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Integrating additive manufacturing (AM) tools in traditional mold-making provides complex yet affordable sand molds and cores. AM processes such as selective laser sintering (SLS) and Binder jetting three-dimensional printing (BJ3DP) are widely used for patternless sand mold and core production. This study aims to perform an in-depth literature review to understand the current status, determine research gaps and propose future research directions. In addition, obtain valuable insights into authors, organizations, countries, keywords, documents, sources and cited references, sources and authors.

This study followed the systematic literature review (SLR) to gather relevant rapid sand casting (RSC) documents via Scopus, Web of Science and EBSCO databases. Furthermore, bibliometrics was performed via the Visualization of Similarities (VOSviewer) software.

An evaluation of 116 documents focused primarily on commercial AM setups and process optimization of the SLS. Process optimization studies the effects of AM processes, their input parameters, scanning approaches, sand types and the integration of computer-aided design in AM on the properties of sample. The authors performed detailed bibliometrics of 80 out of 120 documents via VOSviewer software.

This review focuses primarily on the SLS AM process.

A SLR and bibliometrics using VOSviewer software for patternless sand mold and core production via the AM process.

This paper aims to report the effect of titanium oxide (TiO₂) particles on the specific wear rate (SWR) of alkaline treated bamboo and flax fiber-reinforced composites (FRCs) under dry sliding condition by using a robust statistical method.

In this research, the epoxy/bamboo and epoxy/flax composites filled with 0–8 Wt.% TiO₂ particles have been fabricated using simple hand layup techniques, and wear testing of the composite was done in accordance with the ASTM G99-05 standard. The Taguchi design of experiments (DOE) was used to conduct a statistical analysis of experimental wear results. An analysis of variance (ANOVA) was conducted to identify significant control factors affecting SWR under dry sliding conditions. Taguchi prediction model is also developed to verify the correlation between the test parameters and performance output.

The research study reveals that TiO₂ filler particles in the epoxy/bamboo and epoxy/flax composite will improve the tribological properties of the developed composites. Statistical analysis of SWR concludes that normal load is the most influencing factor, followed by sliding distance, Wt.% TiO2 filler and sliding velocity. ANOVA concludes that normal load has the maximum effect of 31.92% and 35.77% and Wt.% of TiO2 filler has the effect of 17.33% and 16.98%, respectively, on the SWR of bamboo and flax FRCs. A fairly good agreement between the Taguchi predictive model and experimental results is obtained.

This research paper attempts to include both TiO₂ filler and bamboo/flax fibers to develop a novel hybrid composite material. TiO₂ micro and nanoparticles are promising filler materials, it helps to enhance the mechanical and tribological properties of the epoxy composites. Taguchi DOE and ANOVA used for statistical analysis serve as guidelines for academicians and practitioners on how to best optimize the control variable with particular reference to natural FRCs.

Three-dimensional (3D) casting means using additive manufacturing (AM) techniques to print the mould for casting the cast tool. The printed mould, however, should be checked for its dimensional accuracy. 3D scanning can be used for the same. The purpose of this study is to combine the different AM techniques for 3D casting with 3D scanning to produce parts with close tolerance for preparing electrical discharge machining (EDM) electrodes.

The four processes, namely, stereolithography, selective laser sintering, fused deposition modelling and vacuum casting, are used to print the casting mould. The mould is designed in two halves, assembled to form a complete mould. The mould is 3D scanned in two stages: before and after using it as a casting mould. The mould's average and maximum dimensional deviations are calculated using 3D-scanned results. The eutectic Sn-Bi alloy is cast in the mould. The surface roughness of the mould and the cast tool are measured.

The cast tool is selected from the four processes in terms of dimensional accuracy and surface finish. The same is electroplated with copper. The microstructure of the cast tool (low-melting-point alloy) and deposited copper is analysed using a scanning electron microscope. Energy dispersive spectroscopy and X-ray diffraction techniques are used to verify the composition of the cast and coated alloy. The electroplated tool is finally tested on the EDM setup. The material removal rate and tool wear are measured. The performance is compared with a solid copper tool. The free-form customised EDM mould is also prepared, and the profile is cast out. The same is tested on the EDM. Thus, the developed path can be successfully used for rapid tooling applications.

The eutectic composition of Sn-Bi is cast in the 3D-printed mould using different AM techniques combined with 3D scanning quality to check its feasibility as an EDM electrode, which is a novel work and has not been done previously.

Herein, the authors report the effects of printing parameters, joining method, and annealing conditions on the structural performance of fusion-joined short-beam sections produced by additive manufacturing.

The authors first identified appropriate printing parameters for joining segmented short beams and then used those parameters to print and fusion-join segments with different configurations of stiffeners to form a longer section of a wing or small wind turbine blade structure.

It was found that the beams with three lateral and three base stiffening ribs give the highest flexural strength among the three beams investigated. Results on joined beams annealed at different conditions showed that annealing at 70 °C for 0.5 h yields higher performance than annealing at the same temperature for longer times. It is also found that in the case of the hot-plate-welded three-dimensional (3D)-printed structures, no annealing is needed for reaching a high strength-to-weight ratio, but annealing is helpful for maximizing the modulus-to-weight ratio. Both thermal buckling and edge wrapping were observed under annealing at 70°C for 0.5 h for 3D-printed beams comprising two lateral and four base stiffening plates.

Fusion-joining of additively manufactured segments is needed owing to the constraint in building volume of a typical commercial 3D-printer. However, study of the effect of process parameters is needed to quantify their effect on mechanical performance. This investigation has therefore identified key printing parameters and annealing conditions for fusion-joining short segments to form larger structures, from multiple 3D-printed sections, such as wind blade structures.

The purpose of this study is to assess the incorporation of fiber reinforced polymer (FRP) bars in concrete as a reinforcement enhances the corrosion resistance in a concrete structure. However, FRP bars are not practically used due to a lack of standard codes. Various codes, including ACI-440-17 and CSA S806-12, have been established to provide guidelines for the incorporation of FRP bars in concrete as reinforcement. The application of these codes may result in over-reinforcement. Therefore, this research presents the use of a machine learning approach to predict the accurate flexural strength of the FRP beams with the use of 408 experimental results.

In this research, the input parameters are the width of the beam, effective depth of the beam, concrete compressive strength, FRP bar elastic modulus and FRP bar tensile strength. Three machine learning algorithms, namely, gene expression programming, multi-expression programming and artificial neural networks, are developed. The accuracy of the developed models was judged by R², root means squared and mean absolute error. Finally, the study conducts prismatic analysis by considering different parameters. including depth and percentage of bottom reinforcement.

The artificial neural networks model result is the most accurate prediction (99%), with the lowest root mean squared error (2.66) and lowest mean absolute error (1.38). In addition, the result of SHapley Additive exPlanation analysis depicts that the effective depth and percentage of bottom reinforcement are the most influential parameters of FRP bars reinforced concrete beam. Therefore, the findings recommend that special attention should be given to the effective depth and percentage of bottom reinforcement.

Previous studies revealed that the flexural strength of concrete beams reinforced with FRP bars is significantly influenced by factors such as beam width, effective depth, concrete compressive strength, FRP bars’ elastic modulus and FRP bar tensile strength. Therefore, a substantial database comprising 408 experimental results considered for these parameters was compiled, and a simple and reliable model was proposed. The model developed in this research was compared with traditional codes, and it can be noted that the model developed in this study is much more accurate than the traditional codes.