Implementation and Manufacturing of DT Sensor Ecosystem for Real-Time Monitoring of Virtual 3D Printers

Reddy, K. Shyam Sunder; Rajesh, R.; Raj, P. Anantha Christu; Arya, Namita; Bhaskaran, R.; Prasad, J. Laxmi

doi:10.1007/s42979-023-01969-w

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…Castelló et al [100] emphasize an innovative digital twin for modeling materials which simplifies the composite part creation process using large-format additive manufacturing (LFAM). For the same category of process, an in-depth analysis was carried out by Reddy et al [101] to examine the application of DT in 3D printers, and to present the recommended framework and Kabaldin et al [96] created a system to enable 3D printing using arc welding within CNC machines. This system offers the possibility of connecting high-speed computing modules and training neural networks with feedback.…”

Section: Process Variabelsmentioning

confidence: 99%

“…Castelló et al [100] emphasize an innovative digital twin for modeling materials which simplifies the composite part creation process using large-format additive manufacturing (LFAM). For the same category of process, an indepth analysis was carried out by Reddy et al [101] to examine the application of DT in 3D printers, and to present the recommended framework and method. For example, the process of updating the digital model is time-consuming, and there is a noticeable delay in results at the interaction layer level.…”

Section: Process Variabelsmentioning

confidence: 99%

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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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Section: Process Variabelsmentioning

confidence: 99%

“…Castelló et al [100] emphasize an innovative digital twin for modeling materials which simplifies the composite part creation process using large-format additive manufacturing (LFAM). For the same category of process, an indepth analysis was carried out by Reddy et al [101] to examine the application of DT in 3D printers, and to present the recommended framework and method. For example, the process of updating the digital model is time-consuming, and there is a noticeable delay in results at the interaction layer level.…”

Section: Process Variabelsmentioning

confidence: 99%