Fakultät 08 / Institut für Produktion
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- Digital Triplet (1)
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As Digital Twins gain more traction and their adoption in industry increases, there is a need to integrate such technology with machine learning features to enhance functionality and enable decision making tasks. This has lead to the emergence of a concept known as Digital Triplet; an enhancement of Digital Twin technology through the addition of an ’intelligent activity layer’. This is a relatively new technology in Industrie 4.0 and research efforts are geared towards exploring its applicability, development and testing of means for implementation and quick adoption. This paper presents the design and implementation of a Digital Triplet for a three-floor elevator system. It demonstrates the integration of a machine learning (ML) object detection model and the system Digital Twin. This was done to introduce an additional security feature that enabled the system to make a decision, based on objects detected and take preliminary security measures. The virtual model was designed in Siemens NX and programmed via Total Integrated Automation (TIA) portal software. The corresponding physical model was fabricated and controlled using a Programmable Logic Controller (PLC) S7 1200. A control program was developed to mimic the general operations of a typical elevator system used in a commercial building setting. Communication, between the physical and virtual models, was enabled using the OPC-Unified Architecture (OPC-UA) protocol. Object recognition using “You only look once” (YOLOV3) based machine learning algorithm was incorporated. The Digital Triplet’s functionality was tested, ensuring the virtual system duplicated actual operations of the physical counterpart through the use of sensor data. Performance testing was done to determine the impact of the ML module on the real-time functionality aspect of the system. Experiment results showed the object recognition contributed an average of 1.083s to an overall signal travel time of 1.338 s.
Metallic tubular micro-components play an important role in a broad range of products,
from industrial microsystem technology, such as medical engineering, electronics and optoelectronics, to sensor technology or microfluidics. The demand for such components is increasing, and forming processes can present a number of advantages for industrial manufacturing. These include, for example, a high productivity, enhanced shaping possibilities, applicability of a wide spectrum of materials and the possibility to produce parts with a high stiffness and strength. However, certain difficulties arise as a result of scaling down conventional tube forming processes to the microscale. These include not only the influence of the known size effects on material and friction behavior, but also constraints in the feasible miniaturization of forming tools. Extensive research work has been conducted over the past few years on micro-tube forming techniques, which deal with the development of novel and optimized processes, to counteract these restrictions. This paper reviews the relevant advances in micro-tube fabrication and shaping. A particular focus is enhancement in forming possibilities, accuracy and obtained component characteristics, presented in the reviewed research work. Furthermore, achievements in severe plastic deformation for micro-tube generation and in micro-tube testing methods are discussed.