Effect of glass fiber reinforcement on compressive strength of photopolymer composite fabricated using vat‐photopolymerization additive technique: An experimental and modeling approach

Abstract

Short or nanofillers‐reinforced composites increase the thermo‐mechanical characteristics of additive manufacturing (AM) base materials. Effective outputs were regulated via parametric optimisation and custom production. One of the most used AM method, Vat photopolymerization (VPP), creates complex and geometrically precise photopolymer parts. This study prepares photopolymer composite (PPC) samples with 2.0, 4.0, 6.0, and 8.0 volume percentages of short glass fiber (SGF) using an inter‐stage stirring method in DLP‐based VPP process. An adequate curing time and formable layer thickness were evaluated for the printability performance of SGF‐photopolymer composite. The 4.0% PPC‐sample showed a 20% higher compressive yield strength than pure photopolymer and other volume fractions. The consequences of increased strength values were assessed by the effect of stirring method and the fractography analysis of PPC specimen. The investigation additionally encompassed numerical simulations that employed an innovative technique for constructing and simulating three‐dimensional representative volume elements (RVEs). The RVEs were designed to replicate the experimental outcomes by randomly situating and orienting fillers within the matrix. The study employs the calibrated Three Network (TN) viscoplastic constitutive material model as the matrix property, and the simulation results demonstrate a margin of error of 5%–10% when compared to the experimental findings.Highlights Layer‐wise availability of SGFs through inter‐stage stirring method The exposure time and layer thickness were determined by Jacob's working curve Concept of RVE is used to combine individual SGF layers into a single layer SEM showed that increasing SGF layer resists cracking better than neat matrix Experimental data and numerical simulations are in good agreement

Predicting dimensional accuracy in 3D printed polydimethylsiloxane‐carbon nanotubes composites via machine learning

“…Additive manufacturing (AM), often known as 3D Printing, represents an emerging and highly promising field that has garnered substantial attention across diverse industries 1,2 . Within the realm of 3D Printing, there exists a specific process termed “Direct Ink Writing (DIW),” which involves the precise layer‐by‐layer extrusion of ink through a fine micron‐sized nozzle.…”