Influence of the chemical composition and process parameters on the mechanical properties of an extruded aluminium alloy for highly loaded structural parts

Terčelj, Milan; Fazarinc, M.; Kugler, G.; Peruš, Iztok

doi:10.1016/j.conbuildmat.2013.03.052

Cited by 12 publications

(7 citation statements)

References 47 publications

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“…Finally, according to the chemical element mapping presented in Figure 9, increased oxide contamination or presence of the oxide layers between aluminum chips were not determined. According to the previous microstructure investigation for aluminum alloy EN AW 6082 usually Al-Mg-Si phases (usually characterized as Al(FeMn)Si and MgSi) can be distinguished [33,34]. Due to the fact that both samples in this investigation were in T6 temper condition, MgSi phase particles were in a form of the fine precipitates and they cannot be clearly distinguished with used magnification and technique, Figure 8.…”

Section: Metallographic Analysismentioning

confidence: 83%

Statistical Analysis of the Combined ECAP and Heat Treatment for Recycling Aluminum Chips Without Remelting

Krolo

Lela

Dumanić

et al. 2019

Metals

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The main aim of this paper is to present an environmentally friendly method for aluminum recycling. Development of new recycling technologies in order to increase scrap reuse potential and CO 2 emission savings are of the main importance for aluminum circular economy. In this paper, aluminum chips waste was recycled without any remelting phase in order to increase energy and material savings. The presented process is usually called solid state recycling or direct recycling. Solid state recycling process consists of chips cleaning, cold pre-compaction and hot direct extrusion followed by a combination of equal channel angular pressing (ECAP) and heat treatment. Influence of holding time during solid solution treatment and both artificial aging time and temperature on mechanical properties of the recycled EN AW 6082 aluminum chips were investigated. A comprehensive number of the experiments were performed utilizing design of experiments approach and response surface methodology. Regression models were developed for describe the influence of heat treatment parameters for presented solid state recycling process on mechanical properties of the recycled samples. Utilizing novel procedure high quality recycled samples were obtained with mechanical properties comparable with commercially produced EN AW 6082 aluminum alloy in T6 temper condition. Metallographic analysis of the recycled samples was also performed.In order to reduce the negative effect on the environment, various authors recognized solid state recycling (SSR) as a possible alternative to the conventional aluminum recycling process [4][5][6][7][8][9]. When SSR is employed, material losses are much smaller because the remelting phase is completely avoided. Aluminum is a highly reactive material and it has a tendency to form aluminum oxide on its surface. This is even more pronounced for lightweight aluminum scrap due to its high surface to mass ratio. During the remelting process this oxide is floating on the melt surface mixed with dross. Therefore, aluminum lightweight scrap (chips, foils, and sheet skeletons) is problematic when conventional recycling technology is used because up to 20% of the aluminum can be lost due to the mixing with dross and burning [9]. According to previous research papers concerned with SSR process, it is possible for material yield to be over 90% when this type of recycling is employed, while energy consumption can be only 10% compared to conventional recycling process that uses remelting [6][7][8]. Furthermore, manufacturing profiles from solid state bonded chips gives a 96% saving in CO 2 emission compared to production from billets made by conventional recycling of aluminum manufacturing waste [8]. In order to produce SSR samples with appropriate quality, the combination of the high temperature, normal stress, shear stress, and plastic deformation should be achieved [6,7]. Therefore, the most used process for SSR is direct hot extrusion, but lately severe plastic deformation (SPD) processes opened new possibilities [10]. SPD i...

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Section: Metallographic Analysismentioning

confidence: 83%

Statistical Analysis of the Combined ECAP and Heat Treatment for Recycling Aluminum Chips Without Remelting

Krolo

Lela

Dumanić

et al. 2019

Metals

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“…Artificial neural networks (ANNs) as methods of artificial intelligence have emerged as powerful tools in materials science, playing a crucial role in understanding and mathematical description of the (properties of) various metallic materials, including aluminum alloys (i.e., [1][2][3][4][5]). The application of ANNs in this field has significantly enhanced our ability to analyze the physical phenomena in different metallic alloys and their performance.…”

Section: Introductionmentioning

confidence: 99%

Automatic Explanation Of Metal’s Phenomena By Application Of Artificial Neural Networks – Use Case For Hot Extruded Aluminum Alloy 6082

Goričan,

Terčelj,

Peruš

2024

Preprint

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Artificial intelligence methods, especially artificial neural networks (ANNs), have recently been successfully used more and more frequently in the mathematical description of physical phenomena in the processing of metallic materials. Results based on measured data of phenomena in real production are often, at least locally, different or more complex than the results of established theories. In general, ANN models, which mostly work as "black boxes", allow us to improve production efficiency by themselves. The fact is, however, that a better understanding of the phenomena under consideration, which is otherwise enabled by classical mathematical approaches in the form of explicit mathematical formulas, could ensure even greater efficiency. In this article, therefore, a general concept is proposed to explain the discussed phenomena in metallic materials. The application of the concept is illustrated on the example of hot extrusion of aluminum alloy 6082, where CAE is used as an artificial neural network to model the phenomenon and predict the key parameters of the phenomenon. ChatGPT is used to explain the results according to the proposed concept. The basic idea is to enable practicing engineers to better understand the considered phenomena and results obtained by ANNs.

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“…These data can be analyzed using artificial intelligence methods, which are now widely used in many fields [17,18]. For the analysis of complex influences of chemical composition and process parameters on mechanical properties, where a sufficiently large database can be collected, the artificial neural network-based methods are particularly suitable [19][20][21][22][23]. In the present work, the Conditional Average Estimator artificial neural network (CAE ANN), which belongs to the probabilistic types of neural networks, was used for the analysis.…”

Section: Introductionmentioning

confidence: 99%

Contour Maps for Simultaneous Increase in Yield Strength and Elongation of Hot Extruded Aluminum Alloy 6082

Peruš

Kugler

Malej

et al. 2022

Metals

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In this paper, the Conditional Average Estimator artificial neural network (CAE ANN) was used to analyze the influence of chemical composition in conjunction with selected process parameters on the yield strength and elongation of an extruded 6082 aluminum alloy (AA6082) profile. Analysis focused on the optimization of mechanical properties as a function of casting temperature, casting speed, addition rate of alloy wire, ram speed, extrusion ratio, and number of extrusion strands on one side, and different contents of chemical elements, i.e., Si, Mn, Mg, and Fe, on the other side. The obtained results revealed very complex non-linear relationships between all of these parameters. Using the proposed approach, it was possible to identify the combinations of chemical composition and process parameters as well as their values for a simultaneous increase of yield strength and elongation of extruded profiles. These results are a contribution of the presented study in comparison with published research results of similar studies in this field. Application of the proposed approach, either in the research and/or in industrial aluminum production, suggests a further increase in the relevant mechanical properties.

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Influence of the chemical composition and process parameters on the mechanical properties of an extruded aluminium alloy for highly loaded structural parts

Cited by 12 publications

References 47 publications

Statistical Analysis of the Combined ECAP and Heat Treatment for Recycling Aluminum Chips Without Remelting

Statistical Analysis of the Combined ECAP and Heat Treatment for Recycling Aluminum Chips Without Remelting

Automatic Explanation Of Metal’s Phenomena By Application Of Artificial Neural Networks – Use Case For Hot Extruded Aluminum Alloy 6082

Contour Maps for Simultaneous Increase in Yield Strength and Elongation of Hot Extruded Aluminum Alloy 6082

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