Casting Microstructure Inspection Using Computer Vision: Dendrite Spacing in Aluminum Alloys

Abstract

This paper investigates the determination of secondary dendrite arm spacing (SDAS) using convolutional neural networks (CNNs). The aim was to build a Deep Learning (DL) model for SDAS prediction that has industrially acceptable prediction accuracy. The model was trained on images of polished samples of high-pressure die-cast alloy EN AC 46000 AlSi9Cu3(Fe), the gravity die cast alloy EN AC 51400 AlMg5(Si) and the alloy cast as ingots EN AC 42000 AlSi7Mg. Color images were converted to grayscale to reduce the number of training parameters. It is shown that a relatively simple CNN structure can predict various SDAS values with very high accuracy, with a R2 value of 91.5%. Additionally, the performance of the model is tested with materials not used during training; gravity die-cast EN AC 42200 AlSi7Mg0.6 alloy and EN AC 43400 AlSi10Mg(Fe) and EN AC 47100 Si12Cu1(Fe) high-pressure die-cast alloys. In this task, CNN performed slightly worse, but still within industrially acceptable standards. Consequently, CNN models can be used to determine SDAS values with industrially acceptable predictive accuracy.

Determination of linear expansion of AlSi10Mg aluminium alloy depending on external conditions during solidification

“…The SDAS parameter can be evaluated for example manually (this method is used by the authors in articles [ 41 , 42 ] or automatically (the authors in the experiment in the article [ 43 ]. Using the evaluation of the SDAS parameter the authors of the article [ 44 ] also attempted and successfully verified the influence on the corrosion of the aluminium alloy.…”

Advances in Processing and Mechanical Behavior in Lightweight Metals and Alloys

“…The authors [6] studied the nanoprecipitation on the AA7050 aerospace high strength aluminium alloy, after an innovative thermal treatment, and it was proven to be able to increase the toughness in KIC laboratory testing.…”

Estimation of Average Grain Size from Microstructure Image Using a Convolutional Neural Network

“…Quantitative microstructure analysis has also been used to interpret alloy components [ 10 ], material properties [ 11 , 12 ], and microstructural features [ 13 , 14 ] based on microstructure images using machine learning and computer vision. From the point of view of machine learning for image recognition, the prediction of alloy components or material properties and the prediction of average grain size look similar to each other, but there is one major difference.…”