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

Abstract

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.

Fuzzy particle swarm for the right-first-time of fused deposition

“…Each technology has its own applications and strengths and limitations. Among them all, FDM is the most common technology employed in many applications [5]. Therefore, a huge body of research has focused on analyzing, modelling and optimizing this technology and the various thermoplastic polymers that can be printed [6].…”

“…In addition to improving the generalization abilities of the neural network, it was combined with the dimensional analysis conceptual modelling to enhance the understanding of the FDM process [22]. Moreover, a radial based integrated network was proposed to simulate the FDM process and to systematically and accurately anticipate the characteristics of 3D printed parts [5]. Likewise, fuzzy logic systems were utilized to simulate the FDM process and to extract a linguistic understanding [23].…”

“…However, to the best of our knowledge, none of these studies has investigated the utilization of a right-first-time structure to identify the optimal 3D printing parameters that need to be used to 3D print parts with required characteristics, this being due to the fact that inverting highly nonlinear and complex models may not be a straightforward task that leads to a unique set of FDM parameters. Therefore, in this research paper, the radial based integrated network that was presented in [5] is utilized in a right-first-time framework. Such a framework is proposed to identify the FDM parameters that should be used to 3D print specimens with the desired mechanical characteristics.…”

“…However, there is no standard method that can be employed to provide the single/global optimal solution and replace the Pareto optimal set [28]. Therefore, the proposed right-first-time structure integrates the stochastic multi-objective particle swarm optimization and fuzzy logic in a way that can (i) effectively invert the radial based integrated network that was developed in [5]; and (ii) systematically identify the optimal set of the FDM parameters that need to be used to print 3D parts with the desired characteristics according to defined criteria. The right-first-time framework is then validated using a real medical case where three medical parts need to be produced with desired characteristics and then implanted in a 21year-old patient.…”

Prediction of the Mechanical Properties of Basalt Fiber Reinforced High-Performance Concrete Using Machine Learning Techniques

“…On the other hand, machine; earning (ML), as a sub-field of artificial intelligence, has attracted attention in various industries, such as the medical, mechanical, and manufacturing industries [ 21 , 22 , 23 ]. ML has been greatly applied to the material sciences and civil engineering within the past decade, mainly for the prediction of the mechanical properties of different types of concretes [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ].…”