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◆ The International Journal of Advanced Manufacturing Technology2026-07-31· Workflow

Gaussian process regression for optimizing L-DED processing parameters in AlSi10Mg additive manufacturing

Jurandir Marcos Sá de Sousa, Henrique Santos Ferreira, Anselmo Thiesen, Marcos Vinicius Bento, William Mendes de Farias, Priscila Maria Barra Ferreira, Mustafa Awd, Evgeniya Kabliman, Alexandre Pinhel Soares, Juliane Ribeiro da Cruz

原始摘要(英文原文)· Original abstract
Abstract This work presents an experimental workflow coupled with statistical tools to optimize L-DED processing parameters to build defect-free AlSi10Mg multilayer parts. Experimental steps included the quantitative evaluation of single beads, single layers, and multilayers to define L-DED processing windows. Regression and desirability functions, together with multi-objective Bayesian optimization, were used to optimize the main L-DED process parameters to meet target single-layer features. The systematic collection of reliable experimental data was critical to the success of the optimization models, and a detailed methodology is provided. The proposed workflow enabled the production of crack- and macroporosity-free multilayer parts with high geometrical accuracy and metallurgical bond integrity, achieving densities from 99.55% to 99.80% and deposition efficiencies between 87 and 97%, significantly exceeding previously reported values. This work provides practical guidelines to evaluate the outcomes of the AM L-DED process and obtain multilayer AlSi10Mg parts free from deleterious defects.
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Gaussian process regression for optimizing L-DED processing parameters in AlSi10Mg additive manufacturing — 科研速览 Science Skim