RAMP-OPP Robust additive manufacturing of polymer composites for optimal part production

Duration
01.03.2024–28.02.2027

Additive manufacturing (AM) is increasingly used for spare parts and end-use parts in small series. AM is a digital and physical process chain which has many advantages, such as enabling innovative designs and fast lead times. However, there are also challenges when it comes to employing AM in an industrial context. This project addresses some of the key challenges.

Bildet kan inneholde: hvitevare, kjøkkenapparat, vaskemaskin, gass, maskin.

The project focuses on material extrusion AM with fibre-reinforced thermoplastic polymer materials ("composite AM"), in particular AM with continuous fibre reinforcement, e.g. carbon fibres. These are rather new high-end AM processes for making parts with very high stiffness and strength relative to the density. The strength can exceed that of aluminium alloys.

The main objective is to develop technology for composite AM, in order to produce parts with improved quality, repeatability, documentation and sustainability, and to predict mechanical performance with high fidelity. Critical R&D challenges include developing a system for in-situ process monitoring, and establishing material models to be used in industrial finite element analysis software. Furthermore, material recycling of composite AM parts will be demonstrated.

The applicant, Fieldmade AS, was one of the first Norwegian companies to utilise AM, with their innovative portable microfactories. Through this project, Fieldmadeʼs ambition is to generate a deeper understanding of materials, reinforcement layout, processes and part quality, to enable the production of parts for higher criticality levels than currently possible. The results will, in particular, be used to improve Fieldmade's NOMAD LW AM unit which has a large sales potential, in particular for export.

The project employs an interdisciplinary approach, combining expertise in additive manufacturing, materials science, mechanics modelling, sensorics and machine learning. By this, the project will contribute to the development of knowledge-based industry in Norway.

ROBIN will contribute with expertise in machine learning, and in applying various types of sensors for monitoring purposes in combination with machine learning, as well as modelling of dynamical physical systems.

The project is funded as a "Innovation Project for the Industrial Sector 2023" by the Research Council of Norway with Fieldmade AS and Innofiber AS industrial partners, together with SINTEF and UiO (ROBIN).

Participants

Publications

  • Auren, Trym Shalchian-Tabrizi & Auren, Trym Shalchian-Tabrizi (2026). Data acquisition software for Klipper/Moonraker 3D printers. Full text in Research Archive
  • Krumenacker, Nicolas; Tomstad, Asle Joachim; Costa, Elisabete Reia da; Johansen, Marius; Reyne, Baptiste Jean Marius & Vargas, Pablo [Show all 7 contributors for this article] (2026). Mechanical performance of polymer composites produced by additive manufacturing with continuous and short carbon fibres. Full text in Research Archive
  • Auren, Trym Shalchian-Tabrizi (2025). Student paper draft: Camera-Based Anomaly Detection in Fused Filament Fabrication Using YOLOv8. Full text in Research Archive
  • Tørresen, Jim (2025). Presentation as a panelist at a panel on global perspectives on AI development and policy. Full text in Research Archive
  • Tørresen, Jim (2025). Industrial AI Center Partner Presentation. Full text in Research Archive

View all works in NVA

Published Apr. 25, 2024 8:51 AM - Last modified May 26, 2025 2:23 PM