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A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics
2021
Sensors
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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. ...
in the fields of smart manufacturing and advanced robotics. ...
., Fraunhofer IPT, as well as the Chair of Production Metrology and Quality Management, and Production Engineering of the Laboratory for Machine Tools and Production Engineering (WZL) for their permission ...
doi:10.3390/s21196340
pmid:34640660
pmcid:PMC8512418
fatcat:qy3qiazvqrejvfuixudd6ywqsq
A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics
2021
Sensors 21(19)
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This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY ...
., Fraunhofer IPT, as well as the Chair of Production Metrology and Quality Management, and Production Engineering of the Laboratory for Machine Tools and Production Engineering (WZL) for their permission ...
Acknowledgments: The authors would like to thank the German Research Foundation DFG for the support within the Cluster of Excellence "Internet of Production"-390621612. ...
doi:10.18154/rwth-2021-09877
fatcat:yjhprcascvfutisat7olust3kq
Knowledge Modelling and Active Learning in Manufacturing
[article]
2021
arXiv
pre-print
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By combining semantic technologies and active learning, multiple use cases in the manufacturing domain can be addressed taking advantage of the available knowledge and data. ...
While digitalization increases the amount of data available, much data is not labeled and cannot be directly used to train supervised machine learning models. ...
Acknowledgements This work has been carried out in the H STAR project, which has received funding from the European Union's Horizon research and innovation programme under grant agreement No. . ...
arXiv:2107.02298v1
fatcat:4iziznjvyfgpdluwadrjgbxv6u
A systematic review on machine learning methods for root cause analysis towards zero-defect manufacturing
2022
Frontiers in Manufacturing Technology
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The identification of defect causes plays a key role in smart manufacturing as it can reduce production risks, minimize the effects of unexpected downtimes, and optimize the production process. ...
Although artificial intelligence gains more and more attraction in smart manufacturing, machine learning methods for root cause analysis seem to be under-explored. ...
Digital twins provide a means to create a digital replica of the manufacturing line, which can be employed to tailor and optimize production parameters without affecting the running production. ...
doi:10.3389/fmtec.2022.972712
fatcat:yteg6hbflfb3fcf54jtih3isim
Digital Twins in Product Lifecycle for Sustainability in Manufacturing and Maintenance
2020
Applied Sciences
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A "digital twin" is a dynamic, digital replica of a technical object (e.g., a physical system, device, machine or production process) or a living organism. ...
In addition, monitoring processes and process parameters allow for the continued improvement of existing processes as regards intelligent eco-designing and planning and monitoring production processes ...
The digital twin concept becomes even more attractive in sustainable manufacturing, in both subtractive and additive machining. ...
doi:10.3390/app11010031
fatcat:ehzlu42afvdx3fkzchn3h4bi24
State Estimators in Soft Sensing and Sensor Fusion for Sustainable Manufacturing
2022
Sustainability
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robotics, material synthesis and processing, semiconductor, and additive manufacturing. ...
We discuss current and emerging trends in using state estimation as a framework for combining physical knowledge with other sources of data for monitoring and controlling distributed manufacturing systems ...
State Estimation and 'Digital Twins' A digital twin is a computational representation of a physical process where there is exchange of data in real-time between the real and virtual processes. ...
doi:10.3390/su14063635
fatcat:3a2twj3nlzbszlhc5ijluvekde
Queue Length Forecasting in Complex Manufacturing Job Shops
2021
Forecasting
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In order to provide queue length forecasts, this paper introduced a methodology to identify queue lengths in retrospect based on transitional data, as well as a comparison of easy-to-deploy machine learning-based ...
Forecasting, based on static data sets, as well as time series models can be shown to be successfully applied in an exemplary semiconductor case study. ...
rules towards intelligent, digital twin-based production control [3] , from traditional line production towards complex job shops or matrix production systems [4] , from centralized approaches to decentralized ...
doi:10.3390/forecast3020021
fatcat:fbibxonowncqlmrrxixneii5oe
Uncertainty Quantification for Additive Manufacturing Process Improvement: Recent Advances
2022
ASCE-ASME J of Risk & Uncertainty in Engineering SystemsPart B: Mechanical Engineering
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Literature on using the results of UQ activities toward AM process optimization and control (thus supporting maximization of quality and minimization of variability) is also reviewed. ...
Physics-based as well as data-driven models are increasingly being developed and refined in order to support process optimization and control objectives in AM, in particular to maximize the quality and ...
However, digital twin in AM is still in a very early stage and is yet to be demonstrated for real-time process control. ...
doi:10.1115/1.4053184
fatcat:mthqaifq7ndxnawtgeg5dzr7iy
Operator Integrated – Concept for Manufacturing Intelligence
2021
Journal of Machine Engineering
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Model based technologies digital twins or digital images of processes and machines are
the immediate entry points for enhanced mastering of processes. ...
Structure of a former expert system adapted from [20] and [21] without self-learning ability
The curve of used manufacturing times is per person divided in 6 equidistant segments
and becomes green ...
doi:10.36897/jme/144359
fatcat:6jlcyxw7fvbsrfj7xc5zvnmatm
A Review of Real-Time Fault Diagnosis Methods for Industrial Smart Manufacturing
2023
Processes
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In addition, this paper discusses the challenges and potential trends of RTFD in future development to provide a reference for researchers focusing on this field. ...
This paper provides a comprehensive review of the latest RTFD technologies in the field of industrial process monitoring and machine condition monitoring. ...
In addition, data mapping technologies such as the digital twin can be introduced to improve the efficiency and real time of FD by using dual diagnosis in virtual and physical space. ...
doi:10.3390/pr11020369
fatcat:3bzunw76ore7pcagaoi6mlmqpy
Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective
[article]
2022
arXiv
pre-print
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We also describe currently available commercial off-the-shelf products for tracking in manufacturing, and we overview main concepts of digital twins and their applications. ...
The purpose of this document is to review the current state of work for movement analytics both in manufacturing and more broadly. ...
an entire system, based on Product Twin for each device and Process Twin to optimize the manufacturing processes of these components. ...
arXiv:2210.01344v1
fatcat:qovx37gqevgabkxgox7hvj23ue
Machine Learning-Assisted Multi-Objective Optimization of Battery Manufacturing from Synthetic Data Generated by Physics-Based Simulations
[article]
2022
arXiv
pre-print
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In this work, we tackle this issue by proposing an innovative approach, supported by a deterministic machine learning (ML)-assisted pipeline for multi-objective optimization of LIB electrode properties ...
The optimization of the electrodes manufacturing process constitutes one of the most critical steps to ensure high-quality Lithium-Ion Battery (LIB) cells, in particular for automotive applications. ...
Such a fast optimization capabilities will be needed in digital twins collecting data through sensors, and giving instructions to the manufacturing machines through actuators for on the fly and autonomous ...
arXiv:2205.01621v1
fatcat:azkeomlhijemxh4vuwies5nqte
Digital Innovation Enabled Nanomaterial Manufacturing; Machine Learning Strategies and Green Perspectives
2022
Nanomaterials
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Machine learning has been an emerging scientific field serving the modern multidisciplinary needs in the Materials Science and Manufacturing sector. ...
Thus, in this review, a summary of recent computational developments is provided with the aim to cover excelling research and present challenges towards unbiased, decentralized, and data-driven decision-making ...
models and machine learning [47] Machine Learning for Advanced Additive Manufacturing • Theoretical design expectations • Practical manufacturing capabilities • AI for AM [50] Machine learning in ...
doi:10.3390/nano12152646
pmid:35957077
pmcid:PMC9370746
fatcat:hbluqbnexffnjkqu6b4lzpqwiq
Artificial-Intelligence-Driven Customized Manufacturing Factory: Key Technologies, Applications, and Challenges
2020
Proceedings of the IEEE
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2021 pre-print arXiv:2108.03383v1 fatcat:7cbkssngwjdy5dmsiwyo6hh5vq
The state-of-the-art AI technologies of potential use in CM, i.e., machine learning, multi-agent systems, Internet of Things, big data, and cloud-edge computing are surveyed. ...
The characteristics of a customized smart factory are to include self-perception, operations optimization, dynamic reconfiguration, and intelligent decision-making. ...
For instance, customers can remotely monitor the details of the production process via a digital twin or a virtual production line system. ...
doi:10.1109/jproc.2020.3034808
fatcat:bpljlzguqjhypedblczhmch2uq
(Data-Driven) Reinforcement learning based optimal decision making towards product lifecycle sustainability
2022
International journal of computer integrated manufacturing (Print)
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The method has been validated in a case study of an international vehicle manufacturer, combined with modelling and simulation. ...
Then, a generalised procedure of using RL for decision support is proposed based on available lifecycle data, such as operation and maintenance data. ...
Given sufficient examples of past problems and their solutions, bringing this into a machine learning framework as a supervised learning problem is straightforward. ...
doi:10.1080/0951192x.2022.2025623
fatcat:y5xk3wffujekrp6n7c22k4ifdi
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