Digital Twin for Human-Robot Interactive Welding and Welder Behavior Analysis

Wang, Qiyue; Jiao, Wenhua; Wang, Peng; Zhang, Yuming

doi:10.1109/jas.2020.1003518

Cited by 112 publications

(47 citation statements)

References 37 publications

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“…In unsupervised learning methods, there is no labeling of data required, and the model is expected to infer patterns from the unlabeled input data. Clustering algorithms, such as principle component analysis (PCA) [ 260 , 274 ] and k-means methods [ 205 ] and generative models using generative adversarial network (GAN) [ 137 , 150 ] and variational autoencoders (VAE) [ 150 ] all use unlabeled data at the training stage, thus falling into the category of unsupervised learning. One of the challenges in applying unsupervised learning methods is that the number of clusters is normally not known a priori.…”

Section: Discussionmentioning

confidence: 99%

“…Other supervised learning applications, such as visual question answering for the HMC system, are included in Table 4 [ 271 , 272 , 273 ]. A supervised/unsupervised learning example is [ 274 ], where Wang et al used FFT–PCA–SVM–based DT for human–robot interactive welding and welder behavior analysis. In [ 275 ], Lv et al proposed a reinforcement learning–based DT for improving medical equipment assembly efficiency during COVID-19.…”

Section: Advanced Roboticsmentioning

confidence: 99%

“…Table 4 summarizes AI-equipped DTs and categorizes them based on learning algorithms used and subfields, such as control (detailed in Section 3.2), planning (detailed in Section 3.3), and HRI/HRC (detailed in Section 3.4). ANN Enabling industrial robots to bypass obstacles [272] LSTM Visual question answering for HMC system [273] Supervised and Unsupervised Learning FFT-PCA-SVM HRI welding and welder behavior analysis (identifying the professional level) [274] Reinforcement Learning DDPG COVID-19, improve efficiency in assembling medical equipment [275] Predictive Maintenance Supervised Learning DNN System health monitoring [276] Maximizing the overall plant availability of modern manufacturing systems [277] Workspace Modeling Supervised Learning Monte Carlo method Simulating the workspace of the mechanisms [278] Others Supervised Learning RF Estimation of lawn grass lengths for robotic lawn mower [279] PCA: Principal Component Analysis; GD: Gradient Descent; QP: Quadratic Programming; MACG: MultiAgent Computational Guidance; FFT: Fast Fourier Transform; UAV: Unmanned Aerial Vehicle.…”

Section: Overviewmentioning

confidence: 99%

See 2 more Smart Citations

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Huang

Shen²,

et al. 2021

Sensors

153

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Digital twin (DT) and artificial intelligence (AI) technologies have grown rapidly in recent years and are considered by both academia and industry to be key enablers for Industry 4.0. As a digital replica of a physical entity, the basis of DT is the infrastructure and data, the core is the algorithm and model, and the application is the software and service. The grounding of DT and AI in industrial sectors is even more dependent on the systematic and in-depth integration of domain-specific expertise. This survey comprehensively reviews over 300 manuscripts on AI-driven DT technologies of Industry 4.0 used over the past five years and summarizes their general developments and the current state of AI-integration in the fields of smart manufacturing and advanced robotics. These cover conventional sophisticated metal machining and industrial automation as well as emerging techniques, such as 3D printing and human–robot interaction/cooperation. Furthermore, advantages of AI-driven DTs in the context of sustainable development are elaborated. Practical challenges and development prospects of AI-driven DTs are discussed with a respective focus on different levels. A route for AI-integration in multiscale/fidelity DTs with multiscale/fidelity data sources in Industry 4.0 is outlined.

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Section: Discussionmentioning

confidence: 99%

Section: Advanced Roboticsmentioning

confidence: 99%

Section: Overviewmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Huang

Shen²,

et al. 2021

Sensors

153

View full text Add to dashboard Cite

show abstract

“…Finally, some authors develop a digital twin with the aim of improving the efficiency of the HRC industrial processes. For instance, Wang et al create in [129] a digital twin (DT) for human-robot interactive welding that includes the robot, the operator, and the welding scene in order to replicate the welding operations and analyze the welder behaviors after a welding skill level classification from demonstrated operation data. To this intent, a combination of fast Fourier transform (FFT), principal component analysis (PCA), and support vector machine (SVM) is proposed by the authors.…”

Section: B Schedulingmentioning

confidence: 99%

Control Techniques for Safe, Ergonomic, and Efficient Human-Robot Collaboration in the Digital Industry: A Survey

Proia

Carli

Cavone

et al. 2022

IEEE Trans. Automat. Sci. Eng.

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The fourth industrial revolution, also known as Industry 4.0, is reshaping the way individuals live and work while providing a substantial influence on the manufacturing scenario.The key enabling technology that has made Industry 4.0 a concrete reality is without doubt collaborative robotics, which is also evolving as a fundamental pillar of the next revolution, the so-called Industry 5.0. The improvement of employees' safety and well-being, together with the increase of profitability and productivity, are indeed the main goals of human-robot collaboration (HRC) in the industrial setting. The robotic controller design and the analysis of existing decision and control techniques are crucially needed to develop innovative models and state-of-the-art methodologies for a safe, ergonomic, and efficient HRC. To this aim, this paper presents an accurate review of the most recent and relevant contributions to the related literature, focusing on the control perspective. All the surveyed works are carefully selected and categorized by target (i.e., safety, ergonomics, and efficiency), and then by problem and type of control, in presence or absence of optimization. Finally, the discussion of the achieved results and the analysis of the emerging challenges in this research field are reported, highlighting the identified gaps and the promising future developments in the context of the digital evolution.Note to Practitioners-The design and development of manufacturing systems are experiencing substantial changes towards full automation. This ongoing challenge is being tackled by academia and industrial practitioners with the adoption of collaborative robots, where the skills and peculiarities of humans (e.g., intelligence, creativity, adaptability, etc.) and robots (e.g., flexibility, pinpoint accuracy, tirelessness, etc.) are combined to better perform a variety of tasks. Nevertheless, due to their different characteristics, there is an emerging need for designing suitable decision and control techniques to ensure a safe and ergonomic HRC, while keeping the highest level of productivity. Against this background, the aim of this paper is to provide researchers and practitioners with a reference source in the related field, which can help them designing and developing suitable solutions to control problems in safe, ergonomic, and efficient collaborative robotics.

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“…With the development of artificial intelligence, IoT, digital twin [69], and parallel intelligence [70], the manufacturing industry is moving towards the goal of smart manufacturing. A number of edge computing frameworks or applications based on virtualization technologies are deployed to different industrial processes, e.g., semiconductor manufacturing [71], robotic assistance for emergency management [72], explosion prevention in mining industry [73], maintenance management [74,75], Fabric defect detection for textile production [76], oil and gas production [26,77], spectroscopic inspection for olive [78], and Augmented Reality for shipbuilding [79].…”

Section: Applications To Industrial Processesmentioning

confidence: 99%

Evolving Container to Unikernel for Edge Computing and Applications in Process Industry

Chen

Zhou

2021

Processes

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Industry 4.0 promotes manufacturing and process industry towards digitalization and intellectualization. Edge computing can provide delay-sensitive services in industrial processes to realize intelligent production. Lightweight virtualization technology is one of the key elements of edge computing, which can implement resource management, orchestration, and isolation services without considering heterogenous hardware. It has revolutionized software development and deployment. The scope of this review paper is to present an in-depth analysis of two such technologies, Container and Unikernel, for edge computing. We discuss and compare their applicability in terms of migration, security, and orchestration for edge computing and industrial applications. We describe their performance indexes, evaluation methods and related findings. We then discuss their applications in industrial processes. To promote further research, we present some open issues and challenges to serve as a road map for both researchers and practitioners in the areas of Industry 4.0, industrial process automation, and advanced computing.

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Digital Twin for Human-Robot Interactive Welding and Welder Behavior Analysis

Cited by 112 publications

References 37 publications

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Control Techniques for Safe, Ergonomic, and Efficient Human-Robot Collaboration in the Digital Industry: A Survey

Evolving Container to Unikernel for Edge Computing and Applications in Process Industry

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