Digital Signal Processing

Rao, K. Deergha; Swamy, M. N. S.

doi:10.1007/978-981-10-8081-4

Cited by 29 publications

(21 citation statements)

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“…In a three-dimensional characteristic, spectrograms show how the spectral density of a signal varies over time. 27 In a spectrogram, the time increases linearly along the horizontal axis and the frequency increases along the vertical axis. 28 The approach to convert time signals into spectrograms and evaluate them by CNNs has already been successfully implemented for the processing of audio signals, for example for speech recognition, 29 or in medical technology.…”

Section: Pre-processing Of the Process Datamentioning

confidence: 99%

Prediction of the surface quality of friction stir welds by the analysis of process data using Artificial Neural Networks

Hartl

Praehofer

Zaeh

2020

Proceedings of the Institution of Mechanical Engineers, Part L:

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Friction stir welding is an advanced joining technology that is particularly suitable for aluminum alloys. Various studies have shown that welding quality depends significantly on the welding speed and the rotational speed of the tool. It is frequently possible to detect an unsuitable setting of these parameters by examining the resulting surface defects, such as increased flash formation or surface galling. In this work, Artificial Neural Networks were used to analyze process data in friction stir welding and predict the resulting quality of the weld surface. For this purpose, nine different variables were recorded during friction stir welding of EN AW-6082 T6 sheets: the forces and accelerations in three spatial directions, the spindle torque, and temperatures at the tool shoulder and tool probe. In Case 1, the welds were assigned to the classes good and defective on the basis of a human visual inspection of the weld surface. In Case 2, the welds were categorized into the two classes on the basis of a surface topography analysis. Subsequently, three different major Artificial Neural Network architectures were tested for their ability to predict the surface quality: Feed Forward Fully Connected Neural Networks, Recurrent Neural Networks and Convolutional Neural Networks. The highest classification accuracy was achieved when Convolutional Neural Networks were used. Thus, the evaluation of the force signal transverse to the welding direction yielded the highest accuracy of 99.1% for the prediction of the result of the human visual inspection. The result achieved for the prediction of the topography analysis was an accuracy of 87.4% when the spindle torque was evaluated. Using all nine different process variables to predict the topography analysis, the accuracy could be improved slightly to 88.0%. The sampling rate of the spindle torque was varied between 40 Hz and 9600 Hz and no significant influence was determined. The findings show that Convolutional Neural Networks are well suited for the interpretation of friction stir welding process data and can be used to predict the resulting surface quality. In future work, the results are to be used to develop a parameter optimization method for friction stir welding.

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Section: Pre-processing Of the Process Datamentioning

confidence: 99%

Prediction of the surface quality of friction stir welds by the analysis of process data using Artificial Neural Networks

Hartl

Praehofer

Zaeh

2020

Proceedings of the Institution of Mechanical Engineers, Part L:

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“…For the CNN, spectrograms were generated, similar to Hartl et al [15]. Spectrograms depict the spectral density of a signal depending on the time and the frequency in a three-dimensional manner [21].…”

Section: Data Acquisition and Pre-processingmentioning

confidence: 99%

Process Monitoring in Friction Stir Welding Using Convolutional Neural Networks

Hartl

Bachmann

Habedank

et al. 2021

Metals

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Preliminary studies have shown the superiority of convolutional neural networks (CNNs) compared to other network architectures for determining the surface quality of friction stir welds. In this paper, CNNs were employed to detect cavities inside friction stir welds by evaluating inline measured process data. The aim was to determine whether CNNs are suitable for identifying surface defects exclusively, or if the approach is transferable to internal weld defects. For this purpose, 120 welds were produced and examined by ultrasonic testing, which was the basis for labeling the data as “good” or “defective.” Different types of artificial neural network were tested for predicting the placement of the welds into the defined classes. It was found that the way of labeling the data is significant for the accuracy achievable. When the complete welds were uniformly labeled as “good” or “defective,” an accuracy of 98.5% was achieved by a CNN, which was a significant improvement compared to the state of the art. When the welds were labeled segment-wise, an accuracy of 79.2% was obtained by using a CNN, showing that a segment-wise prediction of the cavities is also possible. The results confirm that CNNs are well suited for process monitoring in friction stir welding and their application enables the identification of various defect types.

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“…Controllers with a Digital Signal Processor (DSP) core have the processing power necessary for the software implementation of a discrete time equalizer. The theory and practice of industrial operation of DSP controllers are nowadays well studied and documented [8], educational institutions use DSP controllers in laboratory work on digital signal processing in real time [9].…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Synthesis of automatic speed control system of laboratory research bench drive motor on the basis of discrete time equalizer

Sheremet

Sadovoi

2020

EEJET

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Дослiдження базується на використаннi методу дискретного часового еквалайзера для здiйснення синтезу та практичної реалiзацiї системи автоматичного керування швидкiстю електроприводу постiйного струму. Для виконання експериментальних дослiджень створено лабораторно-дослiдний стенд. Синтез систем автоматичного керування методом дискретного часового еквалайзера вiдрiзняється вiд традицiйного пiдпорядкованого регулювання координат або метода узагальненого характеристичного полiнома повною вiдмовою вiд використання бажаних характеристичних полiномiв. Такий пiдхiд дозволяє отримати бажанi динамiчнi та статичнi властивостi системи виключно виходячи з бажаної перехiдної функцiї, яка повинна бути наближеною до природного характеру протiкання перехiдних процесiв (монотонного, аперiодичного або коливального). Iнтегроване середовище проектування Code Composer Studio дозволило практично реалiзувати запропонованi дискретнi часовi еквалайзери, обернену модель об'єкта керування та блок модифiкацiї зворотного перетворення у виглядi спецiальних пiдпрограм для мiкроконтролера Texas Instruments TMS320F28335-макросiв на мовi програмування C/C++. Побудоване у вiдповiдностi до розробленої функцiональної схеми взаємодiї макросiв основне тiло керуючої програми надало можливiсть для проведення експериментальних дослiджень iз застосуванням як лише основного каналу керування з одним дискретним часовим еквалайзером, так i комбiнованого керування з двома дискретними часовими еквалайзерами (основним та компенсуючим). Оскiльки весь програмний код, використаний пiд час дослiджень, написано мовою програмування високого рiвня C/C++ з використанням об'єктно-орiєнтованих пiдходiв, то вiн є апаратно незалежним вiд типу мiкропроцесора i з легкiстю може бути перенесений на iншу апаратну базу Ключовi слова: дискретний часовий еквалайзер,мiкроконтролер, автоматизована система керування, двигун постiйного струму

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Digital Signal Processing

Cited by 29 publications

References 0 publications

Prediction of the surface quality of friction stir welds by the analysis of process data using Artificial Neural Networks

Prediction of the surface quality of friction stir welds by the analysis of process data using Artificial Neural Networks

Process Monitoring in Friction Stir Welding Using Convolutional Neural Networks

Synthesis of automatic speed control system of laboratory research bench drive motor on the basis of discrete time equalizer

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