Work

Simulation-Based Prediction of Milling-Induced Shape Errors on Compliant, Additively Manufactured Components

Year: 2026

Type: article

Abstract: Additively manufactured components require machining of functional surfaces to meet geometric requirements. Due to low stiffness and non-nominal as-built geometry, they are susceptible to milling-induced shape deviations. This paper presents a hybrid process simulation for predicting shape errors in compliant metallic laser powder bed fusion components. The method combines real-geometry-based technological NC simulation, quasi-static force prediction, finite element-based structural response simulation, and surface reconstruction between roughing and finishing to enable multistage operation. The approach is validated for linear and non-linear toolpaths with varying immersion angles and compliance conditions. The results show reproduced force profiles, while magnitude deviations highlight the relevance of deformation-dependent engagement feedback in high-compliance regions. An analytical back-calculation based on the effective engagement cross-section reveals that accounting for deflection-induced engagement reduction reduces force deviations. During roughing, maximum shape errors for linear and non-linear toolpaths are overestimated by 4–5 %, and critical high-error regions are identified. The reconstructed intermediate geometry after roughing is essential for finishing, since neglecting geometry feedback underestimates finishing forces. With geometry feedback, the maximum finishing shape error is predicted as 0.090 mm, while the measured value is 0.086 mm. The simulation captures dominant quasi-static shape-error regimes and supports process-chain-oriented prediction in additive-subtractive manufacturing.

Source: Journal of Manufacturing and Materials Processing

DOI: 10.3390/jmmp10080310

Authors: Berend Denkena, Klaas Maximilian Heide, Roland Lachmayer, Jens Niedermeyer, Fabian Schlenker

Institution: Leibniz University Hannover

Language: English

Topic: Advanced machining processes and optimization

Subfield: Mechanical Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,172

Funder: Deutsche Forschungsgemeinschaft