The development of a radial based integrated network for the modelling of 3D fused deposition

• Published in: Rapid prototyping journal 2023-01, Vol.29 (2), p.408-421
• Main Authors: AlAlaween, Wafa', Abueed, Omar, Gharaibeh, Belal, Alalawin, Abdallah, Mahfouf, Mahdi, Alsoussi, Ahmad, Albashabsheh, Nibal
• Format: Article
• Language: English
• Subjects: 3-D printers; Additive manufacturing; Data points; Deposition; Design of experiments; Dimensional analysis; Fused deposition modeling; Mathematical models; Mechanical properties; Medical research; Neural networks; Orthogonal arrays; Parameters; Polymers; Polyvinyl alcohol; Radial basis function; Rapid prototyping; Rheology; Surgical implants; Tensile strength; Three dimensional models; Three dimensional printing; Tissue engineering
• ISSN: 1355-2546; 1758-7670; 1355-2546
• DOI: 10.1108/RPJ-04-2022-0121

Purpose The purpose of this research paper is to investigate and model the fused deposition modelling (FDM) process to predict the mechanical attributes of 3D printed specimens. Design/methodology/approach By exploiting the main effect plots, a Taguchi L18 orthogonal array is used to investigate the effects of such parameters on three mechanical attributes of the 3D printed specimens. A radial-based integrated network is then developed to map the eight FDM parameters to the three mechanical attributes for both PEEK and PEKK. Such an integrated network maps and predicts the mechanical attributes through two consecutive phases that consist of several radial basis functions (RBFs). Findings Validated on a set of further experiments, the integrated network was successful in predicting the mechanical attributes of the 3D printed specimens. It also outperformed the well-known RBF network with an overall improvement of 24% in the coefficient of determination. The integrated network is also further validated by predicting the mechanical attributes of a medical-surgical implant (i.e. the MidFace Rim) as an application. Originality/value The main aim of this paper is to accurately predict the mechanical properties of parts produced using the FDM process. Such an aim requires modelling a highly dimensional space to represent highly nonlinear relationships. Therefore, a radial-based integrated network based on the combination of composition and superposition of radial functions is developed to model FDM using a limited number of data points.