Maschinelles Lernen als modernes Werkzeug für Additive Fertigung

Abstract

In modern manufacturing systems, quality monitoring is crucial for efficient and cost-effective production. Conventional systems rely on thresholds and process windows, but machine learning (ML) techniques promise greater accuracy and efficiency. However, pre-processing the data is still timeconsuming. This paper presents an approach to visually verify two Variational Autoencoders (VAEs) using contextual information such as print job numbers and timestamps, with the aim of predicting time series data from image data to optimize additive manufacturing processes in time. The approach focuses on the Digital Light Processing (DLP) printing process and emphasizes the importance of accurate data pre-processing and contextual visualization. The approach utilizes VAE-generated latent spaces to improve prediction accuracy in additive manufacturing and implement quality monitoring without thresholds and process windows.