A state-of-the-art review on implementation of digital twin in additive manufacturing to monitor and control parts quality

2021 ◽  
Author(s):  
Rakesh Kumar Phanden ◽  
S.V. Aditya ◽  
Aaryan Sheokand ◽  
Kapil Kumar Goyal ◽  
Pardeep Gahlot ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
State Of The Art ◽  
Digital Twin ◽  
And Control ◽  
Monitor And Control

Seismic RTDT: Real-Time Digital Twin for Boosting Performance of Seismic Operations

2019 ◽  
Author(s):  
Severin Sadjina ◽  
Stian Skjong ◽  
Armin Pobitzer ◽  
Lars T. Kyllingstad ◽  
Roy-Jostein Fiskerstrand ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
State Of The Art ◽  
Seismic Survey ◽  
Digital Twin ◽  
Detection Systems ◽  
Early Results ◽  
Actual Equipment ◽  
Automation Systems ◽  
And Control

Abstract Here, we present the R&D project Real-Time Digital Twin for Boosting Performance of Seismic Operations, which aims at increasing the overall operational efficiency of seismic vessels through digitisation and automation. The cornerstone in this project is the development of a real-time digital twin (RTDT) — a sophisticated mathematical model and state estimator of all the in-sea seismic equipment, augmented with real-time measurements from the actual equipment. This provides users and systems on-board the vessel with a live digital representation of the state of the equipment during operations. By combining the RTDT with state-of-the-art methods in machine learning and control theory, the project will develop new advisory and automation systems that improve the efficiency of seismic survey operations, reduce the risk of equipment damage, improve health monitoring and fault detection systems, and improve the quality of the seismic data. This will lead to less unproductive time, reduced costs, reduced fuel consumption and reduced emissions for a given operational scope. The main focus in this paper is the presentation of today’s challenges in offshore seismic surveys, and how state-of-the-art technology can be adopted to improve various operations. We discuss how simulation technology, machine learning and live sensor measurements can be integrated in on-board decision support and automation systems, and highlight the importance of such systems for designing the complex, autonomous offshore vessels of the future. Finally, we present some early results from the project in the form of two brief case studies.

scholarly journals Digital Twin in Electrical Machine Control and Predictive Maintenance: State-of-the-Art and Future Prospects

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5933
Author(s):  
Georgios Falekas ◽  
Athanasios Karlis
Keyword(s):  
State Of The Art ◽  
Predictive Maintenance ◽  
Electrical Machine ◽  
Digital Twin ◽  
Industrial Manufacturing ◽  
Core Concepts ◽  
Processing Techniques ◽  
And Control ◽  
Future Work ◽  
Relevant Work

State-of-the-art Predictive Maintenance (PM) of Electrical Machines (EMs) focuses on employing Artificial Intelligence (AI) methods with well-established measurement and processing techniques while exploring new combinations, to further establish itself a profitable venture in industry. The latest trend in industrial manufacturing and monitoring is the Digital Twin (DT) which is just now being defined and explored, showing promising results in facilitating the realization of the Industry 4.0 concept. While PM efforts closely resemble suggested DT methodologies and would greatly benefit from improved data handling and availability, a lack of combination regarding the two concepts is detected in literature. In addition, the next-generation-Digital-Twin (nexDT) definition is yet ambiguous. Existing DT reviews discuss broader definitions and include citations often irrelevant to PM. This work aims to redefine the nexDT concept by reviewing latest descriptions in broader literature while establishing a specialized denotation for EM manufacturing, PM, and control, encapsulating most of the relevant work in the process, and providing a new definition specifically catered to PM, serving as a foundation for future endeavors. A brief review of both DT research and PM state-of-the-art spanning the last five years is presented, followed by the conjunction of core concepts into a definitive description. Finally, surmised benefits and future work prospects are reported, especially focused on enabling PM state-of-the-art in AI techniques.

scholarly journals Hybrid metal additive manufacturing: A state–of–the-art review

2021 ◽  
Vol 2 ◽  
pp. 100032
Author(s):  
J.P.M. Pragana ◽  
R.F.V. Sampaio ◽  
I.M.F. Bragança ◽  
C.M.A. Silva ◽  
P.A.F. Martins
Keyword(s):  
Additive Manufacturing ◽  
State Of The Art ◽  
Metal Additive Manufacturing ◽  
Metal Additive

The politics of the Hajj organisation in Senegal: security, legitimation and clientelism

2021 ◽  
Vol 59 (1) ◽  
pp. 1-20
Author(s):  
Cédric Jourde ◽  
Marie Brossier ◽  
Muriel Gomez-Perez
Keyword(s):  
Public Service ◽  
The State ◽  
Religious Leaders ◽  
Symbolic Resources ◽  
Travel Agencies ◽  
Security Concerns ◽  
And Control ◽  
Monitor And Control

ABSTRACTThis article analyses how the state in Senegal has managed the hajj since the liberalisation era in the early 2000s. Although the essence of the hajj is religious, it is also deeply political and requires that the state manages complex relations with pilgrims, religious leaders, private travel agencies, politicians and Saudi authorities. This article argues that three inter-related imperatives structure the conduct of the Senegalese state: a security imperative, a legitimation imperative, and a clientelistic imperative. Security concerns lead the state to monitor and control pilgrims travelling to Mecca. Legitimation is seen in the collaborative relations with Sûfi orders and in the framing of the hajj organisation as a ‘public service’. Finally, given the magnitude of financial and symbolic resources attached to the hajj, clientelistic relations are constitutive of state officials’ actions. Overall, despite the post-2000 liberalisation of the hajj, the state has maintained its role as a gatekeeper, regulator and supervisor.

scholarly journals State of the art in additive manufacturing and its possible chemical and particle hazards—review

Indoor Air ◽  
2021 ◽  
Author(s):  
Elżbieta Dobrzyńska ◽  
Dorota Kondej ◽  
Joanna Kowalska ◽  
Małgorzata Szewczyńska
Keyword(s):  
Additive Manufacturing ◽  
State Of The Art

scholarly journals A Multi-Timescale Bilinear Model for Optimization and Control of HVAC Systems with Consistency

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 400 ◽  
Author(s):  
Zelin Nie ◽  
Feng Gao ◽  
Chao-Bo Yan
Keyword(s):  
Optimization Model ◽  
Air Conditioning ◽  
State Of The Art ◽  
Hvac Systems ◽  
Practical Significance ◽  
Hvac System ◽  
Bilinear Model ◽  
Optimization And Control ◽  
And Control ◽  
Slow Timescale

Reducing the energy consumption of the heating, ventilation, and air conditioning (HVAC) systems while ensuring users’ comfort is of both academic and practical significance. However, the-state-of-the-art of the optimization model of the HVAC system is that either the thermal dynamic model is simplified as a linear model, or the optimization model of the HVAC system is single-timescale, which leads to heavy computation burden. To balance the practicality and the overhead of computation, in this paper, a multi-timescale bilinear model of HVAC systems is proposed. To guarantee the consistency of models in different timescales, the fast timescale model is built first with a bilinear form, and then the slow timescale model is induced from the fast one, specifically, with a bilinear-like form. After a simplified replacement made for the bilinear-like part, this problem can be solved by a convexification method. Extensive numerical experiments have been conducted to validate the effectiveness of this model.

scholarly journals Drone Deep Reinforcement Learning: A Review

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 999
Author(s):  
Ahmad Taher Azar ◽  
Anis Koubaa ◽  
Nada Ali Mohamed ◽  
Habiba A. Ibrahim ◽  
Zahra Fathy Ibrahim ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
State Of The Art ◽  
Real Life ◽  
Environment Monitoring ◽  
Simulated Environments ◽  
Infrastructure Inspection ◽  
Remote Sensing Mapping ◽  
And Control ◽  
The Military ◽  
Uav Navigation

Unmanned Aerial Vehicles (UAVs) are increasingly being used in many challenging and diversified applications. These applications belong to the civilian and the military fields. To name a few; infrastructure inspection, traffic patrolling, remote sensing, mapping, surveillance, rescuing humans and animals, environment monitoring, and Intelligence, Surveillance, Target Acquisition, and Reconnaissance (ISTAR) operations. However, the use of UAVs in these applications needs a substantial level of autonomy. In other words, UAVs should have the ability to accomplish planned missions in unexpected situations without requiring human intervention. To ensure this level of autonomy, many artificial intelligence algorithms were designed. These algorithms targeted the guidance, navigation, and control (GNC) of UAVs. In this paper, we described the state of the art of one subset of these algorithms: the deep reinforcement learning (DRL) techniques. We made a detailed description of them, and we deduced the current limitations in this area. We noted that most of these DRL methods were designed to ensure stable and smooth UAV navigation by training computer-simulated environments. We realized that further research efforts are needed to address the challenges that restrain their deployment in real-life scenarios.

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