Quality Testing in Aluminum Die-Casting – A Novel Approach using Acoustic Data in Neural Networks

Abstract

In quality control of aluminum die casting various processes are used. For example, the density of the parts can be measured, X-ray images or images from the computed tomography are analyzed. All common processes lead to practically usable results. However, the problem arises that none of the processes is suitable for inline quality control due to their time duration and to their costs of hardware. Therefore, a concept for a fast and low-cost quality control process using sound samples is presented here. Sound samples of 240 aluminum castings are recorded and checked for their quality using X-ray images. All parts are divided into the categories "good" without defects, "medium" with air inclusions ("blowholes") and "poor" with cold flow marks. For the processing of the generated sound samples, a Convolutional Neuronal Network was developed. The training of the neural network was performed with both complete and segmented sound samples ("windowing"). The generated models have been evaluated with a test data set consisting of 120 sound samples. The results are very promising. Both models show an accuracy of 95% and 87% percent, respectively. The results show that a new process of acoustic quality control can be realized using a neural network. The model classifies most of the aluminum castings into the correct categories. Keywords: acoustic quality control, aluminum die casting, convolutional neural networks, sound data