Robust optimization of 3D printing process parameters considering process stability and production efficiency

With the rapid evolution and application of 3D printing technology, production capacity can be easily expanded by increasing the number of 3D printing machines or implementing distributed production to meet customer demand. According to our best knowledge, no paper has been found to address the process stability and production efficiency of 3D printers used in mass production or distributed manufacturing. This paper takes the material extrusion (MEX) 3D printing process as an example, aiming to minimize the difference in the quality of parts fabricated by different printers or nozzles at a lower printing cost by selecting the appropriate process parameter settings. First, combine the latent variable Gaussian process (LVGP) model with the split-plot experimental design to investigate the mechanical properties and costs of printing parts in relation to critical process parameters. Second, under consideration of the in-process uncertainty, the interval number theory is used to measure the split-plot effect of the mechanical properties of printing parts. Finally, a multi-objective optimization model integrating the split-plot effect and printing cost is established to get the Pareto optimal solution. The grey relational analysis (GRA) is used to select the optimal compromise solutions from the Pareto solution sets. The results of the validation experiments show that a more efficient and stable printing process can be achieved under the optimal parameter settings obtained by the proposed approach. This study will provide theoretical references and technical support for the standardization and industrialization of 3D printing technology.