Real-Time Object Detection for Smart Connected Worker in 3D Printing

Bian, Shijie; Lin, Tiancheng; Li, Chen; Fu, Yongwei; Jiang, Mengrui; Wu, Tongzi; Hang, Xiyi; Li, Bingbing

doi:10.1007/978-3-030-77970-2_42

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…Lemos et al used deep learning-based object detection techniques to enhance the adaptability and effectiveness of robotic manipulators for parts identification in additive manufacturing [12]. Leveraging the processing speed of object detection models, Bian et al developed a real-time system that could monitor machine states and perform automatic fault detection on 3D printers [13].…”

Section: Related Workmentioning

confidence: 99%

Affordable Artificial Intelligence-Assisted Machine Supervision System for the Small and Medium-Sized Manufacturers

Bian

et al. 2022

Sensors

Self Cite

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With the rapid concurrent advance of artificial intelligence (AI) and Internet of Things (IoT) technology, manufacturing environments are being upgraded or equipped with a smart and connected infrastructure that empowers workers and supervisors to optimize manufacturing workflow and processes for improved energy efficiency, equipment reliability, quality, safety, and productivity. This challenges capital cost and complexity for many small and medium-sized manufacturers (SMMs) who heavily rely on people to supervise manufacturing processes and facilities. This research aims to create an affordable, scalable, accessible, and portable (ASAP) solution to automate the supervision of manufacturing processes. The proposed approach seeks to reduce the cost and complexity of smart manufacturing deployment for SMMs through the deployment of consumer-grade electronics and a novel AI development methodology. The proposed system, AI-assisted Machine Supervision (AIMS), provides SMMs with two major subsystems: direct machine monitoring (DMM) and human-machine interaction monitoring (HIM). The AIMS system was evaluated and validated with a case study in 3D printing through the affordable AI accelerator solution of the vision processing unit (VPU).

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Section: Related Workmentioning

confidence: 99%

Affordable Artificial Intelligence-Assisted Machine Supervision System for the Small and Medium-Sized Manufacturers

Bian

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Lemos et al used deep learning-based object detection techniques to enhance the adaptability and effectiveness of robotic manipulators for parts identification in additive manufacturing [19]. Leveraging the processing speed of object detection models, Bian et al developed a real-time system that could monitor machine states and perform automatic fault detection on 3D printers [5].…”

Section: Introductionmentioning

confidence: 99%

Affordable Artificial Intelligence-assisted Machine Supervision System for the Small and Medium-sized Manufacturers

Bian

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

With the rapid concurrent advance of artificial intelligence (AI) and Internet of Things (IoT) technology, manufacturing environments are being upgraded or equipped with smart and connected infrastructure that empower workers and supervisors to optimize manufacturing workflow and processes for improved energy efficiency, equipment reliability, quality, safety, and productivity. For many small and medium-sized manufacturers (SMMs) who heavily rely on people to supervise manufacturing processes and facilities, this presents a challenge of capital cost and complexity. This research aims to create an affordable, scalable, accessible, and portable (ASAP) solution to automate supervision of manufacturing processes. The proposed approach seeks to reduce the cost and complexity of smart manufacturing deployment for SMMs through deployment of consumer grade electronics and novel AI development methodology. The proposed system, AI-assisted Machine Supervision (AIMS), provides SMMs with two major subsystems: direct machine monitoring (DMM), and human-machine interaction monitoring (HIM). The AIMS system was evaluated and validated with a case study in 3D printing through the affordable AI accelerator solution of the vision processing unit (VPU).

show abstract

Real-Time Object Detection for Smart Connected Worker in 3D Printing

Cited by 2 publications

References 19 publications

Affordable Artificial Intelligence-Assisted Machine Supervision System for the Small and Medium-Sized Manufacturers

Affordable Artificial Intelligence-Assisted Machine Supervision System for the Small and Medium-Sized Manufacturers

Affordable Artificial Intelligence-assisted Machine Supervision System for the Small and Medium-sized Manufacturers

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