Characterisation of Geometric Accuracy in Metal Fused Filament Fabricated Parts Using X-ray Computed Tomography
Paper in proceeding, 2026

This work presents a methodology for quantifying geometric deviations in metal parts fabricated via Fused Filament Fabrication (FFF) using Ultrafuse 316L stainless steel filament. A blade-shaped geometry is selected as a representative case and analysed before and after sintering using high-resolution X-ray computed tomography (XCT). The XCT data are aligned to nominal and scaled geometries to assess deviations introduced during each manufacturing stage. In parallel, a thermo-mechanical sintering simulation is performed to predict shrinkage and deformation. Comparison between simulated results and XCT data reveals location-dependent discrepancies, with deformation at critical regions exceeding 2 mm and simulation errors ranging from 0.5 to 2 mm. The study highlights the limitations of standard shrinkage scaling and demonstrates the value of XCT-based characterisation in validating and improving predictive models for metal FFF. The proposed approach provides a foundation for model-informed design and process compensation strategies in sintering-based additive manufacturing.

Metal Additive Manufacturing

Defect Classification

AI

Quality Control

Author

Roham Sadeghi Tabar

Department of Engineering

Chalmers, Industrial and Materials Science, Product Development

Andi Kuswoyo

University of Cambridge

Institut Teknologi Bandung

Department of Engineering

Christos Margadji

University of Cambridge

Department of Engineering

Sebastian W. Pattinson

Department of Engineering

University of Cambridge

Procedia CIRP

22128271 (ISSN)

Vol. 139 343-348

AI-based Deformation Compensation for the AM Process (AIDCAM)

VINNOVA (2024-03565), 2024-11-01 -- 2025-12-31.

Subject Categories (SSIF 2025)

Applied Mechanics

DOI

10.1016/j.procir.2025.09.045

More information

Latest update

4/21/2026