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The aim of this study is to investigate the printing parameters of fused deposition modeling (FDM), a material extrusion-based method, and to examine the mechanical and thermal properties of their polylactic acid (PLA) components reinforced with copper, bronze, and carbon fiber micro particles.

Tensile test samples were created by extruding composite filament materials using FDM-based 3D printer. Taguchi method was used to design experiments where layer thickness, infill density, and nozzle temperature were the printing variables. Analysis of variance (ANOVA) was applied to determine the effect of these variables on tensile strength.

The results of this study showed that the reinforcement of metal particles in PLA material reduces strength and increases elongation. The highest tensile strength was obtained when the layer thickness, infill density, and nozzle temperature were set to 100 µm, 60%, and 230 °C, respectively. As a result of thermal analysis, cooper-PLA showed the highest thermal resistance among metal-based PLA samples.

It is very important to examine the mechanical and thermal quality of parts fabricated in FDM with metal-PLA composites. In the literature, the mechanical properties of metal-reinforced composite PLA parts have been examined using different factors and levels. However, the fabrication of parts using the FDM method with four different metal-added PLA materials has not been examined before. Another unique aspect of the study is that both mechanical and thermal properties of composite materials will be examined.

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.

This study aims to improve the mechanical properties of an object produced by fused deposition modelling with high-grade polymer.

The study uses an ensembled surrogate-assisted evolutionary algorithm (SAEA) to optimize the process parameters for example, layer height, print speed, print direction and nozzle temperature for enhancing the mechanical properties of temperature-sensitive high-grade polymer poly-ether-ether-ketone (PEEK) in fused deposition modelling (FDM) 3D printing while considering print time as one of the important parameter. These models are integrated with an evolutionary algorithm to efficiently explore parameter space. The optimized parameters from the SAEA approach are compared with those obtained using the Gray Relational Analysis (GRA) Taguchi method serving as a benchmark. Later, the study also highlights the significant role of print direction in optimizing the mechanical properties of FDM 3D printed PEEK.

With the use of ensemble learning-based SAEA, one can successfully maximize the ultimate stress and percentage elongation with minimum print time. SAEA-based solution has 28.86% higher ultimate stress, 66.95% lower percentage of elongation and 7.14% lower print time in comparison to the benchmark result (GRA Taguchi method). Also, the results from the experimental investigation indicate that the print direction has a greater role in deciding the optimum value of mechanical properties for FDM 3D printed high-grade thermoplastic PEEK polymer.

This study is valid for the parameter ranges, which are defined to conduct the experimentation.

This study has been conducted on the basis of taking only a few important process parameters as per the literatures and available scope of the study; however, there are many other parameters, e.g. wall thickness, road width, print orientation, fill pattern, roller speed, retraction, etc. which can be included to make a more comprehensive investigation and accuracy of the results for practical implementation.

This study deploys a novel meta-model-based optimization approach for enhancing the mechanical properties of high-grade thermoplastic polymers, which is rarely available in the published literature in the research domain.

The investigation aims to improve Nd: YAG laser technology for precision cutting of carbon fiber reinforcing polymers (CFRPs), specifically those containing newly created resin (NDR) from the polyethylene and polyurea group, is the goal of the study. The focus is on showing how Nd: YAG lasers may be used to precisely cut CFRP with NDR materials, emphasizing how useful they are for creating intricate and long-lasting components.

The study employs a systematic approach that includes complicated factorial designs, Taguchi L27 orthogonal array trials, Gray relational analysis (GRA) and machine learning predictions. The effects of laser cutting factors on CFRP with NDR geometry are investigated experimentally, with the goal of optimizing the cutting process for greater quality and efficiency. The approach employs data-driven decision-making with GRA, which improves cut quality and manufacturing efficiency while producing high-quality CFRP composites. Integration of machine learning models into the optimization process significantly boosts the precision and cost-effectiveness of laser cutting operations for CFRP materials.

The work uses Taguchi L27 orthogonal array trials for systematically explore the effects of specified parameters on CFRP cutting. The cutting process is then optimized using GRA, which identifies influential elements and determines the ideal parameter combination. In this paper, initially machining parameters are established at level L3P3C3A2, and the optimal machining parameters are determined to be at levels L3P2C3A3 and L3P2C1A2, based on predictions and experimental results. Furthermore, the study uses machine learning prediction models to continuously update and optimize kerf parameters, resulting in high-quality cuts at a lower cost. Overall, the study presents a holistic method to optimize CFRP cutting processes employing sophisticated techniques such as GRA and machine learning, resulting in better quality and efficiency in manufacturing operations.

The novel concept is in precisely measuring the kerf width and deviation in CFRP samples of NDR using sophisticated imaging techniques like SEM, which improves analysis and precision. The newly produced resin from the polyethylene and polyurea group with carbon fiber offers a more precise and comprehensive understanding of the material's behavior under different cutting settings, which makes it novel for kerf width and kerf deviation in their studies. To optimize laser cutting settings in real time while considering laser machining conditions, the study incorporates material insights into machine learning models.

The impact of the application of hollow structures through variations of infill patterns and their density on the tensile properties was considered. The mechanical properties of the parts have a significant influence on the behavior and reliability of the parts in exploitation.

In this paper, the mechanical properties of the additively manufactured ABS material were investigated depending on the FDM printing parameters, which relate both to process parameters such as printing velocity and layer thickness, but also to coupled influence with the change of specimen orientation, that is raster angle. A standard tensile test was applied so that the specimens were prepared according to the ASTM D638 standard.

The results of the conducted experimental research enable the identification of the optimal choice of printing parameters for additively produced ABS materials with the highest values of strain at break and tensile strength. The significance of the obtained results is reflected in the recommendations for the selection of appropriate combination of process parameters for additive manufacturing of ABS parts using FDM technology.

This paper evaluates influence of FDM printing parameters on the tensile strength of parts and therefore on the reliability of the parts.

This study aims to evaluate in situ oxidative polymerization of aniline (Ani) as a post-processing method to promote extrusion-based 3D printed parts, made from insulating polymers, to components with functional properties, including electrical conductivity and chemical sensitivity.

Extrusion-based 3D printed parts of polyethylene terephthalate modified with glycol (PETG) and polypropylene (PP) were coated in an aqueous acid solution via in situ oxidative polymerization of Ani. First, the feedstocks were characterized. Densely printed samples were then used to assess the adhesion of polyaniline (PAni) and electrical conductivity on printed parts. The best feedstock candidate for PAni coating was selected for further analysis. Last, a Taguchi methodology was used to evaluate the influence of printing parameters on the coating of porous samples. Analysis of variance and Tukey post hoc test were used to identify the best levels for each parameter.

Colorimetry measurements showed significant color shifts in PP samples and no shifts in PETG samples upon pullout testing. The incorporation of PAni content and electrical conductivity were, respectively, 41% and 571% higher for PETG in comparison to PP. Upon coating, the surface energy of both materials decreased. Additionally, the dynamic mechanical analysis test showed minimal influence of PAni over the dynamic mechanical properties of PETG. The parametric study indicated that only layer thickness and infill pattern had a significant influence on PAni incorporation and electrical conductivity of coated porous samples.

Current literature reports difficulties in incorporating PAni without affecting dimensional precision and feedstock stability. In situ, oxidative polymerization of Ani could overcome these limitations. However, its use as a functional post-processing of extrusion-based printed parts is a novelty.

This study aims to present and evaluate a novel analytical model to predict the structural properties of parts fabricated by fused filament fabrication (FFF) along any non-orthogonal direction.

A new analytical model to estimate the ultimate tensile stress (UTS) and elastic modulus (E) of polylactic acid (PLA)-FFF parts fabricated in any non-orthogonal build orientation, is proposed. The new model is based on an ellipsoid, two angles that define the orientation with respect to the build axes, the infill value and the structural properties along the build axes. The proposed model is evaluated by comparing the UTS and E properties predicted by this model, with the results obtained from experimental tensile tests on PLA-FFF specimens manufactured using variable infill values and non-orthogonal build orientations.

The proposed model is able to predict with good precision the structural properties of PLA-FFF parts along any direction and infill value.

Although the study and results are limited to the UTS and E tensile properties of PLA-FFF components, the model may be extended to other materials or similar additive manufacturing processes.

The new proposed model is able to determine the structural properties of FFF components in any direction, so it can be used during the design process of FFF parts, reducing the need for experimental tests and speeding up the product development process.

Existing models to predict the structural properties of FFF components are limited to orthogonal build orientations (X, Y and Z); however, the new proposed model is able to predict the tensile properties in any direction and infill value. In addition, a new set of experimental data about the structural behaviour of PLA-FFF parts along non-orthogonal build orientations is provided, extending the existing results in the literature.

The rapid growth of 3D printing has transformed the cost-effective production of prototypes and functional items, primarily using extrusion technology with thermoplastics. This study aims to focus on optimizing mechanical properties, precisely highlighting the crucial role of mechanical compressive strength in ensuring the functionality and durability of 3D-printed components, especially in industrial and engineering applications.

Using the Box−Behnken experimental design, the research investigated the influence of layer thickness, wall perimeter and infill level on mechanical resistance through compression. Parameters such as maximum force, printing time and mass utilization are considered for assessing and enhancing mechanical properties.

The layer thickness was identified as the most influential parameter over the compression time, followed by the degree of infill. The number of surface layers significantly influences both maximum strength and total mass. Optimization strategies suggest reducing infill percentage while maintaining moderate to high values for surface layers and layer thickness, enabling the production of lightweight components with adequate mechanical strength and reduced printing time. Experimental validation confirms the effectiveness of these strategies, with generated regression equations serving as a valuable predictive tool for similar parameters.

This research offers valuable insights for industries using 3D printing in creating prototypes and functional parts. By identifying optimal parameters such as layer thickness, surface layers and infill levels, the study helps manufacturers achieve stronger, lighter and more cost-efficient components. For industrial and engineering applications, adopting the outlined optimization strategies can result in components with enhanced mechanical strength and durability, while also reducing material costs and printing times. Practitioners can use the developed regression equations as predictive tools to fine-tune their production processes and achieve desired mechanical properties more effectively.

This research contributes to the ongoing evolution of additive manufacturing, providing insights into optimizing structural rigidity through polylactic acid (PLA) selection, Box−Behnken design and overall process optimization. These findings advance the understanding of fused deposition modeling (FDM) technology and offer practical implications for more efficient and economical 3D printing processes in industrial and engineering applications.

The study aims to determine the the optimal value of output parameters of a variable compression ratio (CR) diesel engine are investigated at different loads, CR and fuel modes of operation experimentally. The output parameters of a variable compression ratio (CR) diesel engine are investigated at different loads, CR and fuel modes of operation experimentally. The performance parameters like brake thermal efficiency (BTE) and brake specific energy consumption (BSEC), whereas CO emission, HC emission, CO₂ emission, NO_x emission, exhaust gas temperature (EGT) and opacity are the emission parameters measured during the test. Tests are conducted for 2, 6 and 10 kg of load, 16.5 and 17.5 of CR.

In this investigation, the first engine was fueled with 100% diesel and 100% Calophyllum inophyllum oil in single-fuel mode. Then Calophyllum inophyllum oil with producer gas was fed to the engine. Calophyllum inophyllum oil offers lower BTE, CO and HC emissions, opacity and higher EGT, BSEC, CO₂ emission and NO_x emissions compared to diesel fuel in both fuel modes of operation observed. The performance optimization using the Taguchi approach is carried out to determine the optimal input parameters for maximum performance and minimum emissions for the test engine. The optimized value of the input parameters is then fed into the prediction techniques, such as the artificial neural network (ANN).

From multiple response optimization, the minimum emissions of 0.58% of CO, 42% of HC, 191 ppm NO_x and maximum BTE of 21.56% for 16.5 CR, 10 kg load and dual fuel mode of operation are determined. Based on generated errors, the ANN is also ranked for precision. The proposed ANN model provides better prediction with minimum experimental data sets. The values of the R² correlation coefficient are 1, 0.95552, 0.94367 and 0.97789 for training, validation, testing and all, respectively. The said biodiesel may be used as a substitute for conventional diesel fuel.

The blend of Calophyllum inophyllum oil-producer gas is used to run the diesel engine. Performance and emission analysis has been carried out, compared, optimized and validated.

Material extrusion (MEX) is one of the most known techniques in the additive manufacturing (AM) sector to produce components with a wide range of polymeric and composite materials. Moisture causes alterations in material properties and for filaments strongly hygroscopic like nylon-based composites this means greater ease of deterioration. Drying the filament to reduce the moisture content may not be sufficient if the humidity is not controlled during printing. The purpose of this study is to achieve the recovery of a commercial nylon-based composite filament by applying process optimization using an open source MEX machine.

A statistical approach based on Taguchi’s method allowed to achieve an ultimate tensile strength (UTS). A verification of the geometrical capabilities of the process has been performed according to the standard ISO/ASTM 52902-2019. Chemical tests were also carried out to test the resistance to corrosion in acid and basic solutions.

An UTS of 71.37 MPa was obtained, significantly higher than the value declared by the filament’s manufacturer (Stratasys Inc., USA). The best configuration of process parameters leads to good geometrical deviations for flat surfaces, in a range of 0.01 and 0.38 for flatness, while cylindrical faces showed more important deviations from the nominal values. The good applicability of the material in corrosive environments has been confirmed.

This study examined the performance restoration potential of a nylon composite filament that was significantly affected by storage conditions. For the filament manufacturer, if the material remains in ambient air for an hour or idle in the machine for more than 24 h, the material may no longer be suitable for printing. The study highlighted that the drying of the filament must not be temporary but constant to guarantee printability, and, by acting on the process parameters, it is possible to obtain better mechanical properties than declared by the manufacturer.