Experimental investigation and statistical analysis of additively manufactured onyx-carbon fiber reinforced composites

This is the peer reviewed version of the following article published in final form at https://doi.org/10.1002/app.50338. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.Availability of additive manufacturing (AM) has influenced the scientific community to improve on production and versatility of the components created with several associated technologies. Adding multiple substances through superimposing levels is considered as a part of three-dimensional (3D) printing innovations to produce required products. These technologies are experiencing an increase in development nowadays. It requires frequently adding substance and has capacity to fabricate extremely complex geometrical shapes. However, the fundamental issues with this advancement include alteration of capacity to create special products with usefulness and properties at an economically viable price. In this study, significant procedural parameters: layer designs/ patterns (hexagonal, rectangular and triangular) and infill densities (30, 40 and 50%) were considered to investigate into their effects on mechanical behaviors of fused deposition modeling (FDM) or 3D-printed onyx-carbon fiber reinforced composite specimens, using a high-end 3D printing machine. Mechanical (tensile and impact) properties of the printed specimens were conclusively analyzed. From the results obtained, it was observed that better qualities were achieved with an increased infill density, and rectangular-shaped design exhibited an optimum or maximum tensile strength and energy absorption rate, when compared with other counterparts. The measurable relapse conditions were viably evolved to anticipate the real mechanical qualities with an accuracy of 96.4%. In comparison with other patterns, this was more closely predicted in the rectangular design, using regression models. The modeled linear regression helps to define the association of two dependent variables linked with properties of the dissimilar composite material natures. The models can further predict response of the quantities before and also guide practical applications.Peer reviewe