scholarly journals Machine Learning for Automated Weld Quality Monitoring and Control - CRADA 461

2021 ◽  
Author(s):  
Darrell Herling
Keyword(s):  
Machine Learning ◽  
Quality Monitoring ◽  
Weld Quality ◽  
Monitoring And Control ◽  
And Control

The Study of Quality Monitoring and Control for Laser Cutting Based on Pulse-Induced Acoustic Sound

2012 ◽  
Vol 182-183 ◽  
pp. 422-426 ◽  
Author(s):  
Hui Juan Hao ◽  
Guang He Cheng ◽  
Ji Yong Xu
Keyword(s):  
Adaptive Control ◽  
Laser Cutting ◽  
Cutting Speed ◽  
Processing Parameters ◽  
Wavelet Denoising ◽  
Quality Monitoring ◽  
Monitoring And Control ◽  
Time Frequency ◽  
And Control ◽  
The Impact

In this paper, the pulse-induced acoustic sound in laser cutting is collected, and the data processing is performed with wavelet denoising and time-frequncy analyzing. The impact of laser processing parameters on the acoustic signal is discussed; and further analysis of the effect of cutting speed is conducted. The corresponding relationship between the best velocity and the maximum time-frequency energy density is got; also the plan of adaptive control in laser cutting is designed. The results in this paper can provide important parameters for adaptive control of laser cutting.

scholarly journals Enabling Smart Buildings by Indoor Visible Light Communications and Machine Learning

2019 ◽  
Author(s):  
Shuping Dang ◽  
Guoqing Ma ◽  
Basem Shihada ◽  
Mohamed-Slim Alouini
Keyword(s):  
Machine Learning ◽  
Visible Light ◽  
Data Transmission ◽  
Visible Light Communications ◽  
Monitoring And Control ◽  
Smart Building ◽  
Wide Range ◽  
Tremendous Amount ◽  
Processing Techniques ◽  
And Control

<pre>The smart building (SB), a promising solution to the fast-paced and continuous urbanization around the world, is an integration of a wide range of systems and services and involves a construction of multiple layers. The SB is capable of sensing, acquiring and processing a tremendous amount of data as well as performing proper action and adaptation accordingly. With rapid increases in the number of connected nodes and thereby the data transmission demand in SBs, conventional transmission and processing techniques are insufficient to provide satisfactory services. To enhance the intelligence of SBs and achieve efficient monitoring and control, both indoor visible light communications (VLC) and machine learning (ML) shall be applied jointly to construct a reliable data transmission network with powerful data processing and reasoning abilities. In this regard, we envision an SB framework enabled by indoor VLC and ML in this article.</pre>

Environmental building monitoring and control based on machine learning and fog computing on an IoT architecture

2020 ◽  
Author(s):  
Rafael L. Patrao ◽  
Francisco L. de Caldas Filho ◽  
Lucas M. C. e Martins ◽  
Gerson do N. Silva ◽  
Matheus S. Monteiro ◽  
...  
Keyword(s):  
Machine Learning ◽  
Fog Computing ◽  
Monitoring And Control ◽  
Building Monitoring ◽  
And Control

scholarly journals Integrating Statistical Machine Learning in a Semantic Sensor Web for Proactive Monitoring and Control

Sensors ◽  
2017 ◽  
Vol 17 (4) ◽  
pp. 807 ◽  
Author(s):  
Jude Adeleke ◽  
Deshendran Moodley ◽  
Gavin Rens ◽  
Aderemi Adewumi
Keyword(s):  
Machine Learning ◽  
Monitoring And Control ◽  
Statistical Machine Learning ◽  
Sensor Web ◽  
Semantic Sensor Web ◽  
And Control ◽  
Proactive Monitoring

scholarly journals Legal and formal analysis of chosen provisions of Directive 2009/30/EC and of the Act of 25 August 2006 on the fuel quality monitoring and control system

2019 ◽  
Vol 29 ◽  
pp. 171-181
Author(s):  
Weronika Gadzicka
Keyword(s):  
Control System ◽  
Formal Analysis ◽  
Quality Monitoring ◽  
Fuel Quality ◽  
Monitoring And Control ◽  
Monitoring And Control System ◽  
The Subject ◽  
Business Entities ◽  
Economic Analyses ◽  
And Control

The Directive 2009/30/EC and the Polish Act on the fuel quality monitoring and control system imposes a duty on the entities fulfilling the National Reduction Target NRT to reduce the emission of greenhouse gases to 6%. The reduction target of at least 6% can not be spread over the entire fuel market. It applies to every entity implementing the NRT separately. Member States, as well as the fuel market, are not responsible for not achieving the reduction target. This responsibility is limited to individual business entities implementing the NRT and concerns the fulfillment of the reduction target and the providing of the report on the implementation of the reduction target. It is necessary to carry out technical and economic analyses, the subject of which should be to answer the question of whether the entities realising the NRT are able to predict the potential amount of the fine that may be imposed on them based on art. 35c section 3 of the Act. Analiza prawna i formalna wybranych przepisów dyrektywy 2009/30/WE i ustawy z dnia 25 sierpnia 2006 roku o systemie monitorowania i kontroli jakości paliwDyrektywa 2009/30/WE oraz ustawa o systemie monitorowania i kontroli jakości paliw nakładają na podmioty spełniające Narodowy Cel Redukcyjny obowiązek obniżenia emisji gazów cieplarnianych do 6%. Cel redukcji wynoszący co najmniej 6% nie może być rozłożony na cały rynek paliw — dotyczy to każdego podmiotu wdrażającego NCR oddzielnie. Państwa członkowskie, jak też rynek paliw, nie ponoszą odpowiedzialności w wypadku nieosiągnięcia tegoż celu redukcyjnego. Odpowiedzialność ta jest ograniczona do poszczególnych podmiotów gospodarczych realizujących NCR i dotyczy realizacji celu redukcyjnego oraz dostarczenia sprawozdania z realizacji NCR. Konieczne jest przeprowadzenie analiz technicznych i ekonomicznych, których przedmiotem powinno być udzielenie odpowiedzi na pytanie, czy podmioty realizujące NRT są w stanie przewidzieć potencjalną kwotę grzywny, jaka może zostać na nie nałożona na podstawie art. 35c ust. 3 ustawy.

scholarly journals Applications of Machine Learning in Process Monitoring and Controls of L-PBF Additive Manufacturing: A Review

2021 ◽  
Vol 11 (24) ◽  
pp. 11910
Author(s):  
Dalia Mahmoud ◽  
Marcin Magolon ◽  
Jan Boer ◽  
M.A Elbestawi ◽  
Mohammad Ghayoomi Mohammadi
Keyword(s):  
Machine Learning ◽  
Defect Detection ◽  
In Situ Monitoring ◽  
Sensor Data ◽  
Learning Approaches ◽  
Monitoring And Control ◽  
In Situ Sensors ◽  
Applications Of Machine Learning ◽  
And Control

One of the main issues hindering the adoption of parts produced using laser powder bed fusion (L-PBF) in safety-critical applications is the inconsistencies in quality levels. Furthermore, the complicated nature of the L-PBF process makes optimizing process parameters to reduce these defects experimentally challenging and computationally expensive. To address this issue, sensor-based monitoring of the L-PBF process has gained increasing attention in recent years. Moreover, integrating machine learning (ML) techniques to analyze the collected sensor data has significantly improved the defect detection process aiming to apply online control. This article provides a comprehensive review of the latest applications of ML for in situ monitoring and control of the L-PBF process. First, the main L-PBF process signatures are described, and the suitable sensor and specifications that can monitor each signature are reviewed. Next, the most common ML learning approaches and algorithms employed in L-PBFs are summarized. Then, an extensive comparison of the different ML algorithms used for defect detection in the L-PBF process is presented. The article then describes the ultimate goal of applying ML algorithms for in situ sensors, which is closing the loop and taking online corrective actions. Finally, some current challenges and ideas for future work are also described to provide a perspective on the future directions for research dealing with using ML applications for defect detection and control for the L-PBF processes.

scholarly journals A Review - Water Quality Monitoring System

2019 ◽  
Author(s):  
R. J. Sapkal ◽  
Pooja Wattamwar ◽  
Rani Waghmode ◽  
Umrunnisa Tamboli
Keyword(s):  
Water Quality ◽  
Water Level ◽  
Water System ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Network Coverage ◽  
Monitoring And Control ◽  
Water Sensor ◽  
And Control ◽  
System Prototype

This paper describes the main reason for need of effective and efficient water level monitoring and control of water quality in flat system tends to keeping the human resources healthy and sustainable, and to reduce the usage of water for household purposes. Due to climate changes and variability so many huge impacts are caused by the water system to the natural environment. Incredible methods are used by collecting water samples, testing and analyses in water laboratories alone. However, It is not always easy to be captured, analyses and fast dissemination of information to relevant users for making timely and well-versed decisions. In this project Water Sensor System prototype is developed for water level and quality monitoring in society is presented. These kind of growth was introduced by the assessment of widespread atmosphere that Including accessibility of cellular network Coverage at the site of process.

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