A Research on the Effect of Retrogression and Re-Aging Heat Treatment on Hot Tensile Properties of AA7075 Aluminum Alloys

Aluminum alloys are preferred in most industries due to the functional properties they provide. It is known that alloys that can be processed with heat treatments show better mechanical properties. 7xxx series alloys can be processed via heat treatments and are often used in environmental conditions such as extreme temperatures and corrosive environments. Corrosive sensitivities such as stress corrosion cracking can be observed with the effect of working conditions. It is known that retrogression and re-aging heat treatment provide corrosion resistance and decrease the stress corrosion cracking velocity. The purpose of this study is to examine the tensile behavior of annealed and retrogression-re-aging heat-treated AA7075 alloys at elevated temperatures. The mechanical properties of the alloys were investigated by conducting tensile tests at room temperature, 100, 200, and 300 °C. Hardness tests were performed at room temperature on the samples that were taken from tensile test specimens after tensile tests. The potential effects of test temperature on mechanical and microstructural properties were examined. The annealed and RRA heat-treated alloys were characterized by scanning electron microscope and X-ray diffraction analysis. As a result, an increase in strength and hardness of the retrogression-re-aging treated AA7075 alloys was observed. The ductility of the retrogression-re-aging treated alloy was lower compared to the annealed AA7075 alloy. Fracture surface examinations showed that there was a semi-ductile fracture below 200 °C and ductile fracture at temperatures of 200 and 300 °C. Ductility was observed to increase with increasing temperature

Mechanical and wear performance of A356/Al2O3 aluminum nanocomposites by considering the mechanical milling time and microstructural properties

Purpose: The paper aims to examine the mechanical and wear performance of A356/Al2O3 (alumina) nanocomposites. The correlation between wear performance and the microstructural properties that result from various mechanical milling periods was investigated. Design/methodology/approach: The production of nano alumina reinforced (1 Wt.%) A356 aluminum nanocomposite specimens was carried out using the traditional powder metallurgy method, incorporating three different mechanical milling times (1, 2 and 4 h). Subsequently, mechanical and wear performance assessments were conducted using hardness, compression and pin-on-disc wear tests. Findings: Although the specimens subjected to the most prolonged mechanical milling (4 h) demonstrated superior hardness and compressive strength properties, they exhibited a remarkable weight loss during the wear tests. The traditional evaluation, which supports that the wear performance is generally correlated with hardness, does not consider the microstructural properties. Since the sample milled for 1 h has a moderate microstructure, it showed better wear performance than the sample with higher hardness. Originality/value: The originality of the paper is demonstrated through its evaluation of wear performance, incorporating not only hardness but also the consideration of microstructural properties resulted from mechanical milling. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-02-2023-0031

Framework of controlling 3d virtual human emotional walking using BCI

A Brain-Computer Interface (BCI) is the device that can read and acquire the brain activities. A human body is controlled by Brain-Signals, which considered as a main controller. Furthermore, the human emotions and thoughts will be translated by brain through brain signals and expressed as human mood. This controlling process mainly performed through brain signals, the brain signals is a key component in electroencephalogram (EEG). Based on signal processing the features representing human mood (behavior) could be extracted with emotion as a major feature. This paper proposes a new framework in order to recognize the human inner emotions that have been conducted on the basis of EEG signals using a BCI device controller. This framework go through five steps starting by classifying the brain signal after reading it in order to obtain the emotion, then map the emotion, synchronize the animation of the 3D virtual human, test and evaluate the work. Based on our best knowledge there is no framework for controlling the 3D virtual human. As a result for implementing our framework will enhance the game field of enhancing and controlling the 3D virtual humans’ emotion walking in order to enhance and bring more realistic as well. Commercial games and Augmented Reality systems are possible beneficiaries of this technique. © 2015 Penerbit UTM Press. All rights reserved

Investigation of Mechanical Properties of AA7075 Alloys Aged by Various Heat Treatments

Abstract: In this study, annealing (O), artificial aging (T6), retro-regression aging (RRA) and high temperature pre-precipitation (HTPP) heat treatments were applied to AA7075 aluminium alloys. The effects of these treatments on the mechanical properties of AA7075 alloy were investigated. The microstructures of the samples were examined by Optical Microscope (OM), Scanning Electron Microscope (SEM) and Energy Dispersive X-ray (EDX) analysis. Then, X-ray diffraction analysis (XRD) was conducted to identify intermetallics formed in the microstructure of the samples. Tensile and hardness tests were carried out to investigate the mechanical properties. Results showed that secondary phase particles such as Al2Cu, Al2CuMg and MgZn2 are formed in the microstructures. In terms of the mechanical properties, T6 applied samples showed the best results. The HTPP applied alloy which presented optimum ductility behaviour among the other heat-treated samples. Dimples and some cleavage surfaces were observed on the fracture surfaces of the samples. Therefore, it is concluded that a ductile/semi-ductile fracture occurred on the samples

Background Subtraction Methods in Video Streams: A Review

Background subtraction is one of the most important parts in image and video processing field. There are some unnecessary parts during the image or video processing, and should be removed, because they lead to more execution time or required memory. Several subtraction methods have been presented for the time being, but find the best-suited method is an issue, which this study is going to address. Furthermore, each process needs to the specific subtraction technique, and knowing this issue helps researchers to achieve faster and higher performance in their research. This paper presents a comparative study of several existing background subtraction methods which have been investigated from simple background subtraction to more complex statistical techniques. The goal of this study is to provide a view of the strengths and drawbacks of the widely used methods. The methods are compared based on their memory requirement, the computational time and their robustness of different videos. Finally, a comparison between the existing methods has been employed with some factors like computational time or memory requirements. It is also hoped that this analysis helps researchers to address the difficulty of selecting the most convenient method for background subtraction

Adaptation of Deeplab V3+ for Damage Detection on Port Infrastructure Imagery

Regular inspection and maintenance of infrastructure facilities are crucial to ensure their functionality and safety for users. However, current inspection methods are labor-intensive and can vary depending on the inspector. To improve this process, modern sensor systems and machine learning algorithms can be deployed to detect defects based on rapidly acquired data, resulting in lower downtime. A quality-controlled processing chain allows to provide hence informed uncertainty assessments to inspection operators. In this study, we present several Deeplab V3+ models optimized to predict corroded segments of the quay wall at JadeWeserPort, Germany, which is a dataset from the 3D HydroMapper research project. Our models achieve generally high accuracy in detecting this damage type. Therefore, we examine the use of a Region Growing-based weakly supervised approach to efficiently extend our model to other common types in the future. This approach achieves about 90 % of the results compared to corresponding fully supervised networks, of which a ResNet-50 variant peaks at 55.6 % Intersection-over-Union regarding the test set's corrosion class

A Debiasing Variational Autoencoder for Deforestation Mapping

Deep Learning (DL) algorithms provide numerous benefits in different applications, and they usually yield successful results in scenarios with enough labeled training data and similar class proportions. However, the labeling procedure is a cost and time-consuming task. Furthermore, numerous real-world classification problems present a high level of class imbalance, as the number of samples from the classes of interest differ significantly. In various cases, such conditions tend to promote the creation of biased systems, which negatively impact their performance. Designing unbiased systems has been an active research topic, and recently some DL-based techniques have demonstrated encouraging results in that regard. In this work, we introduce an extension of the Debiasing Variational Autoencoder (DB-VAE) for semantic segmentation. The approach is based on an end-to-end DL scheme and employs the learned latent variables to adjust the individual sampling probabilities of data points during the training process. For that purpose, we adapted the original DB-VAE architecture for dense labeling in the context of deforestation mapping. Experiments were carried out on a region of the Brazilian Amazon, using Sentinel-2 data and the deforestation map from the PRODES project. The reported results show that the proposed DB-VAE approach is able to learn and identify under-represented samples, and select them more frequently in the training batches, consequently delivering superior classification metrics

Kinstate intervention in ethnic conflicts : Albania and Turkey compared

Albania and Turkey did not act in overtly irredentist ways towards their ethnic brethren in neighboring states after the end of communism. Why, nonetheless, did Albania facilitate the increase of ethnic conflict in Kosovo and Macedonia, while Turkey did not, with respect to the Turks of Bulgaria? I argue that kin-states undergoing transition are more prone to intervene in external conflicts than states that are not, regardless of the salience of minority demands in the host-state. The transition weakens the institutions of the kin-state. Experiencing limited institutional constraints, self-seeking state officials create alliances with secessionist and autonomist movements across borders alongside their own ideological, clan-based and particularistic interests. Such alliances are often utilized to advance radical domestic agendas. Unlike in Albania's transition environment, in Turkey there were no emerging elites that could potentially form alliances and use external movements to legitimize their own domestic existence or claims