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Journal articles on the topic 'Algorithm aluminum'

Author: Grafiati

Published: 28 May 2022

Last updated: 30 July 2025

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1

Mohamed, Ali, and Adel Khalleefah Hamad Darmeesh. "Aluminum and Aluminum Alloys Systematic mapping on different Aluminum and Aluminum Alloys practices and benefits and outcome performance by Weka application." International Journal of Technology and Education Research 1, no. 03 (2023): 152–76. https://doi.org/10.63922/ijeter.v1i03.479.

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Abstract Aluminum is one of the most adaptable, cost-effective, and visually appealing metallic materials available for a variety of applications, from soft, highly ductile wrapping foil to the most rigorous engineering ones. Furthermore, this is due to the special combinations of properties that aluminum as well as its alloys offer. Moreover, the only other metal used as a structural material after steel is aluminum alloys. Just 2.7 g/cm3 is what aluminum is made of about a third as much as steel (7.83 g/cm3). Aluminum weighs just around 170 lb per cubic foot, compared to approximately 490 lb

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2

Dai, Jiayang, Hangbin Liu, Yichu Zhang, Haofan Shi, and Peirun Ling. "An Optimization Design of Energy Consumption for Aluminum Smelting Based on a Multi-Objective Artificial Vulture Algorithm." Metals 15, no. 2 (2025): 105. https://doi.org/10.3390/met15020105.

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In the process of regenerative aluminum smelting, the temperature of the furnace needs to be maintained between 700 and 850 by adjusting the setting parameters of the smelting furnace. The setting parameters are usually adjusted by manual work, and inaccuracies in manual operation can lead to wasted energy as well as unstable temperatures. Energy consumption and temperature stability are two conflicting objectives, which are difficult to find optimal parameters for the aluminum smelting process. In this paper, an improved multi-objective artificial vulture algorithm (IMOAVOA) is developed to s

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3

Cherkunov, V. I. "Optimization of Unevenness of Aluminum and Aluminum Oxide Coatings Obtained by Magnetron Sputtering under Mutual Movement of Substrate and Magnetron." LETI Transactions on Electrical Engineering & Computer Science 18, no. 1 (2025): 14–21. https://doi.org/10.32603/2071-8985-2025-18-1-14-21.

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The application of optical coatings to products of relatively large dimensions is an urgent task in the production of optical devices. This paper considers a method for obtaining a coating of aluminum and aluminum oxides on optical parts with a diameter of up to 300 mm. To that end, a system with a mutual displacement of the plate and the magnetron with a target diameter of 100 mm was used. Such systems require certain motion algorithms. The algorithms should enable a high evenness of the coating on the products. The work was focused on simulation and selection of a magnetron displacement algo

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4

Fan, Chao Hua, Yu Ting He, Heng Xi Zhang, Hong Peng Li, and Feng Li. "Predictive Model Based on Genetic Algorithm-Neural Network for Fatigue Performances of Pre-Corroded Aluminum Alloys." Key Engineering Materials 353-358 (September 2007): 1029–32. http://dx.doi.org/10.4028/www.scientific.net/kem.353-358.1029.

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In the paper, genetic algorithm is introduced in the study of network authority values of BP neural network, and a GA-NN algorithm is established. Based on this genetic algorithm-neural network method, a predictive model for fatigue performances of the pre-corroded aluminum alloys under a varied corrosion environmental spectrum was developed by means of training from the testing dada, and the fatigue performances of pre-corroded aluminum alloys can be predicted. The results indicate that genetic algorithm-neural network algorithm can be employed to predict the underlying fatigue performances o

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5

Wang, Teng, Jianhuan Su, Chuan Xu, and Yinguang Zhang. "An Intelligent Method for Detecting Surface Defects in Aluminium Profiles Based on the Improved YOLOv5 Algorithm." Electronics 11, no. 15 (2022): 2304. http://dx.doi.org/10.3390/electronics11152304.

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In response to problems such as low recognition rate, random distribution of defects and large-scale differences in the detection of surface defects of aluminum profiles by other state-of-the-art algorithms, this paper proposes an improved MS-YOLOv5 model based on the YOLOv5 algorithm. First, a PE-Neck structure is proposed to replace the neck part of the original algorithm in order to enhance the model’s ability to extract and locate defects at different scales. Secondly, a multi-streamnet is proposed as the first detection head of the algorithm to increase the model’s ability to identify dis

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Nargundkar, Aniket, Satish Kumar, and Arunkumar Bongale. "Multi-Objective Optimization of Friction Stir Processing Tool with Composite Material Parameters." Lubricants 12, no. 12 (2024): 428. https://doi.org/10.3390/lubricants12120428.

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Compared to base aluminum alloys, the surface composites of aluminum alloys are more widely used in the automotive, aerospace, and other industries. The ability to yield enhanced physical properties and a smoother microstructure has made friction stir processing (FSP) the method of choice for developing aluminum-based surface composites in recent times. In this work, the Goal Programming (GP) approach is adopted for the Multi-Objective Optimization of FSP processes with three Artificial Intelligence (AI)-based metaheuristics, viz., Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO)

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Liu, Congbiao. "Research on Key Technologies for Optimization of Forming Parameters of Aluminum Alloy Hood Outer Panel." International Journal of Mechanical and Electrical Engineering 2, no. 2 (2024): 125–30. http://dx.doi.org/10.62051/ijmee.v2n2.14.

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At present, computer technology and finite element software analysis technology have been widely used in the stamping and forming process of aluminum alloy hood outer plate to predict the possible quality defects in the stamping process, and effectively improve the quality and production efficiency of parts. The stamping and forming of the outer plate of the automobile aluminum alloy hood is a highly nonlinear process, and its forming quality involves many forming factors, if the variable design is not reasonable, it may lead to quality defects such as wrinkles or cracks of the molded parts. D

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8

Han, Guo Ju. "Aluminum-Clad Invar Super Heat-Resistant Aluminium Alloy Stranded Wire Sag Calculation." Applied Mechanics and Materials 441 (December 2013): 58–61. http://dx.doi.org/10.4028/www.scientific.net/amm.441.58.

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Comparison with conventional ACSR aluminum clad ultra-resistant aluminum alloy invar wire with low sag, load flow characteristics, linear expansion coefficient of the wire there is migration point, the migration point temperature coefficient of linear expansion around the wire is different, therefore can not be directly calculated using the equation of state migration point directly after the aluminum clad super-resistant aluminum alloy invar wire sag. This combined with engineering examples, the wire sag derived formulas, combined with the current reality of the design units, presents a pract

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9

Li, Jie Jia, Jie Li, Rui Qu, and Ying Li. "Application Research on Fault Diagnosis Based on Improved Wavelet Neural Network." Advanced Materials Research 499 (April 2012): 268–72. http://dx.doi.org/10.4028/www.scientific.net/amr.499.268.

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Aluminum electrolysis is a nonlinear, multi-couplings, time-variable and large time-delay industrial process system. The paper puts forward the fault diagnoisis method of improved wavelet Elman neural network, which firstly simplifies the input of network with the method of principal component analysis, secondly, the weights, as well as scale factor and shift factor of the wavelet function are optimized by use of the wavelet Elman network which is optimized by improved particle swarm algorithm. Then it is verified by the simulation. The simulation results show that the method can precisely for

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10

Zhang, Wen, Shibao Sun, and Huanjing Yang. "Research on aluminum defect classification algorithm based on deep learning with attention mechanism." Frontiers in Computing and Intelligent Systems 2, no. 1 (2022): 101–5. http://dx.doi.org/10.54097/fcis.v2i1.3173.

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Product quality is an important indicator for determining the quality of industrial products. Defects on the surface of aluminum profiles are inevitably caused in the actual production process due to the influence of various factors such as environment and equipment, and these defects seriously affect the quality of aluminum profiles. The focus and difficulty of research have shifted to how to quickly and accurately identify and classify surface defects in aluminum profiles. To address this issue, this paper proposes an aluminum defect classification algorithm that uses an attention mechanism

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Wang, Jie, Yan Sha Zhang, Feng Pan, and Lin Wang. "Defect Detection of Aluminum Profiles based on Improved Feature Pyramids." MATEC Web of Conferences 380 (2023): 01016. http://dx.doi.org/10.1051/matecconf/202338001016.

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For the surface defects of aluminum profiles, there are problems of multi-scale, small object and irregular shape. This paper proposes a defects detection algorithm based on improved feature pyramid. This method compresses and saves the feature information extracted by the backbone networks, and calculates the similarity between deep and shallow features, so as to alleviate the phenomenon of loss of feature information and weakening of feature expression ability, thereby solving the problem of multi-scale and small object. At the same time, deformable convolution is introduced to enhance the f

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12

Gao, Tianhao, Ke Zhang, Huaitao Shi, Jinbao Zhao, and Jiejia Li. "A two-stage classifier switchable aluminum electrolysis fault diagnosis method." Transactions of the Institute of Measurement and Control 44, no. 8 (2021): 1708–20. http://dx.doi.org/10.1177/01423312211059637.

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Traditional aluminum electrolysis fault diagnosis methods have problems such as low accuracy, small forecast advance, and high CPU usage, which make their popularity low in enterprises. Aiming at the above problems, a fault diagnosis method with switchable two-level classifiers is designed. The input data are first judged by the first-level algorithm. If it is determined that there is no fault, the result will be output directly. If it is determined that there is a fault in the electrolytic cell, the data will be transferred to the second-level network for specific fault diagnosis. The first l

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13

MISHRA, AKSHANSH. "Information Retrieval in Friction Stir Welding of Aluminum Alloys by using Natural Language Processing based Algorithms." Welding Technology Review 96 (August 29, 2024): 147–54. http://dx.doi.org/10.26628/simp.wtr.v96.1158.147-154.

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Text summarization is a technique for condensing a big piece of text into a few key elements that give a general impression of the content. When someone requires a quick and precise summary of a large amount of information, it becomes vital. If done manually, summarizing text can be costly and time-consuming. Natural Language Processing (NLP) is the sub-division of Artificial Intelligence that narrows down the gap between technology and human cognition by extracting the relevant information from the pile of data. In the present work, scientific information regarding the Friction Stir Welding o

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14

Fan, Chao Hua, Yu Ting He, Hong Peng Li, and Feng Li. "Performance Prediction of Pre-Corroded Aluminum Alloy Using Genetic Algorithm-Neural Network and Fuzzy Neural Network." Advanced Materials Research 33-37 (March 2008): 1283–88. http://dx.doi.org/10.4028/www.scientific.net/amr.33-37.1283.

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Genetic algorithm is introduced in the study of network authority values of BP neural network, and a GA-NN algorithm is established. Based on this genetic algorithm-neural network method, a predictive model for fatigue performances of the pre-corroded aluminum alloys under a varied corrosion environmental spectrum was developed by means of training from the testing dada. At the same time, a fuzzy-neural network method is established for the same purpose. The results indicate that genetic algorithm-neural network and fuzzy-neural network can both be employed to predict the underlying fatigue pe

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15

Lilansa, Noval, Rizqi Aji Pratama, Richard Tanadi, and Renold Nindi Kara Natasasmita. "PENGUJIAN ALGORITMA YOLO UNTUK DETEKSI CACAT PADA PRODUK HASIL ALUMUNIUM CASTING PADA INDUSTRI OTOMOTIF." JTT (Jurnal Teknologi Terapan) 10, no. 2 (2024): 118. https://doi.org/10.31884/jtt.v10i2.664.

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This research addresses the challenges faced by one of the automotive parts manufacturing plants in Indonesia, in detecting leaks in car components manufactured through aluminum die casting. Existing manual monitoring methods are time-consuming, prone to human error, and pose risks to product quality and operational safety. To overcome these challenges, this research proposes the application of machine learning, specifically computer vision techniques, such as Object Detection to identify and localize gas bubbles using a Leak Tester Machine equipped with a camera sensor equipped with the YOLO

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16

Zhang, Qi, Hongqun Tang, Yong Li, Bing Han, and Jiadong Li. "Improved Method Based on Retinex and Gabor for the Surface Defect Enhancement of Aluminum Strips." Metals 13, no. 1 (2023): 118. http://dx.doi.org/10.3390/met13010118.

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Aiming at the problems of the blurred image defect contour and the surface texture of the aluminum strip suppressing defect feature extraction when collecting photos online in the air cushion furnace production line, we propose an algorithm for the surface defect enhancement and detection of aluminum strips based on the Retinex theory and Gobar filter. The Retinex algorithm can enhance the information and detail part of the image, while the Gobar algorithm can maintain the integrity of the defect edges well. The method first improves the high-frequency information of the image using a multi-sc

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17

Wang, Zhen, Wen Bin Gu, Xing Bo Xie, Qi Yuan, Yu Tian Chen, and Tao Jiang. "Explosion Resistance of Three-Dimensional Mesoscopic Model of Complex Closed-Cell Aluminum Foam Sandwich Structure Based on Random Generation Algorithm." Complexity 2020 (July 29, 2020): 1–16. http://dx.doi.org/10.1155/2020/8390798.

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According to the randomness of the spatial distribution and shape of the internal cells of closed-cell foam aluminum and based on the Voronoi algorithm, we use ABAQUS to model the random polyhedrons of pore cells firstly. Then, the algorithm of generating aluminum foam with random pore size and random wall thickness is written by Python and Fortran, and the mesh model of random polyhedral particles and random wall thickness was established by the algorithm read in by TrueGrid software. Finally, the mesh model is impo rted into the LS-DYNA software to remove the random polyhedron part of the po

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18

Liu, Shou Shan, Chuan Jiang Wang, Li Jun Bi, and Chang Zhi Lv. "Application of Adaptive Wavelet Thresholding to Ultrasonic Signal Compression of Aluminum Alloy Forgings." Advanced Materials Research 658 (January 2013): 89–92. http://dx.doi.org/10.4028/www.scientific.net/amr.658.89.

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In this paper, for the purpose of ultrasonic signal compression and the coherent noise depressing in nondestructive test of aluminum alloy forging, the mathematical model of defect echoes is discussed and confirmed. And then the wavelet kernel is also confirmed according the waveform of the defect echoes. As the algorithms of standard hard thresholding and soft thresholding of wavelet transform can not bring out effective compression and depression to the coherent noise, an adaptive wavelet thresholding algorithm is introduced. Experimental results indicate that the adaptive wavelet thresholdi

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19

Liu, Zhen Hua, Hai Bo Shi, and Xiao Feng Zhou. "Aluminum Profile Type Recognition Based on Texture Features." Applied Mechanics and Materials 556-562 (May 2014): 2846–51. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.2846.

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Because aluminum profile’s structure is complex and diverse, we need to match the different parameters for different profiles before automated detection of surface defects of aluminum profile. This process often requires manual input, affecting the detection efficiency. To solve this problem, we analyze the characteristics of aluminum profile, through GLCM algorithm and Gabor wavelet transform methods, which are image texture feature extraction methods to get aluminum profile’s texture feature, then we use the Support Vector Machine (SVM) classification algorithm based on radial basis function

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20

Celen, Burak, Melik Bugra Ozcelik, Furkan Metin Turgut, et al. "Calendar ageing modelling using machine learning: an experimental investigation on lithium ion battery chemistries." Open Research Europe 2 (August 12, 2022): 96. http://dx.doi.org/10.12688/openreseurope.14745.1.

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Background: The phenomenon of calendar ageing continues to have an impact on battery systems worldwide by causing them to have undesirable operation life and performance. Predicting the degradation in the capacity can identify whether this phenomenon is occurring for a cell and pave the way for placing mechanisms that can circumvent this behaviour. Methods: In this study, the machine learning algorithms, Extreme Gradient Boosting (XGBoost) and artificial neural network (ANN) have been used to predict the calendar ageing data belonging to six types of cell chemistries namely, Lithium Cobalt Oxi

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21

Leni, Desmarita, Yuda Perdana Kusuma, Muchlisinalahuddin Muchlisinalahuddin, Femi Earnerstly, Riza Muharni, and Ruzita Sumiati. "PERANCANGAN METODE MACHINE LEARNING BERBASIS WEB UNTUK PREDIKSI SIFAT MEKANIK ALUMINIUM." Jurnal Rekayasa Mesin 14, no. 2 (2023): 611–26. http://dx.doi.org/10.21776/jrm.v14i2.1370.

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The main objective of this research is to design a web-based machine learning model that can predict the mechanical properties of aluminum based on its chemical composition. By inputting nine variables of chemical elements such as Al, Mg, Zn, Ti, Cu, Mn, Cr, Fe, and Si, the model is able to provide predictions for two output data, Yield Strength (YS) and Tensile Strength (TS). The research aims to understand the relationship between chemical composition and mechanical properties of aluminum, and to develop a tool that can be used to predict these properties with a high level of accuracy. Overa

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Celen, Burak, Melik Bugra Ozcelik, Furkan Metin Turgut, et al. "Calendar ageing modelling using machine learning: an experimental investigation on lithium ion battery chemistries." Open Research Europe 2 (February 22, 2023): 96. http://dx.doi.org/10.12688/openreseurope.14745.2.

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Background: The phenomenon of calendar ageing continues to have an impact on battery systems worldwide by causing them to have undesirable operation life and performance. Predicting the degradation in the capacity can identify whether this phenomenon is occurring for a cell and pave the way for placing mechanisms that can circumvent this behaviour. Methods: In this study, the machine learning algorithms, Extreme Gradient Boosting (XGBoost) and artificial neural network (ANN) have been used to predict the calendar ageing data belonging to six types of cell chemistries namely, Lithium Cobalt Oxi

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Li, Jiejia, Tianhao Gao, and Xinyang Ji. "Multi-model and multi-level aluminum electrolytic fault diagnosis method." Transactions of the Institute of Measurement and Control 41, no. 15 (2019): 4409–23. http://dx.doi.org/10.1177/0142331219859786.

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A multi-model and multi-level aluminum electrolytic fault prediction method is proposed. In this method, it innovatively uses the image recognition technology to predict aluminum electrolytic faults, and superimposes the chaotic neural network model to form a dual-model parallel fault prediction system for aluminum electrolysis, which can obtain more faults information from different angles. Then, it designs the decision fusion layer, which combines the prediction results of the above two models to output the final prediction results and enhances the credibility of the prediction results. In a

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Wang, Zhen, Wen Bin Gu, Xing Bo Xie, Yu Tian Chen, and Lei Fu. "Meso-Complexity Computer Simulation Investigation on Antiexplosion Performance of Double-Layer Foam Aluminum under Pore Grading." Complexity 2020 (September 22, 2020): 1–13. http://dx.doi.org/10.1155/2020/4121926.

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Foam aluminum is an energy-absorbing material with excellent performance. The interlayer composed of multiple layers of foam aluminum and steel plate has good antiexplosion ability. In order to explore the antiexplosion performance of double-layer foam aluminum under different porosity rankings and to reveal its microscopic deformation law and failure mechanism, three kinds of aluminum foams with a porosity of 80%, 85%, and 90% were selected to form six different structures. Based on the Voronoi algorithm, a three-dimensional foam aluminum generation algorithm with random pore size and random

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Chang, Jincai, Zhihang Wang, Qingyu Zhu, and Zhao Wang. "SVR Prediction Algorithm for Crack Propagation of Aviation Aluminum Alloy." Journal of Mathematics 2020 (November 28, 2020): 1–12. http://dx.doi.org/10.1155/2020/1034639.

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Aluminum alloy material is an important component material in the safe flight of aircraft. It is very important and necessary to predict the fatigue crack growth between holes of aviation aluminum alloy materials. At present, the investigation on the prediction of the cracks between two holes and multiholes is a key problem to be solved. Due to the fact that the fatigue crack growth test of aluminum alloy plate with two or three holes was carried out by the MTS fatigue testing machine, the crack length growth data under different test conditions were obtained. In this paper, support vector reg

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Jiang, Ming Shun, Xiang Yang Li, Shi Cheng Wang, et al. "Impact Localization System by Using FBG Sensors and Extreme Learning Machine Algorithm." Applied Mechanics and Materials 740 (March 2015): 664–67. http://dx.doi.org/10.4028/www.scientific.net/amm.740.664.

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Aluminum alloy structure impact localization system by using fiber Bragg grating (FBG) sensors and impact localization algorithm were investigated. The impact localization method was proposed based on Extreme Learning Machine (ELM). Cross-correlation analysis method was used to extract the impact signal time difference. And ELM was used to realize impact localization. ELM model’s input was signal time difference and the output was the impact location. At last, FBG impact localization system was established. In aluminum alloy plate’s 500mm*500mm*2mm experiment area, the FBG impact localization

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27

Chen, Suokun, and Qinmin Zhang. "Study on constitutive model and mechanical performance test simulation of 6082-T6 based on genetic optimization algorithm." Journal of Physics: Conference Series 2808, no. 1 (2024): 012062. http://dx.doi.org/10.1088/1742-6596/2808/1/012062.

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Abstract Through the tensile test of 6082-T6 aluminum alloy sample, the mechanical properties parameters of the material (specified non-proportional tensile strength, tensile strength, elastic modulus E, elongation at break, proportional limit, Poisson’s ratio and strain hardening coefficient N) and the calculation method of strain hardening coefficient N of the constitutive model are analyzed and studied. A method of compiling a genetic algorithm (Ga) based on the Matlab platform to solve the hardening index of aluminum alloy materials is proposed. The results show that 6082-T6 aluminum alloy

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Ahmad, Mubarak, Zenghua Liu, Abdul Basit, Wenshuo Jiang, Wu Bin, and Cunfu He. "Quantification of defects in an aluminum plate using direction-tunable shear horizontal wave imaging." E3S Web of Conferences 233 (2021): 04030. http://dx.doi.org/10.1051/e3sconf/202123304030.

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The quantification of damage, in plates, pipes and such like structures, is one of the current research areas. In this study, to estimate the notch size, shape, and orientation was carried out experimentally based on fundamental shear horizontal mode, SH0 mode. Using least number of transmitters, the reconstruction algorithm for the probabilistic inspection of damage (RAPID) has been used to carry out fast and efficient investigation of scattered waves using SH0 wave interaction with notch at various incident angles. An approach of ultrasonic guided waves and (RAPID) algorithm, using a directi

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Yang, Dalian, Yilun Liu, Songbai Li, Jie Tao, Chi Liu, and Jiuhuo Yi. "Fatigue crack growth prediction of 7075 aluminum alloy based on the GMSVR model optimized by the artificial bee colony algorithm." Engineering Computations 34, no. 4 (2017): 1034–53. http://dx.doi.org/10.1108/ec-11-2015-0362.

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Purpose The aim of this paper is to solve the problem of low accuracy of traditional fatigue crack growth (FCG) prediction methods. Design/methodology/approach The GMSVR model was proposed by combining the grey modeling (GM) and the support vector regression (SVR). Meanwhile, the GMSVR model parameter optimal selection method based on the artificial bee colony (ABC) algorithm was presented. The FCG prediction of 7075 aluminum alloy under different conditions were taken as the study objects, and the performance of the genetic algorithm, the particle swarm optimization algorithm, the n-fold cros

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Miao, Xiaoting, Dong Wang, Lin Ye, Ye Lu, Fucai Li, and Guang Meng. "Identification of dual notches based on time-reversal lamb waves and a damage diagnostic imaging algorithm." Journal of Intelligent Material Systems and Structures 22, no. 17 (2011): 1983–92. http://dx.doi.org/10.1177/1045389x11421821.

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An integration of time-reversal Lamb wave signals from a sensor network and a damage diagnostic imaging algorithm is developed to identify dual notches in an aluminum plate. The time reversibility of Lamb waves for one wave propagation path in an aluminum plate is investigated using dynamic finite element analysis (FEA). A time-reversal-based damage index (DI) is calibrated by correlation of the reconstructed waveform and the original activated tone burst, when the fundamental symmetric (S0) mode alone is reversed or when both the S0 mode and the fundamental antisymmetric (A0) mode are reverse

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Feng, Yin-An, and Wei-Wei Song. "Surface Defect Detection for Aerospace Aluminum Profiles with Attention Mechanism and Multi-Scale Features." Electronics 13, no. 14 (2024): 2861. http://dx.doi.org/10.3390/electronics13142861.

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A YOLOv5 aluminum profile defect detection algorithm that integrates attention and multi-scale features is proposed in this paper to address the issues of the low detection accuracy, high false detection rates, and high missed detection rates that are caused by the large-scale variation of surface defects, inconspicuous small defect characteristics, and a lack of concentrated feature information in defect areas. Firstly, an improved CBAM (Channel-Wise Attention Module) convolutional attention module is employed, which effectively focuses on the feature information of defect areas in the alumin

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Li, Guang Yu, and Liang Hou. "The Automatic Detection System Development of Aluminum Production Line Quality." Applied Mechanics and Materials 462-463 (November 2013): 525–28. http://dx.doi.org/10.4028/www.scientific.net/amm.462-463.525.

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Aluminum electrolytic capacitors in the production process, in order to ensure product quality, reduce consumption, should happen quickly detect aluminum surface quality of this paper, digital image processing technology as the core, collecting images in different directions on the computer using a variety of assistance and recognition algorithm to achieve the automatic detection of defects in aluminum. The system will greatly improve production efficiency, reduce labor intensity and improve the working environment of the scene.

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Xiong, Zhenqiang, Jiadong Li, Peng Zhao, and Yong Li. "Prediction of Mechanical Properties of Aluminium Alloy Strip Using the Extreme Learning Machine Model Optimized by the Gray Wolf Algorithm." Advances in Materials Science and Engineering 2023 (July 4, 2023): 1–16. http://dx.doi.org/10.1155/2023/5952072.

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Mechanical properties are important indicators for evaluating the quality of strips. This paper proposes a mechanical performance prediction model based on the Gray Wolf Optimization (GWO) algorithm and the Extreme Learning Machine (ELM) algorithm. In the modeling process, GWO is used to determine the optimal weights and deviations of ELM and experiments are used to determine the model’s key parameters. The model effectively avoids manual intervention and significantly improves aluminum alloy strips’ mechanical property prediction accuracy. This paper uses processed data from the aluminum allo

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Tian, Li, and Qi Wang. "Numerical Analysis for Progressive Collapse and Protection of Underground Structure under Internal Blast Load." Applied Mechanics and Materials 226-228 (November 2012): 1039–44. http://dx.doi.org/10.4028/www.scientific.net/amm.226-228.1039.

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Dynamic response of an underground structure with the protection of foamed aluminum under internal blast load, compared with that without any protection, has been investigated numerically in this paper. The three dimensional model of the two-storey and two-span underground structure covered with soil around was built with the explicit dynamic analytical software LS-DYNA. The three-stage simulation method （TSSM） is proposed. And the middle column of the structure is covered with foamed aluminum which provides a better protection for the column under the blast load. The solid-fluid interaction a

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Mishra, Akshansh, and Anish Dasgupta. "Optimization of the Mechanical Property of Friction Stir Welded Heat Treatable Aluminum Alloy by using Bio-Inspired Artificial Intelligence Algorithms." Frattura ed Integrità Strutturale 16, no. 62 (2022): 448–59. http://dx.doi.org/10.3221/igf-esis.62.31.

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The concepts and inspiration of biological evolution in nature are used to create new and effective competing tactics in the burgeoning field of bio-inspired computing optimization algorithms. In the present work, nine specimens of similar alloys i.e., AA6262 were Friction Stir Welded. Spindle Speed (RPM), Traverse Speed (mm/min), and Plunge Depth (mm) were the input parameters while the Ultimate Tensile Strength (MPa) was an output parameter. The main objective of the work is to obtain the maximum optimized Ultimate Tensile Strength (MPa) by using Bio-Inspired Artificial Intelligence Algorith

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Wang, Zequan, and Shengli Lv. "A Salt Spray Corrosion Prediction Model for Aviation Aluminum Alloy Based on MVO-GRNN Algorithm." Journal of Physics: Conference Series 2437, no. 1 (2023): 012050. http://dx.doi.org/10.1088/1742-6596/2437/1/012050.

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Abstract It is very important to focus on the corrosion failure of aluminum alloy materials for airframe because of the increasing of aircraft service time. However, due to the long corrosion test cycle, the number of samples for processing alloy corrosion related data with high cost is very small. In this paper, a corrosion rate prediction model for less sample data sets is proposed: a General Regression Neural Network which using Multi-Verse Optimizer to optimize prarmeters in order to improve accuracy. In this paper, according to the public data of China Corrosion and Protection Network, th

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Zolotarev, C. A., and A. T. Taruat. "PARALLEL IMAGE RECONSTRUCTION USING THE MAXIMUM LIKELIHOOD METHOD USING A GRAPHICS PROCESSOR AND THE OpenGL LIBRARY." Дефектоскопия, no. 6 (December 15, 2024): 28–38. http://dx.doi.org/10.31857/s0130308224060036.

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The creation of fast parallel iterative statistical algorithms based on the use of graphics accelerators is an important and urgent task of great scientific and practical importance. An algorithm based on the method of maximizing the mathematical expectation of maximum likelihood (maximum likelihood expectation MLEM) is considered. MLEM is a numerical method for determining maximum likelihood estimates and, since its first application in the field of image reconstruction in 1982, remains one of the most popular statistical methods of image reconstruction, being the foundation for many other ap

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Xia, Qingfeng, Yin Li, Ning Sun, et al. "A Multi-Objective Genetic Algorithm-Based Predictive Model and Parameter Optimization for Forming Quality of SLM Aluminum Anodes." Crystals 14, no. 7 (2024): 608. http://dx.doi.org/10.3390/cryst14070608.

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Aluminum–air batteries are characterized as “green energy for the 21st century” due to their clear advantages in terms of high current discharge, high specific energy, low cost, and easy-to-obtain electrode materials. This study develops the SLM aluminum anode quality prediction model and evaluates its learning and training results using the BP neural network architecture. By altering the network topology of the SLM aluminum anode quality prediction model, we create a process parameter backpropagation model that takes advantage of the extremely adaptable capabilities of artificial neural netwo

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Ziolkowski, Marek, Hartmut Brauer, and Milko Kuilekov. "Interface identification in magnetic fluid dynamics." Serbian Journal of Electrical Engineering 1, no. 1 (2003): 61–69. http://dx.doi.org/10.2298/sjee0301061z.

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In magnetic fluid dynamics appears the problem of reconstruction of free boundary between conducting fluids, e.g. in aluminum electrolysis cells. We have investigated how the interface between two fluids of different conductivity of a highly simplified model of an aluminum electrolysis cell could be reconstructed by means of external magnetic field measurements using simple genetic algorithm.

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Xu, Chenhua, Le Wang, Xiaofeng Lin, Zhi Li, and Xin Yu. "Intelligent Optimization of Cell Voltage for Energy Saving in Process of Electrolytic Aluminum." Journal of Advanced Computational Intelligence and Intelligent Informatics 20, no. 2 (2016): 231–37. http://dx.doi.org/10.20965/jaciii.2016.p0231.

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Based on the characteristic of cell voltage fluctuations in the process of electrolytic aluminum, a new method based on neural-network-genetic-algorithm (NNGA) for the optimization of cell voltage is proposed in this paper. First, the method of kernel principal component based on analysis of electrolytic aluminum process is used to determine the operating parameters. Second, in order to predict cell voltage in real time, back propagation neural network (BPNN) is used to establish the cell voltage prediction model. Third, the model of the optimization control of cell voltage is constructed, and

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Tang, Junlong, Shenbo Liu, Dongxue Zhao, Lijun Tang, Wanghui Zou, and Bin Zheng. "An Algorithm for Real-Time Aluminum Profile Surface Defects Detection Based on Lightweight Network Structure." Metals 13, no. 3 (2023): 507. http://dx.doi.org/10.3390/met13030507.

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Surface defects, which often occur during the production of aluminum profiles, can directly affect the quality of aluminum profiles, and should be monitored in real time. This paper proposes an effective, lightweight detection method for aluminum profiles to realize real-time surface defect detection with ensured detection accuracy. Based on the YOLOv5s framework, a lightweight network model is designed by adding the attention mechanism and depth-separable convolution for the detection of aluminum. The lightweight network model improves the limitations of the YOLOv5s framework regarding to its

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Laroche, Sylvain, and Clement Forget. "Grain Sizing of Anodized Aluminum by Color Image Analysis." Microscopy Today 5, no. 4 (1997): 17–19. http://dx.doi.org/10.1017/s1551929500061411.

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Grain size characterization in Aluminum alloys can be correlated with thermo-mechanical processing properties. In order to predict the processing characteristics of these alloys under certain combinations of strain, deformation and temperature, the metallographic measure of the grain size can be used. Most of the technigues that have been proposed so far do not provide reliable and reproducible quantitative metallographic measurements of the grain size due to human error. Considering that this manual task is also tedious to perform, a general color image analysis algorithm is proposed to autom

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Kretz, Ferenc, Zoltán Gácsi, and C. Hakan Gür. "Microstructure Characterization of SiC_p-Reinforced Aluminum Matrix Composites by Newly Developed Computer-Based Algorithms." Materials Science Forum 534-536 (January 2007): 909–12. http://dx.doi.org/10.4028/www.scientific.net/msf.534-536.909.

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This paper presents a new approach for analyzing the microstructure of SiCp-reinforced aluminum matrix composites from digital images. Various samples of aluminum matrix composite were fabricated by hot pressing the powder mixtures with certain volume and size combinations of pure Al and SiC particles. Microstructures of the samples were analyzed by computer-based image processing methods. Since the conventional methods are not suitable for separating phases of such complex microstructures, some new algorithms have been developed for the improved recognition of the particles in the metal matri

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Bocko, Jozef, Michael Dorn, and Viera Nohajová. "Application of Evolutionary Algorithm in Elasticity." Applied Mechanics and Materials 816 (November 2015): 363–68. http://dx.doi.org/10.4028/www.scientific.net/amm.816.363.

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This article introduces evolutionary algorithms and their utilization in mechanical engineering. First part of this work describes evolutionary algorithms and their characteristica. The main body of evolutionary algorithms, the selection methods for parents and the types of reproduction are explained in the next part of this article. Termination conditions are also discussed. Finally, the application of evolutionary algorithms to a problem in mechanical engineering is described. Thereby, the material parameters for a Bodner-Partom model describing visco-elastoplastic material behavior are dete

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Lee, Bokyeong, Hyeonggil Choi, Byongwang Min, and Dong-Eun Lee. "Applicability of Formwork Automation Design Software for Aluminum Formwork." Applied Sciences 10, no. 24 (2020): 9029. http://dx.doi.org/10.3390/app10249029.

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In this study, by applying the developed formwork automation design software to three target structures, we reviewed the applicability of the formwork automation design software for the aluminum formwork. To apply the formwork automation design software, we built an aluminum formwork library based on the conversion of two-dimensional (2D) computer-aided design (CAD) data to three-dimensional building information modeling data for all the components of the aluminum formwork. The results of the automated formwork layout on the target structures using the formwork automation design software confi

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Yu, Hui Jun, Wu Wan, Chen Yun, and Cai Biao Chen. "Research on Aluminum-Plastic Blister Drug Image Segmentation Method Based on Improved Otsu Theory." Advanced Materials Research 962-965 (June 2014): 2797–800. http://dx.doi.org/10.4028/www.scientific.net/amr.962-965.2797.

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In the digital image processing, Otsu algorithm uses the criterion of maximum between-cluster to make image segmentation. In this paper, combined with drug defect detection requirements, the new threshold output functions is put forward which studies on the existing two-dimensional Otsu algorithm in a deep way from the computing complexity and integral effect. The improved algorithm improves the computing speed of the algorithm and optimizes the segmentation effect which is a good segmentation algorithm. The effectiveness of the proposed algorithm has been proved by relevant experiments, and t

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Zhang, Hui, Dongmei Liang, Xiaobo Rui, and Zhuochen Wang. "Noncontact Damage Topography Reconstruction by Wavenumber Domain Analysis Based on Air-Coupled Ultrasound and Full-Field Laser Vibrometer." Sensors 21, no. 2 (2021): 609. http://dx.doi.org/10.3390/s21020609.

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Noncontact ultrasonic detection technology is an effective method to detect damage in time. This paper proposes a noncontact damage detection system based on air-coupled ultrasound and full-field laser vibrometer, which realizes the excitation of relatively single-mode guided waves and the wavefield automatic detection. The system performance is verified through experiments, and the experimental wavenumber is consistent with the theoretical dispersion characteristics of the Lamb wave in the A0 mode. Based on this system, the topography reconstruction algorithms, including the Wavenumber Filter

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Leni, Desmarita, Helga Yermadona, Ade Usra Berli, Ruzita Sumiati, and Haris Haris. "Pemodelan Machine Learning untuk Memprediksi Tensile Strength Aluminium Menggunakan Algoritma Artificial Neural Network (ANN)." Jurnal Surya Teknika 10, no. 1 (2023): 625–32. http://dx.doi.org/10.37859/jst.v10i1.4843.

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This research designs a machine learning model using an Artificial Neural Network (ANN) algorithm to predict the tensile strength of aluminum. This research produces a machine learning model that has 8 (eight) input data variables consisting of the percentage of aluminum chemical composition such as Mg, Zn, Ti, Cu, Mn, Cr, Fe, Si, and 1 output (output), namely aluminum tensile strength. This study makes changes to several variations of parameters, such as variations in the number of split data, training cycles, learning rates, and hidden neurons. This Artificial Neural Network (ANN) modeling p

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Zhang, Yilai, Xue Jian Li, Ling Ke Zeng, and Cheng Kang Chang. "Application of Materials Design Based on Genetic Neural Network." Key Engineering Materials 280-283 (February 2007): 1837–40. http://dx.doi.org/10.4028/www.scientific.net/kem.280-283.1837.

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Neural network (NN) is an effective method in the filed of materials design, but the convergent speed is decided by initial weights. This paper proposes genetic neural network algorithm (GNNA) to design materials. Aluminum titanate modification is studied by the method of GNNA. The results indicate the algorithm works well.

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Li, Jie Jia, and Wen Yue Guan. "Research on Aluminum Electrolytic Multi-Fault Diagnosis Method Based on Immune Genetic Algorithm." Advanced Materials Research 706-708 (June 2013): 1159–62. http://dx.doi.org/10.4028/www.scientific.net/amr.706-708.1159.

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As for a wide variety of faults that happen frequently during the aluminum electrolysis process, a new method of multi-fault diagnosis method using neural network based on immune genetic algorithm (IGA) is proposed. IGA has the abilities of searching for global optima and better convergence. By applying these abilities and the diagnosis characteristics of the aluminum electrolysis process, the study builds the layered fault diagnosis model structure . The results of simulations show that this model is of the better ability of convergent on whole solution space and the capacity of fast learning

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