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

Filip Nikolić,Ivan Štajduhar,Marko Čanađija

doi:10.3390/met11050756

Filip Nikolić, Ivan Štajduhar + Show 1 more

Open Access

https://doi.org/10.3390/met11050756

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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.

Highlights

It is well known that the size of dendrites and the secondary dendrite arm spacing (SDAS) strongly depend on the solidification rate of a given material [1,2]
Azimi et al [23] use a segmentationbased approach based on Fully Convolutional Neural Networks (FCNNs), which are an extension of convolutional neural networks (CNNs), accompanied by a max-voting scheme to classify microstructures
We performed two mutually independent evaluation tests: one using materials that were used during the training, and another using materials that were not used during the training

Summary

Introduction

It is well known that the size of dendrites and the secondary dendrite arm spacing (SDAS) strongly depend on the solidification rate of a given material [1,2]. It is reasonable to assume that some material properties can be determined directly from the value of SDAS. It could be useful to know the SDAS value of the material In this regard, an automatic method for determining. An artificial neural network (ANN) with only two hidden units was used In another early paper, Hancheng et al [12] developed a model to predict tensile strength based on compositions and microstructure by using adaptive neuro-fuzzy inference method. Given that in recent decades our ability to generate data has far surpassed our ability to make sense of it in virtually all scientific domains [13], the development of DL methods could be of particular benefit in materials science. This research hypothesizes that SDAS could be determined directly from the microstructure image data using DL methods

Related Work

Aluminum Alloy Samples

Dataset and Image Preprocessing

Overview of the CNN Model

Results and Discussion

Conclusions