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

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 geometric–numerical milling process simulation for predicting shape errors in compliant metallic laser powder bed fusion components. The method combines real-geometry-based technological numerical control 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.

Authors

Institutions

Publication Details

Journal
Journal of Manufacturing and Materials Processing
Published
2026-08-21
DOI
https://doi.org/10.3390/jmmp10080310
Primary Topic
Additive Manufacturing Materials and Processes
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Jens Niedermeyer, Roland Lachmayer, Berend Denkena, Klaas Maximilian Heide et al.
Journal of Manufacturing and Materials Processing
Additive Manufacturing Materials and Processes
article

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

Jens Niedermeyer, Roland Lachmayer, Berend Denkena, Klaas Maximilian Heide, Fabian Schlenker
article en

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 geometric–numerical milling process simulation for predicting shape errors in compliant metallic laser powder bed fusion components. The method combines real-geometry-based technological numerical control 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.

Journal of Manufacturing and Materials ProcessingVol. 10(8)
Leibniz University Hannover (DE)
Deutsche Forschungsgemeinschaft
Openalex Percentile: Top 19%
Additive Manufacturing Materials and Processes
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.