Review: enhancing Additive Digital Manufacturing with supervised classification machine learning algorithms

Abstract

In the field of in-process monitoring for the production of safety-critical parts, the utilization of Machine Learning (ML) models has demonstrated promising potential for enhancing the manufacturing process. Specifically, Supervised Classification Algorithms, selected based on the complexity of the problem, can significantly improve the performance of ML models. While ML primarily relies on training data to comprehend input-output relationships, its ability to draw rapid conclusions is noteworthy. Deep neural networks have emerged as valuable tools, offering more accurate analytical data compared to Finite Element Method (FEM) models. They present a cost-effective alternative to empirical data by incorporating error compensation in a closed loop. Nevertheless, vision-based techniques, such as those used in this study, necessitate high-quality input images and demand substantial computational power for image classification. To address these challenges, this study proposes the use of a shallow Convolutional Neural Network (CNN)-based architecture for detecting in-process defects in parts manufactured using Additive Manufacturing (AM) technology. Complementing this, Support Vector Machine (SVM) and Extreme Learning Machine (ELM) algorithms are integrated with the CNN architecture. Additionally, the research takes into account the high costs associated with data collection and installation, data unavailability, challenges in data labeling, as well as common difficulties like overfitting and underfitting of ML models. These factors often pose constraints on the application of ML solutions within the context of AM. The study aims to address these issues and shed light on the potential of ML for AM applications. By employing a combination of CNN, SVM, and ELM algorithms, this research delves into the effectiveness of ML models in defect detection during the AM process. The insights derived from this study contribute to mitigating the aforementioned limitations and pave the way for a broader adoption of ML solutions in AM. This optimization aims to enhance performance, reduce costs, and improve the overall quality of manufactured parts.