Fracture surface analysis is crucial in investigating manufacturing failures and material characterization. Traditional manual inspection methods are slow and subjective, prompting the need for efficient automated tools using advanced computer vision techniques. Recent machine learning models for classifying surface fractures show potentials but struggle due to the lack of large, labeled datasets. This study explores the potential application of autoencoders, a self-supervised neural network, to identify unintended fracture surfaces from anomalous manufacturing of tungsten-heavy alloys. The proposed autoencoder-based model achieves 97% accuracy in distinguishing undesirable fracture patterns by analyzing the reconstruction loss of the images, surpassing existing methods. This high accuracy highlights the autoencoder's ability to automatically extract and reduce dimensional features from fracture surfaces effectively. The experimental result obtained on tungsten-heavy alloys demonstrate the model's potential towards developing autoencoder-based automated tools for fractographic analyses across various materials and operational scenarios.