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Physics-informed multi-head LSTM-based predictive-prescriptive framework for process parameter optimization in fused deposition modeling
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DOI：10.1007/s40964-026-01700-0.png)
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Abstract
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Additive manufacturing (AM) by fused deposition modeling (FDM) is prone to dimensional errors, warpage, and residual stress due to complex thermo-mechanical interactions. We present a predictive–prescriptive framework that combines thermomechanical simulations of filament-deposition physics with a multi-head Long Short-Term Memory (LSTM) network to predict layer-wise temperatures and quality metrics (deformation, strain, stress), and to refine process parameters that minimize defects. A 64-case factorial dataset of layer height, nozzle speed, and nozzle temperature was generated via coupled transient thermal–structural analyses for experimentation. The proposed framework produces (i) multi-probe temperature forecasts; (ii) global thermo-mechanical responses; and (iii) parameter prescriptions. It achieves probe-wise temperature RMSEs of 0.13–0.17 °C, deformation errors ≤ 0.02 mm, and stress errors within 0.3 MPa on the test cases. The outputs were validated through experiments involving re-printing guided by the framework, infrared thermography, and stress–strain testing. Using the refined process parameters, we observed a 21–26% reduction in residual stress during compression tests and a 11–39% decrease in total deformation. Meanwhile, infrared thermography showed temperature prediction errors of less than 3.5%. By connecting predictions with actionable parameter recommendations, this approach transforms quality assurance in fused deposition modeling from reactive inspection to proactive, physics-informed, and data-driven process parameter optimization.
Keywords:
Fused Deposition Modeling
Physics-Informed LSTM
Transient Thermal-Structural Analyses
Process Parameter Optimization
In-situ Quality Monitoring
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