Simulation Analysis of Porthole Die Extrusion Process and Die Structure Modifications for an Aluminum Profile with High Length–Width Ratio and Small Cavity

Liu, Zhiwen; Li, Luoxing; Li, Shikang; Yang, Jie; Wang, Guan

doi:10.3390/ma11091517

Cited by 30 publications

(14 citation statements)

References 30 publications

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“…Due to the growing demand for massive infrastructure investment in the world and rapid development of industrialization, the aluminum profiles with high strength, light weight, corrosion resistance, long service life, rich color and other advantages are mainly used in automobile manufacturing, rail transit, equipment and machinery manufacturing, consumer durables, aerospace industry and other industries [1]. Therefore, the surface quality of aluminum profile is very important significance and can greatly affect the product performance.…”

Section: Introductionmentioning

confidence: 99%

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

Duan

Zhang

2020

IEEE Access

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In this paper, 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 paper, 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 paper also proposes a series of innovative methods for the future research of other defects. INDEX TERMS Aluminum profile surface defects, two-stream network, gradient image, convolutional neural network, SVM.

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Section: Introductionmentioning

confidence: 99%

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

Duan

Zhang

2020

IEEE Access

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“…Aluminum alloys have drawn more and more attention in aerospace engineering, automotive, and electronics industry, due to their low density, high specific strength, good corrosion resistance, and good recycling ability [1,2]. Aluminum profile is an application form of aluminum alloys, and the demand for it is extremely large because of the massive use of space-frame constructions in high-speed rail and auto body [3]. Therefore, surface quality of aluminum profiles have assumed significant importance.…”

Section: Introductionmentioning

confidence: 99%

Research on Recognition Technology of Aluminum Profile Surface Defects Based on Deep Learning

Wei

2019

Preprint

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Aluminum profile surface defects can greatly affect the performance, safety and reliability of products. Traditional human-based visual inspection is low accuracy and time consuming, and machine vision-based methods depend on hand-crafted features which need to be carefully designed and lack robustness. To recognize the multiple types of defects with various size on aluminum profiles, a multiscale defect detection network based on deep learning is proposed. Then, the network is trained and evaluated using aluminum profile surface defects images. Results show 84.6%, 48.5%, 96.9%, 97.9%, 96.9%, 42.5%, 47.2%, 100%, 100%, 43.3% average precision(AP) for the ten defect categories, respectively, with a mean AP of 75.8%, which illustrate the effectiveness of the network in aluminum profile surface defects detection. In addition, saliency maps also show the feasibility of the proposed network.

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“…They made a list of definition for defects and their causes, preventive measures, and die correction operations. Liu et al [9] was carried out simulations by HyperXtrude on the extrusion process by porthole dies. They proposed die structure modifications for aluminum profiles with small cavity.…”

Section: Introductionmentioning

confidence: 99%

An extrusion simulation of an aluminum profile by porthole die

Ayer¹,

Bingöl²,

Karakaya³

2019

Vib. proced.

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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.

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Simulation Analysis of Porthole Die Extrusion Process and Die Structure Modifications for an Aluminum Profile with High Length–Width Ratio and Small Cavity

Cited by 30 publications

References 30 publications

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

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

Research on Recognition Technology of Aluminum Profile Surface Defects Based on Deep Learning

An extrusion simulation of an aluminum profile by porthole die

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