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Analiza systemów monitorowania w czasie rzeczywistym – jakość procesów spawania laserowego. Cz. 3

Klimpel Andrzej

Stal, Metale & Nowe Technologie

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2020

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nr 9-10

70--75

PL

Artykuł opisuje podstawy technologii spawania laserowego oraz przyczyny tworzenia się wad złączy spawanych laserowo. Przeprowadzono analizę właściwości czujników stosowanych w nowoczesnych systemach monitorowania w czasie rzeczywistym jakości procesów spawania laserowego oraz podano przykłady zastosowania sterowania adaptacyjnego w tych systemach monitorowania.

XX

The basics of laser welding technology and causes of the typical defects of laser welded joints are described. An analysis of the properties of sensors used in the modern systems of on-line monitoring of laser welding processes quality is carried out. Examples of the applications of adaptive control in these systems are provided.

2

Analiza systemów monitorowania w czasie rzeczywistym jakości procesów spawania laserowego. Cz. 2

Klimpel Andrzej

Stal, Metale & Nowe Technologie

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2019

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nr 11-12

76--79

PL

Artykuł opisuje podstawy technologii spawania laserowego oraz przyczyny tworzenia się wad złączy spawanych laserowo. Przeprowadzono analizę właściwości czujników stosowanych w nowoczesnych systemach monitorowania w czasie rzeczywistym jakości procesów spawania laserowego oraz podano przykłady zastosowania sterowania adaptacyjnego w tych systemach monitorowania.

EN

The basics of laser welding technology and causes of the typical defects of laser welded joints are described. An analysis of the properties of sensors used in the modern systems of on-line monitoring of laser welding processes quality is carried out. Examples of the applications of adaptive control in these systems are provided.

3

A study on welding quality for the automatic vertical-position welding process based on Mahalanobis Distance method

Jin B.-J., Park M.-H., Yun T.-J., Shim J.-Y., Kang B.-Y., Kim I.-S.

Archives of Materials Science and Engineering

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2018

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Vol. 91, nr 1

31--40

EN

Purpose: The welding quality and reducing production cost could be achieved by developing the automatic on-line welding quality monitoring system. However, investigation of welding fault to quantify the welding quality on the horizontal-position welding has been concentrated. Therefore, MD (Mahalanobis Distance) method on the vertical-position welding process by analysing the transform arc voltage and welding current gained from the on-line monitoring system has been applied. Design/methodology/approach: The transformed welding current and arc voltage data were taken from the experiment whereby the data number was 2500 data/s. The prediction of Contact Tip to Work Distance (CTWD) to gain best welding quality using the waveform variations were then taken from the experimental results. MD was employed to quantify the welding quality by analysing the transformed arc voltage and welding current. Finally, the optimal CTWD setting has verified the developed algorithms through additional experiments. Two kinds of experiments has been carried out by changing welding parameters artificially to verify the sensitivity and feasibility of WQ (Welding Quality) based on the concepts of MD and normal distribution. Findings: The results represented that WQ was fully capable of quantifying and qualifying the welding faults for automatic vertical-position welding process. Research limitations/implications: The arc welding process on the vertical-position compared to a horizontal-position welding is much more difficult because the metal transfer is influenced by the gravity force. To solve the problem, a new algorithm to monitor and control the welding fault during the arc welding process has been developed. Furthermore, optimization of welding parameters for the vertical-position welding process was really difficult to use the developed algorithms because they are only useful in selecting stored data and not for evaluating the effect of the variation of welding parameters on the weld ability. Practical implications: The developed algorithm could be achieved the highest welding quality at 15mm CTWD setting which the welding quality is 99.50% for the start section and 99.68% at the middle section. Originality/value: This paper proposed a new algorithm which employed the concepts of MD (Mahalanobis Distance) and normal distribution to describe a good quality welding.

4

Online monitoring system of air distribution in pulverized coal-fired boiler based on numerical modeling

Żymełka P., Nabagło D., Janda T., Madejski P.

Archives of Thermodynamics

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2017

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Vol. 38, no. 4

109–-125

EN

Balanced distribution of air in coal-fired boiler is one of the most important factors in the combustion process and is strongly connected to the overall system efficiency. Reliable and continuous information about combustion airﬂow and fuel rate is essential for achieving optimal stoichiometric ratio as well as efficient and safe operation of a boiler. Imbalances in air distribution result in reduced boiler efficiency, increased gas pollutant emission and operating problems, such as corrosion, slagging or fouling. Monitoring of air ﬂow trends in boiler is an effective method for further analysis and can help to appoint important dependences and start optimization actions. Accurate real-time monitoring of the air distribution in boiler can bring economical, environmental and operational benefits. The paper presents a novel concept for online monitoring system of air distribution in coal-ﬁred boiler based on real-time numerical calculations. The proposed mathematical model allows for identification of mass ﬂow rates of secondary air to individual burners and to overﬁre air (OFA) nozzles. Numerical models of air and flue gas system were developed using software for power plant simulation. The correctness of the developed model was verified and validated with the reference measurement values. The presented numerical model for real-time monitoring of air distribution is capable of giving continuous determination of the complete air flows based on available digital communication system (DCS) data.