Work

Model-based feedforward control strategy of digital micromirror device masks for high-precision Microscale Selective Laser Sintering

Year: 2026

Type: article

Abstract: Microscale Selective Laser Sintering ( μ -SLS) is a promising additive manufacturing process for high-resolution metal fabrication, but its geometric fidelity can be degraded by lateral heat diffusion and geometry-dependent thermal nonuniformity. Conventional static mitigation strategies cannot adapt to these spatially varying thermal effects at the microscale. To address this limitation, this paper presents a deterministic, model-based feedforward thermal optimization framework for single-layer μ -SLS. The proposed framework employs a control-oriented finite-difference model (FDM) directly integrated with a linear-programming-based optimization scheme to compute geometry-specific laser irradiance patterns for digital micromirror device (DMD) projection. Simulation results show that the optimized irradiation patterns mitigate heat-affected zone (HAZ) broadening and improve thermal localization relative to constant-power exposure. Experimental validation is performed on representative single-layer (2.5D) copper nanoparticle geometries. Because pixel-level in situ thermal sensing is unavailable on the present μ -SLS platform, experimental performance is evaluated through scanning electron microscopy (SEM)-based geometric error quantification. Repeated-trial experiments show mean geometric error reductions of 35.2%, 25.6%, and 47.2% for the gear, heart, and wave geometries, respectively, relative to unmodulated exposure. In contrast, the I -beam exhibits a 6.3% increase in geometric error, highlighting a limitation of the present deterministic reduced-order model for long, slender features with geometry-dependent thermal behavior. Overall, this work provides a single-layer proof-of-concept demonstration of geometry-adaptive feedforward thermal control in μ -SLS while identifying the model-fidelity limitations that motivate future uncertainty-aware and higher-fidelity control strategies.

Source: Precision Engineering

DOI: 10.1016/j.precisioneng.2026.10.005

Authors: Aaron Liao, Hsiu-Tsu Shui, Pratik Koirala, Farzana Tasnim, Michael Cullinan, Chinedum Okwudire

Institutions: The University of Texas at Austin, University of Michigan

Language: English

Topic: Additive Manufacturing Materials and Processes

Subfield: Mechanical Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,340

Funder: National Science Foundation