Calibrated Uncertainty Quantification of LPBF/DMLS SS316L Surface Roughness Using Leave-One-Setting-Out Benchmarking and Reliability Decisions

Venkata Phani Babu Vemuri; Sivaramakrishna Valiveti; Subbarao C.V; Sushma  Pappula; Ravikiran  Chintalapudi; Sunanda A

doi:10.29194/NJES.29030552

Authors

Venkata Phani Babu Vemuri Department of Civil Engineering, Dadi Institute of Engineering & Technology, Anakapalle, Andhra Pradesh- 531002, India
Valiveti Sivaramakrishna Department of Mechanical Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad - 500090, Telangana, India
C.V. Subbarao Department of Mechanical Engineering, PACE Institute of Technology & Sciences, Ongole-523272, Andhra Pradesh, India.
Sushma Pappula Department of Mechanical Engineering, PACE Institute of Technology & Sciences, Ongole-523272, Andhra Pradesh, India.
Ravikiran Chintalapudi Department of Mechanical Engineering, MLR Institute of Technology, Hyderabad- 500043, India.
A. Sunanda Department of Mechanical Engineering, Sreenidhi Institute of Science and Technology, Yamnampet, Hyderabad, India

DOI:

https://doi.org/10.29194/NJES.29030552

Keywords:

DMLS, SS316L, Surface Roughness, LOSO, NLPD, ENCE

Abstract

This study develops a decision-oriented uncertainty quantification methodology for analyzing as-built surface roughness in laser powder bed fusion/direct metal laser sintering (LPBF/DMLS) SS316L. A Taguchi L9 design was used to vary laser power (300-360 W), scan speed (800-1000 mm·s-1), and layer thickness (20-80 μm), producing nine process settings with three independently fabricated specimens per setting, resulting in 27 total specimens. Surface roughness was measured by contact stylus profilometry using arithmetic mean roughness (Ra), root mean square roughness (Rq), and maximum profile height (Rz). The mean roughness varied within narrow ranges, Ra as 5.748-5.952 μm, Rq as 6.673-6.811 μm, and Rz as 28.828-28.892 μm, while within-setting scatter remained non-negligible, particularly for Rz. Probabilistic regression models were evaluated using leave-one-setting-out validation, negative log predictive density, interval coverage, calibration diagnostics, and reliability-driven accept/reject analysis. For Ra and Rq, a low-capacity linear mean model with pooled variance achieved the best predictive density, indicating limited transportable heteroscedastic structure under setting-wise extrapolation. For Rz, a nonlinear mean model with pooled variance performed best. Unregularized two-stage variance learning produced unstable uncertainty estimates, whereas shrinkage regularization improved calibration and reduced spurious setting-dependent variance effects. The decision analysis showed that calibration strongly influences process acceptance, reliability thresholds sharply reduced the number of accepted settings, and shrinkage-stabilized uncertainty produced a conservative and consistent decision frontier. The main contribution of this work is the integration of grouped validation, probabilistic calibration, variance-shrinkage modelling, and reliability-aware decision analysis for surface roughness qualification in LPBF/DMLS SS316L.

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