A next-generation IoT-based collaborative framework for electronics assembly

Krishnamurthy, Rajesh; Cecil, J.

doi:10.1007/s00170-017-1561-x

Cited by 27 publications

(11 citation statements)

References 39 publications

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“…Such infrastructure will enable the smart homes and lean/smart manufacturing by deploying huge amounts of sensors under the internet of things (IoTs) framework to realize real-time sensory information collection, data management and analysis 1 . In the future, the combination of artificial intelligence (AI) technology with the IoT technology will lead to AI of things (AIoT)-based living, working, and manufacturing environment, which provides efficient IoT operations, improved human–machine interactions and better capability of decision-making with respect to a complicated and dynamic system 2 , 3 . With the aid of AIoT, computation capacity, and ubiquitous sensory information, digital twin is proposed to be a digital copy of the physical system, i.e., a cyber-physical system, to perform real-time control and optimization of products and production lines, because the required time of getting an optimized solution from cloud server reduces to an ignorable level 4 .…”

Section: Introductionmentioning

confidence: 99%

Triboelectric nanogenerator sensors for soft robotics aiming at digital twin applications

Jin¹,

Sun²,

Li³

et al. 2020

Nat Commun

480

266

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Designing efficient sensors for soft robotics aiming at human machine interaction remains a challenge. Here, we report a smart soft-robotic gripper system based on triboelectric nanogenerator sensors to capture the continuous motion and tactile information for soft gripper. With the special distributed electrodes, the tactile sensor can perceive the contact position and area of external stimuli. The gear-based length sensor with a stretchable strip allows the continuous detection of elongation via the sequential contact of each tooth. The triboelectric sensory information collected during the operation of soft gripper is further trained by support vector machine algorithm to identify diverse objects with an accuracy of 98.1%. Demonstration of digital twin applications, which show the object identification and duplicate robotic manipulation in virtual environment according to the real-time operation of the soft-robotic gripper system, is successfully created for virtual assembly lines and unmanned warehouse applications.

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Section: Introductionmentioning

confidence: 99%

Triboelectric nanogenerator sensors for soft robotics aiming at digital twin applications

Jin¹,

Sun²,

Li³

et al. 2020

Nat Commun

480

266

View full text Add to dashboard Cite

show abstract

“…In terms of the advanced data interpretation, the new era of ML from AI paves the way for strengthening the functionalities of sensors with AI‐enhanced data analytic. [ 104 , 105 , 106 ] Together with the AIoT network, more comprehensive sensory information can be extracted for more diversified applications in the wearable area. Kim et al.…”

Section: Introductionmentioning

confidence: 99%

“…[78] In terms of the advanced data interpretation, the new era of ML from AI paves the way for strengthening the functionalities of sensors with AI-enhanced data analytic. [104][105][106] Together with the AIoT network, more comprehensive sensory information can be extracted for more diversified applications in the wearable area. Kim et al proposed a novel electronic skin sensor attached to the human wrist, which can real-time decode the complex motion of five fingers with a deep neural network-enabled rapid situation learning.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence of Things (AIoT) Enabled Virtual Shop Applications Using Self‐Powered Sensor Enhanced Soft Robotic Manipulator

et al. 2021

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Rapid advancements of artificial intelligence of things (AIoT) technology pave the way for developing a digital-twin-based remote interactive system for advanced robotic-enabled industrial automation and virtual shopping. The embedded multifunctional perception system is urged for better interaction and user experience. To realize such a system, a smart soft robotic manipulator is presented that consists of a triboelectric nanogenerator tactile (T-TENG) and length (L-TENG) sensor, as well as a poly(vinylidene fluoride) (PVDF) pyroelectric temperature sensor. With the aid of machine learning (ML) for data processing, the fusion of the T-TENG and L-TENG sensors can realize the automatic recognition of the grasped objects with the accuracy of 97.143% for 28 different shapes of objects, while the temperature distribution can also be obtained through the pyroelectric sensor. By leveraging the IoT and artificial intelligence (AI) analytics, a digital-twin-based virtual shop is successfully implemented to provide the users with real-time feedback about the details of the product. In general, by offering a more immersive experience in human-machine interactions, the proposed remote interactive system shows the great potential of being the advanced human-machine interface for the applications of the unmanned working space.

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“…Krishnamurthy and Cecil [22] designed and developed an IoT-based CPS for PCB assembly. The outputs from a virtual reality-based simulation was compared and analyzed against the production data of the physical shop floors provided from sensors and cameras to facilitate agile product and process design.…”

Section: Introductionmentioning

confidence: 99%

An IoT-Based Cyber-Physical Framework for Turbine Assembly Systems

Wang

Zhang

2020

IEEE Access

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Turbines are typical engineering-to-order products which can be highly customized. This paper considers the final assembly control under uncertainty in turbine assembly workstations. A cyber-physical framework based on Internet of Things (IoT) is proposed for the turbine assembly, which consists of physical components, cyber components and an IoT-based monitoring system. The IoT-based monitoring modules capture real-time data of the physical components, including the workers, tools, parts and the actual assembly process. These cyber modules can generate the original scheduling for the assembly tasks, re-sequence the assembly tasks, reassign the workers and control the logistics when an unexpected event occurs. Physical activities are undertaken in a turbine assembly workstation based on assembly instructions and guidance from the cyber components. The proposed framework can be implemented in the large turbine assembly system, which can facilitate real-time information driven assembly process monitoring and collaborative control of the task sequence, the worker assignments and logistics in a closed-loop environment. The experimental results show that our method can significantly improve the quality and the efficiency of a turbine assembly system. INDEX TERMS Internet of Things, cyber-physical systems, engineering-to-order, production control, assembly process.

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A next-generation IoT-based collaborative framework for electronics assembly

Cited by 27 publications

References 39 publications

Triboelectric nanogenerator sensors for soft robotics aiming at digital twin applications

Triboelectric nanogenerator sensors for soft robotics aiming at digital twin applications

Artificial Intelligence of Things (AIoT) Enabled Virtual Shop Applications Using Self‐Powered Sensor Enhanced Soft Robotic Manipulator

An IoT-Based Cyber-Physical Framework for Turbine Assembly Systems

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