Wyniki wyszukiwania - BazTech

1

Deep Convolutional Neural Network Algorithm for Prediction of the Mechanical Properties of FSW Copper Welds from its Microstructure

Mishra Akshansh, Suman Asmita

Welding Technology Review

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2023

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R. 95

25--31

EN

Convolutional Neural Network (CNN) is a special type of Artificial Neural Network which takes input in the form of an image. Like Artificial Neural Network they consist of weights that are estimated during training, neurons (activation functions), and an objective (loss function). CNN is finding various applications in image recognition, semantic segmentation, object detection, and localization. The present work deals with the prediction of the welding efficiency of the Friction Stir Welded joints on the basis of microstructure images by carrying out training on 3000 microstructure images and further testing on 300 microstructure images. The loss function decreased for both training and testing set decreased with the increasing number of epochs. The obtained results showed an accuracy of 80 % on the validation dataset.

2

Friction Stir Welding/Processing of High Entropy Alloys (HEAs)

Mishra Akshansh

Welding Technology Review

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2021

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R. 93, nr 1

27--33

EN

The composition of High Entropy Alloys is quite different from the existing classical engineering alloys because in near equiatomic ratios they contain multiple principal alloying elements. Design and development of high entropy alloys is very important to overcome the shortcomings of conventionally used alloys in applications where operating conditions of temperature and loading are extreme. High entropy alloys generally find applications in compressor blades of an aerospace engine, energy, and transportation industries due to its low density and high strength. In order to enhance the application of high entropy alloys, the proper selection of a feasible welding process is very important. It has been observed that when high entropy alloys are subjected to the welding process other than the Friction Stir Welding process then it will result in reduced overall strength and lower hardness in the fusion zone and heat-affected zone. In this recent paper, the application of Friction Stir Welding for joining the high entropy alloys and also using Friction Stir Processing for improving the mechanical and microstructure properties of high entropy alloys are discussed.

PL

Projektowanie i opracowywanie stopów o wysokiej entropii jest bardzo ważne, aby przezwyciężyć niedociągnięcia konwencjonalnie stosowanych stopów w zastosowaniach, w których warunki pracy, takie jak temperatura i obciążenie, są ekstremalne. Stopy o wysokiej entropii zwykle znajdują zastosowanie w łopatkach sprężarek silników lotniczych, energetyce i transporcie ze względu na ich niską gęstość i wysoką wytrzymałość. Aby usprawnić stosowanie stopów o wysokiej entropii, bardzo ważny jest właściwy dobór możliwego do wykonania procesu spawania. Zaobserwowano, że gdy stopy o wysokiej entropii zostaną poddane procesowi spawania innemu niż proces zgrzewania tarciowego z mieszaniem, to spowoduje to zmniejszoną ogólną wytrzymałość i niższą twardość w strefie topienia i strefie wpływu ciepła. W artykule omówiono zastosowanie zgrzewania tarciowego z przemieszaniem do łączenia stopów o wysokiej entropii, a także zastosowanie tarciowej modyfikacji z przemieszaniem do poprawy właściwości mechanicznych i mikrostrukturalnych tych stopów.

3

Artificial Intelligence Algorithms for the Analysis of Mechanical Property of Friction Stir Welded Joints by using Python Programming

Mishra Akshansh

Welding Technology Review

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2020

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R. 92, nr 6

7--16

EN

In modern computational science, the interplay existing between machine learning and optimization process marks the most vital developments. Optimization plays an important role in mechanical industries because it leads to reduce in material cost, time consumption and increase in production rate. The recent work focuses on performing the optimization task on Friction Stir Welding process for obtaining the maximum Ultimate Tensile Strength (UTS) of the friction stir welded joints. Two machine learning algorithms i.e. Artificial Neural Network (ANN) and Decision Trees regression model are selected for the purpose. The input variables are Tool Rotational Speed (RPM), Tool Traverse Speed (mm/min) and Axial Force (KN) while the output variable is Ultimate Tensile Strength (MPa). It is observed that in case of the Artificial Neural Networks the Root Mean Square Errors for training and testing sets are 0.842 and 0.808 respectively while in case of Decision Trees regression model, the training and testing sets result Root Mean Square Errors of 11.72 and 14.61. So, it can be concluded that ANN algorithm gives better and accurate result than Decision Tree regression algorithm.

PL

We współczesnych obliczeniach naukowych wzajemna zależność między uczeniem maszynowym a procesem optymalizacji wyznacza najbardziej istotne osiągnięcia. Optymalizacja odgrywa ważną rolę w przemyśle mechanicznym, ponieważ prowadzi do obniżenia kosztów materiałów, zużycia czasu i wzrostu szybkości produkcji. Ostatnie prace skupiają się na wykonaniu optymalizacji procesu zgrzewania tarciowego z przemieszaniem w celu uzyskania maksymalnej wytrzymałości na rozciąganie (UTS) połączeń zgrzewanych tarciowo z przemieszaniem. Do tego celu wybrano dwa algorytmy uczenia maszynowego, tj. Sztuczną sieć neuronową (ANN) i model decyzyjnego drzewa regresyjnego. Zmienne wejściowe to prędkość obrotowa narzędzia [obr/min], prędkość posuwu narzędzia [mm/min] i siła osiowa [kN], natomiast zmienną wyjściową jest maksymalna wytrzymałość na rozciąganie [MPa]. Zaobserwowano, że w przypadku sztucznych sieci neuronowych średnie błędy kwadratowe zbiorów uczących i testowych wynoszą odpowiednio 0,842 i 0,808, podczas gdy w przypadku modelu decyzyjnego drzewa regresji zbiory uczące i testujące dają średnie błędy kwadratowe 11,72 i 14,61. Można więc stwierdzić, że algorytm ANN daje lepsze i dokładniejsze wyniki niż algorytm regresji drzewa decyzyjnego.

4

Local binary pattern defect recognition approach for the friction stir welded AA 1200 and AA 6061-T6 aluminum alloy

Mishra Akshansh

Journal of Mechanical and Energy Engineering

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2020

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Vol. 4, No 1

27--32

EN

The research reported in this paper focuses on the application of local binary patterns (LBPs) for surface defects detection. The surface defection detection algorithm for friction stir welded aluminum plates is the key part of the entire surface defect recognition system. Two different grades i.e AA 1200 and AA 6061 plates were similarly joined with the help of Friction Stir Welding process. Python codes for the proposed algorithm were executed on Google Colaboratory platform. The results obtained prove that the local binary patterns method can be used for real-time surface defects detection in friction stir welded joints.

5

Discrete Wavelet Transformation Approach for Surface Defects Detection in Friction Stir Welded Joints

Mishra Akshansh

Fatigue of Aircraft Structures

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2020

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Iss. 12

27--35

EN

Friction Stir Welding joint quality depends on input parameters such as tool rotational speed, tool traverse speed, tool tilt angle and an axial force. Surface defects formation occurs when these input parameters are not selected properly. The main objective of the recent paper is to develop Discrete Wavelet Transform algorithm by using Python programming and further subject it to the Friction Stir Welded samples for the identification of various external surface defects present.

6

Regression modeling and neural computing for predicting the ultimate tensile strength of friction stir welded aerospace aluminium alloy

Mishra Akshansh, Vance Jonathan Ve

Journal of Mechanical and Energy Engineering

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2019

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Vol. 3, No 3

221--226

EN

AA7075 is an aluminum alloy which is almost as strong as steel, yet it weighs just one third as much. Unfortunately its use has been limited, due to the fact that pieces of it could not be securely welded together by the traditional welding process. Friction Stir Welding (FSW) process overcomes the limitations of conventional welding process. In our present work we have used Artificial Neural Network which is Artificial Intelligence based technique used for prediction purpose. The main objective of our present work is to compare the predicted results of the Ultimate Tensile Strength (UTS) of Friction Stir welded similar joints through Regression modeling and Artificial Neural Network (ANN) modeling. It was observed that the linear regression algorithm is able to make more accurate predictions compared to neural network algorithm for small dataset.