Robust Parameter Design of Functional Response for Additive Manufacturing Process  
Author Ning Xu


Co-Author(s) Jianjun Wang; Chunfeng Ding; Xiaolei Ren


Abstract As for the quality design problem of the 3D printing process (additive manufacturing process), this paper used a twostage Bayesian hierarchical model to solve the robust parameter design problem of the functional response from a 3D printing manufacturing process. First, the experimental data was obtained by using the measurement equipment at the additive manufacturing quality laboratory from Jiangsu Province Engineering Research Center of quality improvement for high-end equipment. Secondly, we analyzed the experimental data to obtain a functional response, and then built the functional relationship between the output functional response and the performance parameters of the 3D printer, namely, input factors (such as printing temperature, machine temperature, printing layer thickness). Thirdly, a quadratic quality loss function was used to optimize the objective function, and the optimal parameter design value of the input factor was obtained by minimizing the objective function. Finally, the grey relational method was used to analyze the closeness between the functional curve of the confirmation experiment and the target curve. The actual case showed that the proposed method in this paper can obtain robust and reliable parameter design settings.


Keywords Functional Response; Robust Parameters Design; Two-stage Model; Quality Loss Function; Additive Manufacturing; Grey Relation Analysis
    Article #:  RQD27-131

Proceedings of 27th ISSAT International Conference on Reliability & Quality in Design
Virtual Event

August 4-6, 2022