Aluminum Profiles Research Articles

Analysis and improvement of material flow during extrusion process using spreading pocket die for large-size, flat-wide, and multi-ribs profile

Spreading extrusion is an advanced technology to produce the solid aluminum profiles with large-size, flat-wide, and thin-walled structures. Currently, little research has been reported on the hot extrusion process through spreading pocket die for manufacturing these kinds of aluminum profiles. It is a challenging task for die engineers to control the billet through die cavity with a uniform velocity. Reasonable structure design for spreading pocket die can eliminate extrusion defects and improve the performance of extrudate. In this paper, virtual tryout of spreading extrusion process for a large-size, flat-wide, and multi-ribs aluminum profile was performed through arbitrary Lagrangian-Eulerian simulation. Firstly, spreading pocket die was designed based on the theory of metal plastic flowing. The uniformity of flow velocity distribution on the cross-section of die exit was evaluated quantitatively through standard deviation calculation. Then, a series of structure modifications for the spreading pocket die was proposed to improve material flow during die cavity based on the simulated results. Thirdly, a synthetical comparison of extrusion formability for the modified and initial spreading pocket dies was carried out, including the metal flow behavior, exit temperature, residual stress of extrudate, and peak extrusion force. Finally, the modified extrusion dies were manufactured, and corresponding extrusion experiment was performed to verify the effectiveness and reliability of numerical simulations. The key design points of spreading pocket die for large-size, flat-wide, and multi-ribs aluminum profile were concluded.

Joining aluminium profiles to composite sheets by additive manufacturing and forming

Abstract This paper explores the application of the ‘mortise-and-tenon’ concept for joining hollow section aluminium profiles to composite strips or sheets. Wire arc additive manufacturing is combined with joining by forming to fabricate the tenons and to obtain the mechanical interlocking with the mortises available in the strips (or sheets). The workability limits are established by means of an analytical model that combines plastic deformation, instability and fracture. Experimental and finite element modelling are utilized to develop the overall joining process and to validate the round ‘mortise-and-tenon’ design resulting from the analytical model. Pull-out and shear destructive tests are carried out to evaluate the overall strength of the joints and results allow concluding that the new joints can easily and effectively replace existing solutions based on welding, fastening or adhesive bonding. The proposed joining process also circumvents the need to design extra fixing and interlocking features in low cost hollow section aluminium profiles for easy assembling.

Two-Stream Convolutional Neural Network Based on Gradient Image for Aluminum Profile Surface Defects Classification and Recognition

In this article, a novel two-stream convolutional neural network based on gradient image is performed to effectively classify and identify aluminum profiles defects for the first time. Recent feature fusion methods based on two-stream network prove promising performance for defects classification and recognition. In this article, we use data enhancement methods to obtain a large number of samples to prevent the over fitting phenomenon in deep learning. The image gradient is calculated with the Sobel operator, and normalized to transform the data between zero and one under the same dimension. We design a two-stream convolutional neural network model adopting Wavelet transform fusion strategy to realize feature fusion on the ReLU6 layer, which uses the original RGB image of aluminum profile and the gradient image corresponding to the original RGB image as inputs to extract features through two sub-networks and fuses features on a concatenate layer to be input into SVM classifier for classification and recognition. Using Bayesian Optimization function and computing the cross-validation classification error to optimize the hyperparameters to choose the best performance configuration is performed. A series of experimental data, which include accuracy and estimated generalized classification errors of single-stream and two-stream networks with different feature fusion strategies on different fusion layers, are conducted and show that the current model has good convergence, accuracy, stability and generalization. On this basis, this article also proposes a series of innovative methods for the future research of other defects.

Quality prediction of longitudinal seam welds in aluminium profile extrusion based on simulation

The paper presents the analysis of different quality criteria of longitudinal seam welds that appear in aluminium profiles during extrusion using porthole dies. The physical background and limitations of the most widely used criteria are discussed. A new improved dimensionless welding quality criterion that takes into account the unbalances of metal flow is proposed by the authors. It is based on analysis of simulation results obtained by QForm Extrusion FEM software and experiments. The authors also introduced a comparative classification of longitudinal seam welds based on the practical application of profiles. It allows the user to characterise the quality of a seam weld depending on its specific application and make a decision regarding its suitability for certain technical requirements. The proposed criterion has been approved by industrial experiments for different types of profiles. Particularly its numerical critical value has been found for AA 6082 alloy. The quantitative correspondence between welding types and values of developed criterion is also defined.

Impact of supply chain linkages on the performance of enterprises specializing in aluminum profiles: The case of Vietnam

Impact of supply chain linkages on the performance of enterprises specializing in aluminum profiles: The case of Vietnam

Estimating thermal transmittance of the aluminium window profiles in practice

Calculation of the heat flow through the air cavities in the EN ISO 10077-2:2017 standard for the determination of the thermal transmittance of window profiles uses models based on the equivalent thermal conductivity method. The method takes into account the radiative heat exchange in a simplified or accurate manner. In the first case, the heat exchange depends on the average temperature in cavity, in the second case - it is determined accurately by the ray tracing method. It is also of importance to differentiate emissivity of surfaces due to aging or painting what influences calculation time. In this work, the impact of the calculation method and the impact of simplifications in modelling of the untreated surfaces on the value of the thermal transmittance of aluminium profiles was analysed on the example of a real series of products. Comparing the simplified and accurate method of determining the radiation exchange in cavities, the differences in the thermal transmittances of window profiles were up to 22%. The differences between the most simplified and the most accurate modelling of the surfaces emissivity reached 23%.

Effects of Solid Die Types in Complex and Large-Scale Aluminum Profile Extrusion

Increasing customer requirements for quality and productivity in extruding aluminum products has led to the development of different types of extrusion dies. In this study, three different types of dies, including traditional flat die, pocket die and spread die were designed to extrude complex and large-scale solid profiles. The design parameters for these dies were used from actual extrusion experience. The results obtained from steady-state simulation such as velocity, temperature, extrusion force, and die deformation were used to assess the advantages and disadvantages of the dies. Transient simulations were performed to analyze the evolution of transverse weld in the pocket and spread dies. The effects of ram speeds on the related extrusion parameters were also investigated. The research results provide useful guides for designers and engineers in selecting these types of extrusion dies.

Analysis and Control of Twist Defects of Aluminum Profiles with Large Z-Section in Roll Bending Process

This paper focuses on the twist defects and the control strategy in the process of four-roll bending for aluminum alloy Z-section profiles with large cross-section. A 3D finite element model (3D-FEM) of roll bending process has been developed, on the premise of the curvature radius of the profile, the particularly pronounced twist defects characteristic of 7075-O aluminum alloy Z-section profiles were studied by FE method. The simulation results showed that the effective control of the twist defects of the profile could be realized by adjusting the side roller so that the exit guide roll was higher than the entrance one (the side rolls presented an asymmetric loading mode with respect to the main rolls) and increasing the radius of upper roll. Corresponding experimental tests were carried out to verify the accuracy of the numerical analysis. The experimental results indicated that control strategies based on finite element analysis (FEA) had a significant inhibitory function on twist defects in the actual roll bending process.

Desempenho acústico de esquadria de alumínio com melhorias na caixa de persiana

As esquadrias exercem funções que extrapolam sua funcionalidade e composição estética, pois são fundamentais para o atendimento de diversos requisitos, entre eles o desempenho acústico. Se mal projetadas e instaladas, as esquadrias, representam o principal ponto de falha na isolação sonora de uma fachada, especialmente quando compostas por persiana. Este artigo tem como objetivo apresentar a influência no desempenho acústico, descrito na NBR 15575 (ABNT, 2013) de uma esquadria, após a inserção de dois diferentes materiais de absorção acústica em sua caixa de persiana: manta de lã de PET e espuma de alta densidade, nas situações de persiana estendida e recolhida. A amostra ensaiada consiste em uma janela com perfis de alumínio, persianas de alumínio e de vidro laminado 4+4 mm. Os ensaios foram realizados em laboratório, seguindo a ISO 10140-2 (2010). Os resultados obtidos mostraram que não houve melhoria no desempenho acústico da esquadria por meio da inserção dos materiais na caixa de persiana e, em contrapartida, pequenas falhas na instalação geraram perda significativa no desempenho.

The Effect of Elastic Strain and Small Plastic Deformation on Tensile Strength of a Lean Al–Mg–Si Alloy

Al–Mg–Si alloys are usually formed into their final shape by rolling or extrusion. After extrusion, the aluminium profiles are usually straightened, causing the material to be subjected to a small plastic deformation. This study demonstrates the positive effect on strength that can be obtained from such small deformation levels or from only elastically straining the material. Elastic straining of a lean Al–Mg–Si alloy, when performed immediately after solution heat treatment, enhances the material yield strength after artificial ageing to T6. Transmission electron microscopy shows that this effect can be attributed to a higher number density and finer dispersion of the age-hardening precipitate needles. Furthermore, introducing a small plastic deformation of 1% after solution heat treatment results in a comparable strength increase to elastically straining the material. In this case, however, the strength increase is due to the increased dislocation density, which compensates for a lower density of precipitate needles. Finally, by combining plastic deformation with a succeeding elastic strain, we demonstrate how elastic strain can cause an on-set of dislocation cell formation in this material.

Unified detection method of aluminium profile surface defects: Common and rare defect categories

It is difficult to achieve automatic visual detection of aluminium profile surface defects (APSD) owing to their various categories, irregular shapes, random distribution, and unbalanced samples. Utilising the attention mechanism, the unified detection method attempts to address these challenges for both common and rare defects. We formulate our method as a variant of few-shot learning to recognise the common and rare defect categories. First, a category representation network is applied to extract common category feature maps (CCMs). Second, an attention module is proposed to generate the proposal feature maps (PMs) of each rare category. Third, rare category feature maps (RCMs) are transformed from the CCMs under the guidance of the PMs. Finally, the scores of each category are obtained through the spatial pooling of both CCMs and RCMs. Experimental results on our constructed dataset show that our method is effective and outperforms the state-of-the-art methods.

Numerical Simulation of Heat Treatment Process of Aluminium Profiles

Abstract The paper deals with the simulation of cooling processes of the aluminium profiles inside the water quench. Cooling of profile surfaces is performed by water spray, which is created by mixing a water and an air in a nozzles. Formulas were found in literature, modified and applied to a given problem that significantly simplified a solution. The task of numerical simulations was to determine temperature and velocity profile on aluminium profile surfaces for establishing of the heat transfer coefficient which was used as the convective boundary condition necessary to solve the heat transfer in the aluminium profile by finite element method. The difference between FEM and CFD results is up to 10%.

The Design and Manufacturing Process of an Electric Sport Car (EVT S1) Chassis

Nowadays, electric vehicles are becoming more popular day by day. Prototypes of such vehicles are being built extensively; however, cost of them is usually very high. This study presents novel approaches for design and manufacturing of an electric vehicle chassis and its fixtures with very low cost. The chassis is tub type and its original structure is formed regarding manufacturing simplicity, lightness, robustness, and low cost. This novel chassis is formed by planar and simple aluminum profiles that are readily available in stocks of producers. The design philosophy of the chassis is discussed in detail. Finite element analyses of the chassis are adopted for various crash impact loading scenarios and torsional rigidity. Also, original fixture designs for low-cost manufacturing are introduced in this study. Manufacturing cost of the chassis, fixtures, and the vehicle is presented. After road tests, the results are presented and discussed. It is evidenced that a lightweight and low-cost chassis built from simple planar parts may perform very well. Electric vehicles possessing such simple and robust chassis may be popular due to the manufacturing advantages against today’s extremely complex designs.

Springback Study on Profile Flexible 3D Stretch-Bending Process Using the Neural Network

The springback is one of the main defects in the flexible 3D stretch-bending process. In this paper, according to the orthogonal design of experiments, the numerical simulation analysis of the springback for the 3D stretch-bending aluminum profile is carried out by the ABAQUS finite element software. And to investigate the effect of material properties on the springback, the range analysis of the orthogonal experiment is performed. The results show that these material properties of the aluminum profile (elastic modulus E, yield strength σy, and tangent modulus E1) might have the biggest influence on the springback of the aluminum profile, and the optimized forming parameters are founded as follows: the horizontal bending degree is 14°, the vertical bending degree is 14°, the number of multipoint stretch-bending dies is 10, the friction coefficient is 0.15, and aluminum alloy grade is 6063. Moreover, the model of the BP neural network for the prediction of the springback is established and trained based on the orthogonal experiment, and the results with the BP neural network model are in good agreement with experimental results. So it is obvious that the BP neural network could predict effectively the springback of 3D multipoint stretch-bending parts.

An extrusion simulation of an aluminum profile by porthole die

The aluminum alloys are ideal material because of their corrosion resistance, recycling ability, high specific strength and especially low density for lightweight structures of transportation, aerospace, automotive industries. Hot extrusion process is the most used metal forming method for obtaining a variety of aluminum alloy profiles. The demand for large cross section, multi cavity and thin wall profiles has been increasing with the development of the industry and extrusion method is key solution for producing complex profiles with high productivity. These profiles are generally extruded by porthole dies. The extrusion process by porthole die is complicated and die design has great importance for the quality of the extruded product. Design of the porthole die should give optimum material flow and homogenous temperature distribution both for obtaining desired profile and eliminating die scrap. The measuring the temperature and material flow is not possible for closed die formation and it is so important to estimate both material flow, temperature change in the die. For this aim, an extrusion simulation of a porthole die for standard aluminum profile was investigated in this study with the support of HyperXtrude Inspire Extrude Metal 2019 software, which is specialized for FEM calculations of extrusion process. Each step of extrusion process was simulated. Aluminum AA6063 material was used for simulations, the process temperature was 4500 °C and punch velocity was selected as 5 mm/sec. Finally, the FEM results were obtained and the temperature distribution, pressure distribution, billet interface and relative die exit speed results were analyzed.

Optimización del tiempo de barrenado en perfiles de aluminio

The manufacturing branch of aluminum windows national line is an area that nowadays the conventional processes are deficient and with errors at the time of execution causing the manufacturer to lose time, so the need arises to improve the process of drilling in the profiles of aluminum national line for the assembly of windows. The objective of this work is to build a die to optimize times and maximize productivity in the drilling of aluminum profiles national line by means of a study of times, the design was made using the methodology of Bruno Munari. With the construction of the die for aluminum profiles national line was achieved the reduction of the time of drilling on average from 1.7 minutes to 0.07 minutes that represents the optimization of time in 95.88% while to assemble a window the time of 3.5 minutes was reduced to 0.14 minutes what represents the maximization of the production in 96%.

Analytical process design for interference-fit joining of rectangular profiles

Abstract The classical joining of round tubes by die-less hydroforming is extended to joining of rectangular profiles. A new analytical approach for the calculation of required process parameters is developed. It allows the calculation of the interference pressure p and the required fluid pressure range given by a lower and upper limit ( p i , m i n and p i , m a x ). These limits are the result of the deformation state of the inner and outer joining partner. The minimum pressure, where plastic deformation of the inner joining partner occurs first, characterizes the value at which the joint formation starts. The maximum pressure is the value, at which plastic deformation of the outer joining partner occurs. The analytical model is validated by experimental studies where two rectangular aluminum profiles (EN AW-6060) with different heat treatment conditions were joined. Above p i , m a x , the increase of the achievable joint strength with respect to the internal pressure is not significant anymore. The maximum deviation between the analytically and experimentally determined maximum fluid pressure p i , m a x is about 12% in case of a wall thickness of 3 mm and 11% in case of a wall thickness of 5 mm.

Influence of dynamic recrystallization on microstructure and mechanical properties of welding zone in Al–Mg–Si aluminum profile during porthole die extrusion

Abstract The effect of dynamic recrystallization (DRX) on the microstructure and mechanical properties of 6063 aluminum alloy profile during porthole die extrusion was studied through experiment and simulation. The grain morphology was observed by means of electron backscatter diffraction (EBSD) technology. The results show that, at low ram speeds, increasing the ram speed caused an increase in DRX fraction due to the increase of temperature and strain rate. In contrast, at high ram speeds, further increasing ram speed had much less effect on the temperature, and the DRX faction decreased due to high stain rates. The microhardness and fraction of low angle boundaries in the welding zones were lower than those in the matrix zones. The grain size in the welding zone was smaller than that in the matrix zone due to lower DRX fraction. The decrease of grain size and increase of extrudate temperature were beneficial to the improvement of microhardness.

Small Al cluster ion implantation into Si and 4H-SiC.

Continuously downscaling integrated circuit devices requires fabrication of shallower p-n junctions. The ion implantation approach at low energy is subject to low beam current due to the Coulomb repulsion. To overcome this problem cluster ions can be used for implantation. In comparison with single ions, cluster ions possess lower energy per atom and reduced Coulomb repulsion resulting in high equivalent current. In this study to carry out low-energy implantation into single crystalline silicon and 4H-SiC samples we employ Aln - (n = 1-5) clusters with energy in the range of 5-20 keV. The Al clusters are obtained by Cs sputtering of Al rod. Time-of-flight secondary ion mass spectrometry (TOF-SIMS; IONTOF TOF.SIMS-5) is used to study aluminum and oxygen sputter depth profiles for different cluster sizes and implantation energies before and after annealing treatment. A distinguishable effect of the energy per atom in the cluster on reduction of the projected range Rp is revealed. The lowest Rp of 3 ± 1 nm has been achieved in SiC samples at the energy per atom of 1.66 keV. After annealing of Si samples, a considerable change in the Al profiles due to redistribution of Al atoms during motion of the front of recrystallization is observed. The influence of the number of atoms in the cluster at the same energy per atom within the experimental uncertainty is not observed. The transient effects of the sputtering by the primary ion beam distort the shape of the Al profiles in Si samples. In the case of SiC, due to its relatively lower surface chemical activity, more informative TOF-SIMS depth profiling of the shallow cluster implantation is feasible.

Surface defect classification and detection on extruded aluminum profiles using convolutional neural networks

For decades, aluminum extrusion has been successfully applied in the manufacturing of profiles for the applications ranging from locomotives to skyscrapers. In recent years however, increasing profile complexity and the need for rapid production have lead to greater challenges for manufactures seeking rapid and robust production procedures. As a consequence, the occurrence of defects in extruded profile surfaces continues to create difficulties often requiring disposal of entire components. Hence, quality inspection of the profiles must be performed prior to packing in order to identify and appropriately manage defect-containing extrusions. Up until now, quality control in extrusion factories is primarily performed by the human eye due to its high performance in discriminating defect varieties. But human performance is cost intensive and furthermore prone to failure, especially when applied in high-throughput environments. On that account this paper proposes an approach in surface defect classification and detection, whereby a simple camera records the extruded profiles during production and a neural network architecture distinguishes between immaculate surfaces and surfaces containing a variety of common defects (surface defect classification). Furthermore, a neural network is employed to point out the defects in the video frames (surface defect detection). In this work, we show that methods from artificial intelligence are highly compatible with industrial applications such as quality control even under common industry constraints such as very limited data set sizes for training a neural network. Data augmentation as well as transfer learning are the key ingredients for training networks that meet the high requirements of modern production facilities in detecting surface defects, particularly when access to training sets is limited. Accuracies of 0.98 in the classification and mean average precisions of 0.47 in the detection setting are achieved whilst training on a data set containing as little as 813 images. Real-time classification and detection codes are implemented, and the networks perform reliably despite changes in lighting conditions and camera orientation.