Process Parameter Monitoring and Control Using Digital Twin

2019 ◽  
pp. 74-80
Author(s):  
Nihal Desai ◽  
S. K. Ananya ◽  
Lalit Bajaj ◽  
Anupriya Periwal ◽  
Santosh R. Desai
Keyword(s):  
Process Parameter ◽  
Monitoring And Control ◽  
Digital Twin ◽  
And Control ◽  
Parameter Monitoring

scholarly journals A New Concept of Digital Twin Supporting Optimization and Resilience of Factories of the Future

2020 ◽  
Vol 10 (13) ◽  
pp. 4482 ◽  
Author(s):  
Adrien Bécue ◽  
Eva Maia ◽  
Linda Feeken ◽  
Philipp Borchers ◽  
Isabel Praça
Keyword(s):  
Cognitive Modeling ◽  
Monitoring And Control ◽  
Security Testing ◽  
Digital Twin ◽  
Digital Twins ◽  
Support Tools ◽  
The Future ◽  
And Control ◽  
Comprehensive Modeling ◽  
Factories Of The Future

In the context of Industry 4.0, a growing use is being made of simulation-based decision-support tools commonly named Digital Twins. Digital Twins are replicas of the physical manufacturing assets, providing means for the monitoring and control of individual assets. Although extensive research on Digital Twins and their applications has been carried out, the majority of existing approaches are asset specific. Little consideration is made of human factors and interdependencies between different production assets are commonly ignored. In this paper, we address those limitations and propose innovations for cognitive modeling and co-simulation which may unleash novel uses of Digital Twins in Factories of the Future. We introduce a holistic Digital Twin approach, in which the factory is not represented by a set of separated Digital Twins but by a comprehensive modeling and simulation capacity embracing the full manufacturing process including external network dependencies. Furthermore, we introduce novel approaches for integrating models of human behavior and capacities for security testing with Digital Twins and show how the holistic Digital Twin can enable new services for the optimization and resilience of Factories of the Future. To illustrate this approach, we introduce a specific use-case implemented in field of Aerospace System Manufacturing.

The digital twin of the quality monitoring and control in the series solar cell production line

2021 ◽  
Vol 59 ◽  
pp. 127-137
Author(s):  
Feng-Que Pei ◽  
Yi-Fei Tong ◽  
Ming-Hai Yuan ◽  
Kun Ding ◽  
Xi-Hui Chen
Keyword(s):  
Solar Cell ◽  
Production Line ◽  
Quality Monitoring ◽  
Monitoring And Control ◽  
Cell Production ◽  
Digital Twin ◽  
And Control

Application of Desorption Mass Spectrometry to Molecular Beam Epitaxy

1994 ◽  
Vol 340 ◽  
Author(s):  
Keith R. Evans
Keyword(s):  
Molecular Beam ◽  
Growth Parameters ◽  
Growth Parameter ◽  
Surface Processes ◽  
Monitoring And Control ◽  
Mbe Growth ◽  
Desorption Mass Spectrometry ◽  
And Control ◽  
Parameter Monitoring

ABSTRACTMolecular beam epitaxy (MBE) is the most powerful crystal growth technique available in terms of the range of structures which can be produced. However, further advances in MBEprocess monitoring and control are required to produce the most demanding structures. Also, process yield, cost, and throughput must be improved for MBE to gain in production worthiness. Because of these issues, there currently is much research activity focusing on the development of advanced in-situ sensors for improved growth parameter monitoring and control. Desorption mass spectrometry (DMS) is an in-situ sensor technique for detecting species leaving the substrate during MBE growth processing. Since in general all surface processes affect desorption rates, and all growth parameters affect surface processes, DMS can be utilized to monitor a variety of MBE growth parameters and phenomena. Theimplementation of simple feedback techniques which link DMS error signals to changes in one or more growth parameters then provides for continuous control of desorption rates. This paper reviews some of the most recent accomplishments in the application of DMS forimproved growth parameter monitoring and control. Additionally, anticipated future directions in the application of DMS to epitaxial growth are discussed.

scholarly journals Sistem Kendali Nutrisi Hidroponik berbasis Fuzzy Logic berdasarkan Objek Tanam

2021 ◽  
Vol 9 (2) ◽  
pp. 263
Author(s):  
FITRIA SURYATINI ◽  
SUHARYADI PANCONO ◽  
SUSETYO BAGAS BHASKORO ◽  
PUTRI MUTHIA SARASWATI MULJONO
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Water Level ◽  
Farming System ◽  
Water Pump ◽  
Monitoring And Control ◽  
Level Control ◽  
Set Point ◽  
And Control ◽  
Parameter Monitoring

ABSTRAKHidroponik merupakan sistem pertanian yang menggunakan air sebagai media tanam sehingga tidak memerlukan media tanah ataupun area luas. Hidroponik memerlukan perlakuan khusus seperti menjaga kadar nutrisi dalam rentangnya sehingga penggunaan sistem kendali dapat mempermudah pemantauan dan pengaturan parameter. Sistem kendali yang digunakan adalah fuzzy logic mamdani dengan input offset kadar nutrisi dan level air, serta output durasi nyala motor pompa nutrisi dan air untuk mencapai set point yang dapat ditentukan melalui antarmuka berdasarkan database objek tanam atau slider manual. Hasil penelitian menunjukkan nilai keberhasilan sebesar 95,14% untuk kendali nutrisi dan 91,64% untuk kendali level air dalam mencapai set point, serta menghasilkan pertumbuhan tanaman yang lebih baik, dimana rata-rata penambahan tinggi antara sistem dengan dan tanpa kendali nutrisi memiliki perbedaan sebesar 1,96 cm.Kata kunci: sistem kendali, fuzzy logic, IoT, hidroponik, nutrisi ABSTRACTHydroponic is a farming system which uses water as planting media, so it is unnecessary to use soil nor consume wide area. Hydroponic requires special handlings such as maintaining nutrient measurement level within range so the use of control system may ease the parameter monitoring and control. The control system that is used is mamdani fuzzy logic with the nutrient measurement level offset and water level input, as well as nutrient and water pump motors activation duration output to reach the set point determined from the interface based on the planting-object database or manual slider. The results showed a success value of 95.14% for nutritional control and 91.64% for water level control in reaching set point, and resulting in better plant growth, where the average increase in height between the system with and without nutrient control has a difference of 1.96 cm.Keywords: control system, fuzzy logic, IoT, hydroponics, nutrient

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