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The tear strength (Ts) is a significant property for any kind of soft polymeric material such as rubber, elastomer, viscoelastic material and its composites, to quantify the suitability of a material for any shape memory applications. Many times, the soft elastomeric polymer material has to be capable enough to deform to a maximum extent of displacement but at the same time, it has to withstand the maximum load without fail. Along with shape recovery properties (i.e. the ability to recover its shape from programmed to the original), the success of the shape memory cycle is mainly depending on its stiffness and strength. It has to resist tear during stretching (i.e. programming stage) as repeatedly subjected to deformation, and, hence, it is important to study the tear behaviour for shape memory polymers (SMPs) and their composites. The purpose of the work is to investigate the effect of parameters on Ts of 4D printed specimen using Taguchi method.

The objective of the work is to tailor the Ts of SMPs by reinforcing the graphene nano particles (GNPs) in a blended photopolymer (PP) resin with flexible PP and hard PP resin. In this study, a total of nine experiments were designed based on the L9 orthogonal array (OA) using the design of experiments (DOEs). All the shape memory photopolymer composite’s (SMPPCs) specimens are fabricated using masked stereolithography (MSLA), also known as resin three-dimensional printing (R3DP) technique.

Specimens are tested using universal testing machine (UTM) for maximum tear force (F_max) and displacement (δ) caused by tearing the specimen to evaluate the strength against the tear. The results showed that the Wt.% of resin blend highly influenced both F_max and δ, while GNPs also had an impact on δ. The specimens are offering more tear resistance for those specimens blended with less Wt.% of flexible PP at the same time the specimens enable more δ for those specimens reinforced with 0.3 Wt.% GNPs at 10-s exposure time. The optimum combinations are A1, B1 and C3 for the F_max and T_s and at the same time A1, B3 and C3 for δ.

To customise the tear resistance of SMPPCs using MSLA 3 D printing, this study suggested a blend of PP resins reinforced with GNPs. This opens up a new path for creating novel, inexpensive multi-functional 4-dimensional (4D) printed parts.

The use of flexible PP and hard PP resin blends, fabricating the SMPPCs specimens using 3 D printed MSLA technology, investigating the effect of GNPs, resin blend and exposure time, optimizing the process parameters using Taguchi and the work were all validated using confirmation tests and regression analysis using test train method, which increases the originality and novelty.

Shape memory materials are functional materials having a good number of applications due to their unique features of programmable material technology such as self-stretching, self-assembly and self-tightening. Advancements in today’s technology led to the easy fabrication of such novel materials using 3D printing techniques. When an external stimulus causes a 3D printed specimen to change shape on its own, this process is known as 4D printing. This study aims to investigate the effect of graphene nano platelet (GNPs) on the shape memory behaviour of shape memory photo polymer composites (SMPPCs) and to optimize the shape-changing response by using the Taguchi method.

SMPPCs are synthesized by blending different weight fractions (Wt.%) of flexible or soft photopolymer (FPP) resin with hard photopolymer (HPP) resin, then reinforced with GNPs at various Wt.% to the blended PP resin, and then fabricated using masked stereolithography (MSLA) apparatus. The shape memory test is conducted to assess the shape recovery time (T), shape fixity ratio (R_f), shape recovery ratio (R_r) and shape recovery rate (V_r) using Taguchi analysis by constructing an L9 orthogonal array with parameters such as Wt.% of a blend of FPP and HPP resin, Wt.% of GNPs and holding time.

SMPPCs with A3, B3 and C2 result in a faster T with 2 s, whereas SMPPCs with A1, B1 and C3 result in a longer T with 21 s. The factors A and B are ranked as the most significant in the Pareto charts that were obtained, whereas C is not significant. It can be seen from the heatmap plot that when factors A and B increase, T is decreasing and V_r is increasing. The optimum parameters for T and V_r are A3, B3 and C2 at the same time for R_f and R_r are A1, B3 and C1.

Faster shape recovery results from a higher Wt.% of FPP resin in a blend than over a true HPP resin. This is because the flexible polymer links in FPP resin activate more quickly over time. However, a minimum amount of HPP resin also needs to be maintained because it plays a role in producing higher R_f and V_r. The use of GNPs as reinforcement accelerates the T because nanographene conducts heat more quickly, releasing the temporary shape of the specimen more quickly.

The use of FPP and HPP resin blends, fabricating the 4D-printed SMPPCs specimens with MSLA technology, investigating the effect of GNPs and optimizing the process parameters using Taguchi and the work was validated using confirmation tests and regression analysis, which increases the originality and novelty.

This paper aims to model the aerodynamic flow characteristics of NACA0010 for various angle of attacks including stall for incompressible flows using panel methods. This paper also aims to quantify the surface pressure distribution on streamlined bodies and validate the results with analytical Jukouwski method and inverse panel methods that can predict the aerodynamic flow behaviour using the geometric iteration approach.

The 2 D panel method was implemented in Qblade software v.06 which uses the fundamental panel method which rely on source strengths and influence coefficients to determine the velocity and pressure fields on the surface. The software implements the boundary layer or viscous effects to determine the influence on aerodynamic performance at various angles of attack. Jukouwski method is also evaluated for predicting aerodynamic characteristics and is based on the geometric iteration approach. Then complex aerodynamic flow potentials are determined based on the source strengths which are used to predict the pressure and velocity fields.

At low to moderate angles of attack, panel and Jukouwski methods predict similar results for surface pressure coefficients comparable to Hess and Smith inverse method. In comparison to panel method, results from the Jukouwski mapping method predicted the pressure coefficient conservatively for the same free stream conditions. With increase in Reynolds number, lift coefficient and aerodynamic performance improved significantly for un-tripped aerofoil when stall angle is approached when compared to tripped aerofoil.

This study demonstrated that panel methods have higher efficacy in terms of computational time or resources and thus can provide benefits to many real-world aircraft or aerospace design applications.

Even though panel and Jukouwski methods have been studied extensively in the past, this paper demonstrates the efficacy of both methods for modelling aerodynamic flows that range between moderate to high Reynolds number which are critical for many aircraft applications. Both methods have been validated with analytical and inverse design methods which are able to predict aerodynamic flow characteristics for simple bluff bodies, streamlined aerofoils as well as bio-inspired corrugated aerofoils.

Masked stereolithography (MSLA) or resin three-dimensional (3D) printing is one of the most extensively used high-resolution additive manufacturing technologies. Even though, the quality of 3D printing is determined by several factors, including the equipment, materials and slicer. Besides, the layer height, print orientation and exposure time are important processing parameters in determining the quality of the 3D printed green state specimen. The purpose of the paper is to optimize the printing parameters of the Masked Stereolithography apparatus for its dimensional correctness of 3D printed parts using the Taguchi method.

The acrylate-based photopolymer resin is used to produce the parts using liquid crystal display (LCD)-type resin 3D printer. This study is mainly focused on optimizing the processing parameters by using Taguchi analysis, L-9 orthogonal array in Minitab software. Analysis of variance (ANOVA) was performed to determine the most influencing factors, and a regression equation was built to predict the best potential outcomes for the given set of parameters and levels. The signal-to-noise ratios were calculated by using the smaller the better characteristic as the deviations from the nominal value should be minimum. The optimal levels for each factor were determined with the help of mean plots.

Based on the findings of ANOVA, it was observed that exposure time plays an important role in most of the output measures. The model’s goodness was tested using a confirmation test and the findings were found to be within the confidence limit. Also, a similar specimen was printed using the fused filament fabrication (FFF) technique; it was compared with the quality and features of MSLA 3D printing technology.

The study presents the statistical analysis of experimental results of MSLA and made a comparison with FFF in terms of dimensional accuracy and print quality.

Many previous studies reported the results based on earlier 3D printing technology such as stereolithography but LCD-based MSLA is not yet reported for its dimensional accuracy and part quality. The presented paper proposes the use of statistical models to optimize the printing parameters to get dimensional accuracy and the good quality of the 3D printed green part.