Integrating Machine Learning Model and Digital Twin System for Additive Manufacturing

Jyeniskhan, Nursultan; Keutayeva, Aigerim; Kazbek, Gani; Ali, Md Hazrat; Shehab, Essam

doi:10.1109/access.2023.3294486

Cited by 14 publications

(3 citation statements)

References 27 publications

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“…In a recent study, an ML model was paired with a digital twin to study defect formation and user input adjustment before production. ML could aid in data cleaning, improve the accuracy of parameter applications to parts, and demonstrate the effectiveness of AM processes [146]. Since AM processing parameters and stress evolutions are a major concern in manufacturing W, digital twins can serve as a marvelous tool in modeling material behavior.…”

Section: And Modeling Of Am-prepared Materialsmentioning

confidence: 99%

Progress and Challenges of Additive Manufacturing of Tungsten and Alloys as Plasma-Facing Materials

Howard,

Parker,

2024

Materials

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Tungsten (W) and W alloys are considered as primary candidates for plasma-facing components (PFCs) that must perform in severe environments in terms of temperature, neutron fluxes, plasma effects, and irradiation bombardment. These materials are notoriously difficult to produce using additive manufacturing (AM) methods due to issues inherent to these techniques. The progress on applying AM techniques to W-based PFC applications is reviewed and the technical issues in selected manufacturing methods are discussed in this review. Specifically, we focus on the recent development and applications of laser powder bed fusion (LPBF), electron beam melting (EBM), and direct energy deposition (DED) in W materials due to their abilities to preserve the properties of W as potential PFCs. Additionally, the existing literature on irradiation effects on W and W alloys is surveyed, with possible solutions to those issues therein addressed. Finally, the gaps in possible future research on additively manufactured W are identified and outlined.

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Section: And Modeling Of Am-prepared Materialsmentioning

confidence: 99%

Progress and Challenges of Additive Manufacturing of Tungsten and Alloys as Plasma-Facing Materials

Howard,

Parker,

2024

Materials

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“…This enables proactive identification of potential issues, process optimization, and control of the AM process in real time. Machine learning and the Internet of Things (IoT) [84,85] are crucial in the hierarchy of building a DT for the AM process [86,87]. IoT devices and sensors collect real-time data from the AM process, capturing parameters, performance, and environmental conditions [14,88].…”

Section: Real-time Surveillance and Managementmentioning

confidence: 99%

Digital Twin Implementation in Additive Manufacturing: A Comprehensive Review

Ben Amor,

Elloumi,

Eltaief

et al. 2024

Processes

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The additive manufacturing (AM) field is rapidly expanding, attracting significant scientific attention. This family of processes will be widely used in the evolution of Industry 4.0, particularly in the production of customized components. However, as the complexity and variability of additive manufacturing processes increase, there is an increasing need for advanced techniques to ensure quality control, optimize performance, and reduce production costs. Multiple tests are required to optimize processing variables for specific equipment and processes, to achieve optimum processing conditions. The application of digital twins (DTs) has significantly enhanced the field of additive manufacturing. A digital twin, abbreviated as DT, refers to a computer-generated model that accurately depicts a real-world object, system, or process. A DT comprises the complete additive manufacturing process, from the initial conception phase to the final manufacturing phase. It enables the manufacturing process to be continuously monitored, studied, and optimized in real time. DT has emerged as an important tool in the additive manufacturing industry. They allow manufacturers to enhance the process, improve product quality, decrease costs, and accelerate innovation. However, the development of DT in AM is an iterative and continuous process. It requires collaboration between domain experts, data scientists, engineers, and manufacturing teams to guarantee an accurate representation of the process by the digital twin. This paper aims to provide a comprehensive analysis of the current state of DT for additive manufacturing, examining their applications, benefits, challenges, and future directions.

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“…Digital twins are widely used in manufacturing and other industries due to their advantages of dynamic consistency [3], virtualreal interactive feedback [4][5], and broad application prospects [6]. However, in the digital twin system for industrial Internet, there are still many problems in realtime information interaction [7], virtual-reality synchronisation [8][9] and mutual control [10], etc. Exploring and researching these key technologies is of great significance for the digitalization and intelligence of industrial equipment [11][12].…”

Section: Introductionmentioning

confidence: 99%

Establishment of Web-Based Digital Twin System for Truss Gantry Crane

Li,

Wang,

Fan

et al. 2023

IEEE Access

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The concept of digital twin intends to establish a real-time mapping between physical and virtual spaces, which individuals can use for real-time monitoring and remote control of product operation. This paper presents a web-based truss gantry crane digital twin system. It elucidates the gradual design process of a web-based digital twin system for analyzing the structural performance of heavy equipment. The construction of a six-layer architectural framework for the digital twin system involves an analysis of its components and motion relationships. Sensors perform data collection to provide data support for real-time mapping of digital space to physical space. The setup of an Express server and the integration of routing files accomplish the data collection, transmission, and processing of the communication system, thereby avoiding a decrease in operational efficiency due to data read and write operations and making full use of the server's capabilities. Use Chrome V8 engine to build 3D visualization space based on HTML and Three.js. Users only need to install a browser to log in to the system at any time and any place to carry out the corresponding operations. Upgrade and maintenance of the system is realized through the operation of the corresponding server, which greatly reduces the maintenance cost.

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Integrating Machine Learning Model and Digital Twin System for Additive Manufacturing

Cited by 14 publications

References 27 publications

Progress and Challenges of Additive Manufacturing of Tungsten and Alloys as Plasma-Facing Materials

Progress and Challenges of Additive Manufacturing of Tungsten and Alloys as Plasma-Facing Materials

Digital Twin Implementation in Additive Manufacturing: A Comprehensive Review

Establishment of Web-Based Digital Twin System for Truss Gantry Crane

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