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

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6340
Author(s):  
Ziqi Huang ◽  
Yang Shen ◽  
Jiayi Li ◽  
Marcel Fey ◽  
Christian Brecher
Keyword(s):  
Industry 4.0 ◽  
Human Robot Interaction ◽  
Smart Manufacturing ◽  
Robot Interaction ◽  
Domain Specific ◽  
Industrial Sectors ◽  
Current State ◽  
Digital Twins ◽  
Metal Machining ◽  
Advanced Robotics

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.

scholarly journals Connectivity as a Design Feature for Industry 4.0 Production Equipment: Application for the Development of an In-Line Metrology System

2021 ◽  
Vol 11 (3) ◽  
pp. 1312
Author(s):  
Ana Pamela Castro-Martin ◽  
Horacio Ahuett-Garza ◽  
Darío Guamán-Lozada ◽  
Maria F. Márquez-Alderete ◽  
Pedro D. Urbina Coronado ◽  
...  
Keyword(s):  
Industry 4.0 ◽  
Design Feature ◽  
Production Equipment ◽  
Smart Manufacturing ◽  
Process Data ◽  
Industrial Sectors ◽  
Advanced Manufacturing Technologies ◽  
Manufacturing Technologies ◽  
Equipment Application ◽  
The Impact

Industry 4.0 (I4.0) is built upon the capabilities of Internet of Things technologies that facilitate the recollection and processing of data. Originally conceived to improve the performance of manufacturing facilities, the field of application for I4.0 has expanded to reach most industrial sectors. To make the best use of the capabilities of I4.0, machine architectures and design paradigms have had to evolve. This is particularly important as the development of certain advanced manufacturing technologies has been passed from large companies to their subsidiaries and suppliers from around the world. This work discusses how design methodologies, such as those based on functional analysis, can incorporate new functions to enhance the architecture of machines. In particular, the article discusses how connectivity facilitates the development of smart manufacturing capabilities through the incorporation of I4.0 principles and resources that in turn improve the computing capacity available to machine controls and edge devices. These concepts are applied to the development of an in-line metrology station for automotive components. The impact on the design of the machine, particularly on the conception of the control, is analyzed. The resulting machine architecture allows for measurement of critical features of all parts as they are processed at the manufacturing floor, a critical operation in smart factories. Finally, this article discusses how the I4.0 infrastructure can be used to collect and process data to obtain useful information about the process.

scholarly journals Digital Twins Collaboration for Automatic Erratic Operational Data Detection in Industry 4.0

2021 ◽  
Vol 11 (7) ◽  
pp. 3186
Author(s):  
Radhya Sahal ◽  
Saeed H. Alsamhi ◽  
John G. Breslin ◽  
Kenneth N. Brown ◽  
Muhammad Intizar Ali
Keyword(s):  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Interaction Mechanism ◽  
Semantic Knowledge ◽  
Smart Manufacturing ◽  
Data Detection ◽  
Workflow Model ◽  
Digital Twins ◽  
Potential Failure ◽  
Operational Data

Digital twin (DT) plays a pivotal role in the vision of Industry 4.0. The idea is that the real product and its virtual counterpart are twins that travel a parallel journey from design and development to production and service life. The intelligence that comes from DTs’ operational data supports the interactions between the DTs to pave the way for the cyber-physical integration of smart manufacturing. This paper presents a conceptual framework for digital twins collaboration to provide an auto-detection of erratic operational data by utilizing operational data intelligence in the manufacturing systems. The proposed framework provide an interaction mechanism to understand the DT status, interact with other DTs, learn from each other DTs, and share common semantic knowledge. In addition, it can detect the anomalies and understand the overall picture and conditions of the operational environments. Furthermore, the proposed framework is described in the workflow model, which breaks down into four phases: information extraction, change detection, synchronization, and notification. A use case of Energy 4.0 fault diagnosis for wind turbines is described to present the use of the proposed framework and DTs collaboration to identify and diagnose the potential failure, e.g., malfunctioning nodes within the energy industry.

Human-robot interaction in industry 4.0 based on an internet of things real-time gesture control system

2020 ◽  
pp. 1-17
Author(s):  
Luis Roda-Sanchez ◽  
Teresa Olivares ◽  
Celia Garrido-Hidalgo ◽  
José Luis de la Vara ◽  
Antonio Fernández-Caballero
Keyword(s):  
Control System ◽  
Internet Of Things ◽  
Real Time ◽  
Industry 4.0 ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Gesture Control

scholarly journals INDUSTRI 4.0: PENGUKURAN TINGKAT KESIAPAN INDUSTRI TEKSTIL DENGAN METODE SINGAPORE SMART INDUSTRY READINESS INDEX

2019 ◽  
Vol 16 (1) ◽  
pp. 29-36
Author(s):  
Satrio Utomo ◽  
Nugraheni Setiastuti
Keyword(s):  
Textile Industry ◽  
Industry 4.0 ◽  
Manufacturing Industry ◽  
Industrial Revolution ◽  
Industrial Sectors ◽  
Textile And Apparel ◽  
Business Efficiency ◽  
Smart Industry ◽  
Advanced Robotics ◽  
Industrial Level

The era of technology is disrupted at this time, better known as the Industrial Revolution 4.0,  already been applied to a various field of each country. Industry 4.0 include Internet of  Thing (IoT), Artificial Intelligence (AI), human-machine interface, 3-D printing, and Advanced Robotics.  Industry 4.0 is expected to increase productivity, business efficiency, and competitiveness. Indonesia’s Ministry of Industry has designed ‘Making Indonesia 4.0’  by preparing a roadmap and strategy to meet industry 4.0. There are 5 (five) prioritize manufacturing industrial sectors: Food and Beverages, Textile and Apparel, Electronics, Chemical, and Automotive. For studies conducted in the textile and apparel industry, as one of the priority industries. The Research study was conducted to determine the level of readiness of the textile manufacturing industry to meet industry 4.0 based on aspects of Technology, Processes, and Organizations. The method used is The Singapore Smart Industry Readiness Index. By knowing this level of readiness, it will help the industry to know the position of the current level of readiness and what needs are needed to reach the level of industry 4.0. By knowing the position, is able to know the strengths and weaknesses of technology from the operational technology used, which then knows the technological priorities that are of concern by management to increase industrial competitiveness towards industrial level 4.0.Based on the results of the mapping, related to the level of readiness of the textile industry of PT. Grand Textile based on technological aspects (1.56), process aspects (1.33) and organizational aspects (2.00) amounted to 1.63; position at level 1 which is categorized as New Comer.

scholarly journals Natural language instructions for human–robot collaborative manipulation

2018 ◽  
Vol 37 (6) ◽  
pp. 558-565 ◽  
Author(s):  
Rosario Scalise ◽  
Shen Li ◽  
Henny Admoni ◽  
Stephanie Rosenthal ◽  
Siddhartha S Srinivasa
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Human Robot Interaction ◽  
Robotic Manipulation ◽  
Easy Access ◽  
Domain Specific Language ◽  
Robot Interaction ◽  
Specific Language ◽  
Domain Specific ◽  
Object Reference

This paper presents a dataset of natural language instructions for object reference in manipulation scenarios. It comprises 1582 individual written instructions, which were collected via online crowdsourcing. This dataset is particularly useful for researchers who work in natural language processing, human–robot interaction, and robotic manipulation. In addition to serving as a rich corpus of domain-specific language, it provides a benchmark of image–instruction pairs to be used in system evaluations and uncovers inherent challenges in tabletop object specification. Example code is provided for easy access via Python.

Digital twins-based smart manufacturing system design in Industry 4.0: A review

2021 ◽  
Vol 60 ◽  
pp. 119-137
Author(s):  
Jiewu Leng ◽  
Dewen Wang ◽  
Weiming Shen ◽  
Xinyu Li ◽  
Qiang Liu ◽  
...  
Keyword(s):  
System Design ◽  
Industry 4.0 ◽  
Manufacturing System ◽  
Smart Manufacturing ◽  
Digital Twins ◽  
Manufacturing System Design

scholarly journals Secure IIoT-Enabled Industry 4.0

2021 ◽  
Vol 13 (22) ◽  
pp. 12384
Author(s):  
Zeeshan Hussain ◽  
Adnan Akhunzada ◽  
Javed Iqbal ◽  
Iram Bibi ◽  
Abdullah Gani
Keyword(s):  
Industry 4.0 ◽  
Smart Manufacturing ◽  
Detection Accuracy ◽  
Hybrid Architecture ◽  
Advanced Persistent Threats ◽  
Current State ◽  
Industrial Internet ◽  
Industrial Iot ◽  
Detection Mechanisms ◽  
Conventional Detection

The Industrial Internet of things (IIoT) is the main driving force behind smart manufacturing, industrial automation, and industry 4.0. Conversely, industrial IoT as the evolving technological paradigm is also becoming a compelling target for cyber adversaries. Particularly, advanced persistent threats (APT) and especially botnets are the foremost promising and potential attacks that may throw the complete industrial IoT network into chaos. IIoT-enabled botnets are highly scalable, technologically diverse, and highly resilient to classical and conventional detection mechanisms. Subsequently, we propose a deep learning (DL)-enabled novel hybrid architecture that can efficiently and timely tackle distributed, multivariant, lethal botnet attacks in industrial IoT. The proposed approach is thoroughly evaluated on a current state-of-the-art, publicly available dataset using standard performance evaluation metrics. Moreover, our proposed technique has been precisely verified with our constructed hybrid DL-enabled architectures and current benchmark DL algorithms. Our devised mechanism shows promising results in terms of high detection accuracy with a trivial trade-off in speed efficiency, assuring the proposed scheme as an optimal and legitimate cyber defense in prevalent IIoTs. Besides, we have cross-validated our results to show utterly unbiased performance.

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