Deep Learning Utilization for In-Line Monitoring of an Additive Co-Extrusion Process Based on Evaluation of Laser Profiler Data

Additive manufacturing is gaining importance in a number of application areas, and there is an increased demand for mechanically resilient components. A way to improve the mechanical properties of parts made of thermoplastics is by using reinforcing material. The study demonstrates the development of a monitoring procedure for a fused filament fabrication-based co-extrusion process for manufacturing wire-reinforced thermoplastic components. Test components in two variants are produced, and data acquisition is carried out with a laser line scanner. The collected data are employed to train deep neural networks to classify the printed layers, aiming for the deep neural networks to be able to classify four different classes and identify layers with insufficient quality. A dedicated convolutional neural network is designed taking into account various factors such as layer architecture, data pre-processing and optimization methods. Several network architectures, including transfer learning (based on VGG16 and ResNet50), with and without fine-tuning, are compared in terms of their performance based on the F1 score. Both the transfer learning model with ResNet50 and the fine-tuning model achieve an F1 score of 84% and 83%, respectively, for the decisive class ‘wire bad’ classifying inadequate reinforcement.