Abstract
The rapid development of production methods and the introduction of additive manufacturing (AM) technologies have increased the design freedom and availability of unlabeled data. Typically, manufacturing process selection requires a large amount of labeled data which is expensive and difficult to reach. This is more critical with the continuous improvement of AM systems, which can be increasingly used to substitute traditional manufacturing technologies. Implementing unsupervised learning into manufacturing process selection and classification is beneficial since unsupervised learning uses unlabeled data. Hence, this paper aims to differentiate parts to be fabricated favorably by means of additive or traditional manufacturing technologies using image processing and unsupervised learning. The input image dataset is constructed from freely accessible web databases in which forty CAD (computer-aided design) models are available. The corresponding images of CAD models are extracted using the SOLIDWORKS 2022 software, where 42.50% are AM-ed, and the remaining are chosen from traditionally manufactured parts. The hierarchical clustering algorithm reported 87.50% accuracy, showing promising potential for manufacturing process classification and image processing applications.