interact with the real-time sensor signals, (3) handle the semantically annotated datasets stored in clouds, and (4) accommodate the data transmission delay. The development of such twins has not yet been studied in detail. This study fills this gap by addressing sensor signal-based digital twin development for intelligent machine tools. Two computerized systems denoted as Digital Twin Construction System (DTCS) and Digital Twin Adaptation System (DTAS) are proposed to construct and adapt the twin, respectively. The modular architectures of the proposed DTCS and DTAS are presented in detail. The real-time responses and delay-related computational arrangements are also elucidated for both systems. The systems are also developed using a Java™-based platform. Milling torque signals are used as an example to demonstrate the efficacy of DTCS and DTAS. This study thus contributes toward the advancement of intelligent machine tools from the context of smart manufacturing

Stress Corrosion Cracking of Stainless Steel 316L Additively Manufactured using Sinter-based and Laser-based Technologies

Stress corrosion cracking (SCC) poses a risk to SS316L components. The solution is widely agreed to be the development of new materials or innovative manufacturing processes that create unique microstructures to improve SCC resistance. 3D printing technologies, especially Laser Powder Bed Fusion and Sinter-based Material Extrusion, are seen as promising pathways for achieving this goal. This research aims to investigate the impact of process parameters on printed microstructures to develop enhanced alloys for SCC resistanc

Advances in the Field of Electrical Machines and Drives

Electrical machines and drives dominate our everyday lives. This is due to their numerous applications in industry, power production, home appliances, and transportation systems such as electric and hybrid electric vehicles, ships, and aircrafts. Their development follows rapid advances in science, engineering, and technology. Researchers around the world are extensively investigating electrical machines and drives because of their reliability, efficiency, performance, and fault-tolerant structure. In particular, there is a focus on the importance of utilizing these new trends in technology for energy saving and reducing greenhouse gas emissions. This Special Issue will provide the platform for researchers to present their recent work on advances in the field of electrical machines and drives, including special machines and their applications; new materials, including the insulation of electrical machines; new trends in diagnostics and condition monitoring; power electronics, control schemes, and algorithms for electrical drives; new topologies; and innovative applications

A 5G Automated Guided Vehicle SME testbed for resilient future factories

Factory automation design engineers building the Smart Factory can use wireless 5G broadband networks for added design flexibility. 5G New Radio builds upon previous cellular communications standards to include technology for “massive machine-type communication” and “ultra-reliable and low-latency communication”. In this work, the authors augment an automated guided vehicle with 5G for additional capabilities (e.g., streaming high-resolution video and enabling long-distance teleoperation), increasing the mobile applications for industrial equipment. Such use cases will provide valuable knowledge to engineers examining 5G for novel smart manufacturing solutions. Our 5G private network testbed is a platform for wireless performance research in industrial locations and provides a development mule for flexible smart manufacturing systems. The rival wireless technology to 5G in industrial settings is Wi-Fi and it is included in the testbed. Furthermore, the authors noted challenges, often unconsidered, facing the move to digital manufacturing technologies. Therefore, the authors summarise the emerging challenges when implementing new digital factory systems, including challenges linked to societal concerns around sustainability and supply chain resilience. The new Smart Factory technologies, including 5G communications, will have their roles to play in alleviating these challenges and ensuring economies have resilient future factories

Micro- and Nanostructured Microfluidic Devices for Localized Protein Immobilization and Other Biomedical Applications

A new immobilization method for the localized adsorption of proteins on thermoplastic surfaces is introduced. Artificial three-phase interfaces were realized by surface structuring to control the wetting behavior which lead to a preferred adsorption in these modified areas. Additionally, different fabrication methods were analyzed to determine mass fabrication capabilities. These fabrication methods also allowed the production of fully structured microchannels to tune the fluids behavior within

Research and Technology

Langley Research Center is engaged in the basic an applied research necessary for the advancement of aeronautics and space flight, generating advanced concepts for the accomplishment of related national goals, and provding research advice, technological support, and assistance to other NASA installations, other government agencies, and industry. Highlights of major accomplishments and applications are presented

Digital twin of construction crane and realization of the physical to virtual connection

Digital twin is an integrated multi-physics representation of a complex physical entity. This article constructs the digital twin of the construction crane, proposes a framework for the construction of the tower crane digital twin, and realizes the connection from physical to virtual in the concept of digital twin. The main contributions are divided into three parts: development of tower crane monitoring dataset, tower crane detection and tower crane operation mode recognition. By using labellmg to annotate more than 20,000 tower crane images in 583 tower crane videos, a tower crane image recognition dataset and a tower crane operating mode dataset are established. Yolov5x algorithm is selected in the tower crane detection. Edge extraction is used to improve the quality of the raw dataset and distance-intersection-over union non-maximum suppression is used to replace the traditional non-maximum suppression part in the Yolov5x algorithm to improve the detect accuracy when some tower cranes are overlapping. The final test set detection accuracy rate is 93.85%. After comparing the LSTM and CNN algorithms, 3DResNet algorithm is selected for tower crane operational mode recognition. The raw dataset is augmented by rotating the image by ±10° and ±20°, and the augmented dataset enlarges five times. Using these methods, the final recognition accuracy of tower crane operation mode reaches 87%. These models can be installed on the cctv to monitor the running status of the tower crane in real time and transfer relevant information to the virtual model. The tower crane in the virtual space completes the action of the physical tower crane, thereby realizing the physical-to-virtual mapping in the digital twin