Quality requirements for additive manufacturing processes are rising as these technologies are increasingly used for high-quality and critical components in various industries, such as aerospace, automotive, medical technology, and biotechnology. Due to its design flexibility, additive manufacturing is often used for small and custom series, making quality control a challenge. A promising approach to improve cost-effectiveness is process monitoring using machine learning. This study presents a sensor concept for fused deposition modeling and a machine learning pipeline to predict process and part quality. Tensile and impact strength were selected as key quality characteristics. The results show high accuracy in predicting tensile strength, while lower accuracy for impact strength is attributed to data variability caused by natural scattering in samples with identical process conditions. This highlights machine learning’s potential to enhance efficiency and reduce costs, offering an alternative to expensive testing methods.
Fused deposition modeling, machine learning, process monitoring
The economy is facing challenges that require sustainable economic activity. Automation offers great potential in this respect, as it can promote energy efficiency, resource conservation and social improvements. Nevertheless, existing sustainability assessment methods often inadequately represent specific requirements for automation solutions. IPH is therefore developing a method to support SMEs in effectively implementing sustainable automation solutions.
sustainability, sustainability assessment, automation solutions, social, economic, ecological
Additive manufacturing enables the economical production of complex components with a high degree of customization. Therefore, the medical industry is using the advantages of additive manufacturing to produce individualized medical devices. Medical devices are subject to special quality control requirements that additive manufacturing processes do not meet yet. This article deals with the introduction of an in situ process monitoring concept using the example of fused deposition modeling. The process monitoring is carried out by a quality model, which accesses the data of a self-developed sensor concept integrated in the printer. This data is analyzed using a machine learning pipeline to predict process and product quality. Thereby, the machine learning pipeline consist of several sequential steps, ranging from data extraction and preprocessing to model training and deployment. The procedure presented for ensuring print quality forms a basis for the production of safety-relevant components in batch size one and extends conventional quality assurance methods in additive manufacturing.
additive manufacturing, quality monitoring, fused deposition modeling, artificial intelligence
Additive manufacturing has established itself in medical technology, where complex and patient-specific products are manufactured. Since additive manufacturing processes are sensitive to changes in process parameters and environmental conditions, quality assurance is a key factor for production. This paper presents the approach for in-situ process monitoring in additive material extrusion.
Additive Manufacturing, 3D printing, Fused Deposition Modeling, quality control, machine learning
