Fused Deposition Modeling Process Parameters Research Articles

Optimizing FDM process parameters: predictive insights through taguchi, regression, and neural networks

Fused deposition modelling (FDM) is a popular additive manufacturing process used for rapid prototyping and the production of complex geometries. Despite its popularity, FDM’s susceptibility to variations in numerous process parameters can significantly impact the quality, design, functionality, and mechanical properties of 3D printed parts. This study explores thirteen FDM process parameters and their influence on the mechanical properties of polylactic acid (PLA) polymer, encompassing surface roughness, warpage, tensile and bending strength, elongation at break, deformation, and microhardness. The optimum parameters were identified alongside key contributors by applying the Taguchi method, signal-to-noise ratios, and analysis of variances (ANOVA). Notably, specific FDM parameters significantly affect the surface profile, with layer thickness contributing 32.65% and fan speed contributing 8.59% to the observed variations. Similarly, warping values show notable influence from nozzle temperature (29.53%), wall thickness (16.74%), layer thickness (16.56%), and retraction distance (12.80%). Tensile strength is primarily determined by wall thickness (31.83%), followed by infill percentage (26.73%) and infill pattern (16.18%). Elongation at break predominantly correlates with wall thickness (44.82%), with a supplementary contribution from nozzle temperature (10.90%). Microhardness lacks a dominant parameter. Bending strength variations primarily arise from layer thickness (38%), wall thickness (37.6%), and infill percentage (9.17%). Deformation tendencies are influenced by layer thickness (19.20%), print speed (11.37%), wall thickness, and fan speed (10.9% each). The optimized dataset of FDM process parameters was then employed in two prediction models: multiple-regression and artificial neural network (ANN). Evaluation based on the correlation coefficient (R2) and root mean squared error (RMSE) indicates that the ANN model outperforms the multiple-regression approach. The results indicate that precise control of FDM parameters, coupled with ANN predictions, facilitates the fabrication of 3D printed parts with the desired mechanical characteristics.

Effect of process parameters on the mechanical performance of FDM printed carbon fiber reinforced PETG

Additive manufacturing (AM), shows promising advancements in layer-based modeling displaying its potential for substantial waste reduction. Out of all the AM methods, the most cost-effective and popular choice for prototyping and making customized parts is fused deposition modeling (FDM). This research investigates how various FDM process parameters specifically layer thickness, raster angle, and printing speed affect the mechanical properties of 3D-printed PETG (polyethylene terephthalate glycol) specimens reinforced with carbon fiber (CF). Optimal settings, including a 0° raster angle, 0.2 mm layer thickness, and 40 mm/s printing speed, enhance both tensile and flexural strength due to improvement in material bonding and alignment. Interestingly, lower values for raster angle, layer thickness, and printing speed also lead to notable improvements in mechanical properties. Contour and interaction plots provide valuable insights into the significant impact of these parameters. The study also explores how different printing speeds influence the mechanical characteristics of CF-reinforced PETG specimens. Furthermore, the comparative analysis demonstrated that incorporating carbon fiber into recycled PET waste materials resulted in an approximately 30% increase in tensile strength. These findings imply that using CF with plastic waste in large-scale industries can pave the way for innovative solutions, enhancing performance and sustainability, especially in the automotive, and defence sectors.

Comprehensive analysis of fused deposition modeling process conditions for enhancing mechanical properties and surface quality of 3D-Printed poly-ether-ether-ketone

Fused deposition modeling (FDM) technology is a convenient manufacturing approach for poly-ether-ether-ketone (PEEK) components. In this study, a comprehensive study of the influence of FDM process parameters on the mechanical property and surface quality of 3D-printed PEEK printed with different directions was carried out. The results show that crystallinity of PEEK materials and the pores/gaps between the printing filaments jointly affect the mechanical properties and surface quality of PEEK structural parts. Then, material-structure-property linkage mechanism was revealed. Through the research conducted, crucial processing parameters to enhance the mechanical properties and surface quality of PEEK samples was determined. High tensile strength (91.75 MPa, which was close to the performance of injection molded components) and surface quality (Ra 7.9 μm) was obtained by regulation the nozzle temperature and annealing temperature to improve the crystallinity. Meanwhile, tensile strength of z-Sample was increased from 7.36 MPa to 18.30 MPa (with an increase of 148.64%). High Young's modulus (3.09 GPa) and large plastic strain (exceed 13.91%) was achieved by regulation the nozzle temperature and printing speed to reduce the porosity between PEEK filaments.

Mechanical properties of additively manufactured polymeric composites using sheet lamination technique and fused deposition modeling: A review

AbstractNowadays, it is possible to produce products with complex geometries, thanks to the experts who have continuously explored the advancements in additive manufacturing (AM) techniques. This review helps readers understand the opportunities and difficulties associated with developing parts for various industries, including aerospace, tooling, electronics, and biomedicine. The present work describes the landscape of polymeric composites using laminated object manufacturing (LOM) and fused deposition modeling (FDM). This paper discusses recent publications in the realm of AM related to composite fabrication, materials, techniques, functionality, and future scope. The effects of nanofiller reinforcement on the mechanical properties of composites have been thoroughly investigated. Furthermore, FDM process parameters such as layer thickness, infill speed, nozzle temperature, and raster angle have also been considered. Finally, the challenges and potential applications of LOM and FDM of additively manufactured composites have been summarized.

Parametric analysis of additively manufactured polymer nanocomposites: A experimental and multiscale study

Fused Filament Fabrication (FFF) or Fused Deposition Modeling (FDM) is a widely utilized additive manufacturing technology in a variety of sectors. However, voids, poor bonding between layers, and FDM parameters commonly affect the FDM-printed objects, altering their strength. Carbon nanotube (CNT) composites for FDM printing have been researched by researchers to improve their characteristics. This paper proposes an efficient three-scale computational model for predicting mechanical properties as well as a unique water-quenching process for preparing CNT-infused filaments. The influence of FDM process parameters on mechanical properties is revealed by extensive parametric analysis. When compared to pure ABS, the CNT-infused composite demonstrated better bonding and modulus. The experimental study showed that an increase in the layer height deteriorates the elastic modulus by 21.03 % and 27.92 %, for ABS and ABS-CNT, respectively. The infill density increases the modulus by 49.3 % and 69.6 % for pure ABS, from 100 % to 75 % and 50 %, respectively. For parts printed in 0–00 and 0–900 orientations, a 2.11 % and a 1.7 % decrease were found for pure ABS and nanocomposite, respectively. The computational results were in good agreement with the experimental findings, with the difference varying from 10.15 % to 5.5 % for 0.1 mm and 0.2 mm layer heights. For other parameters like the raster orientation, the difference for 0–00 and 0–900 was 5.3 % and 6.9 %, respectively. The computational results agree with the experimental results, making it a useful tool for optimizing FDM printing and exploiting CNTs to improve part performance.

Determination of the Effective Fused Deposition Modeling Process Parameters on the Mechanical Properties of Carbon Fiber–Reinforced Polypropylene Parts

Abstract Additive manufacturing is gaining popularity for producing components in industries such as automotive, aerospace, and medicine due to its potential to minimize material waste. Because the strength of the 3-D-printed part is so important, it’s crucial to do research and optimize process parameters to make the printed parts as strong as possible. This work focuses on the experimental investigation and discussion of the tensile and impact strength of parts made from carbon fiber–reinforced polypropylene (PP-CF) using the fused deposition modeling (FDM) technique. Various process parameters, including infill pattern, infill density, layer thickness, and build orientation, are examined on three different levels to determine their influence on the tensile and impact strength of the printed part. The outcomes of the analysis of variance (ANOVA) analysis reveal that infill density primarily affects impact strength, whereas layer thickness significantly influences tensile strength. The optimal combination of parameters leading to the maximum tensile and impact strength consists of a grid infill pattern, 60 % infill density, 0.36-mm layer thickness, and a 45° build orientation. Furthermore, fracture surface analysis is consistent with mechanical test results.

Evaluation on Material Anisotropy of Acrylonitrile Butadiene Styrene Printed via Fused Deposition Modelling

Thermoplastic polymers are widely used in industry to generate parts with reasonable production costs, lightweight, chemical stability, sustainability, and recyclability compared to other materials such as metals, metalloids, or even thermoset polymers. The innovative additive manufacturing (AM) techniques, e.g., fused deposition modelling (FDM), can be used to fabricate thermoplastic products with complex geometries and specific properties. However, the mechanical integrity of those FDM-printed plastic parts can be greatly impacted by a phenomenon named material anisotropy. In this study, an experimental study on a popular 3D printing polymer material—acrylonitrile butadiene styrene (ABS)—is performed to determine how FDM process parameters affect the mechanical properties of the printed ABS parts. This study uniquely concentrates on investigating mechanical anisotropy in FDM-printed ABS, delving into a combination of key printing parameters for a comprehensive exploration. Meanwhile, a finite-element-based numerical analysis is also utilised to numerically evaluate the influences of infill percentage and build orientations on the mechanical properties of the 3D-printed ABS materials for comparison. It generates a better understanding of material anisotropy and helps to find the optimal FDM process parameters to print high-quality ABS parts and may attract industrial interests in transitioning from traditional ABS part production methods such as injection moulding or hot pressing to additive manufacturing.

Optimizing Fused Deposition Modelling Process Parameters for Medical Grade Polymethylmethacrylate Flexural Strength

Optimizing Fused Deposition Modelling Process Parameters for Medical Grade Polymethylmethacrylate Flexural Strength

A decision support system for the selection of FDM process parameters using MOORA

Additive Manufacturing (AM) is an automated process of fabricating three-dimensional (3D) physical objects from a 3D-CAD data by adding layers of materials one upon another through a print head or nozzle without using any tooling components or machining environments. Due to freedom in design, any complex shape can be produced using this process. Fused Deposition Modeling (FDM) is one such AM technology that is commonly used for its simplicity, environment friendliness and low requirement for process monitoring. However, this technology is limited only to small-scale production due to high cost and high build time. The present work focuses on the development of a framework for parametric optimization of the FDM process using multi-objective optimization based on ratio analysis (MOORA). A CAD model of the cam follower mechanism has been prepared in the Solidworks platform and used in this experiment for optimization of build time and cost which have been considered as response variables of the experiment. The experiment has been conducted following the full factorial design of experiment (DoE) method.

Impact of Fused Deposition Modeling Process Parameters and Heat Treatment on Mechanical Characteristics and Product Quality: A Review

Fused Deposition Modeling (FDM) is indeed one of the most commonly used methods of additive manufacturing, particularly for printing polymers and fiber-reinforced polymer composites. When compared to more conventional production meth ods like injection molding, the key benefits of FDM include producing components with intricate shapes, minimal material wastage, shorter production times, and lower costs because no tooling is needed. However, the strength and surface quality of the product produced using this technique are lower, that can be improved by selecting the optimized design variable and applying heat treatment depending on how the product will be used in an industry. This review paper provides an overview of the effect of different process parameters on mechanical properties, print time, and surface characteristics of the parts made of polymers and fiber-reinforced polymer composites in addition to the challenges encountered during the printing of composites. It also discusses the new material’s development, such as natural fiber-reinforced polymer composites, the impact of heat treatment, and the void formation influence on the mechanical properties, build time, dimensional accuracy, and surface finish of 3D-printed parts.

Printing Parameter Optimization of Additive Manufactured PLA Using Taguchi Design of Experiment.

Three-dimensional printing (3DP), known as additive layer manufacturing (ALM), is a manufacturing process in which a three-dimensional structure is constructed by successive addition of deposited layers. Fused Deposition Modeling (FDM) has evolved as the most frequently utilized ALM process because of its cost-effectiveness and ease of operation. Nevertheless, layer adhesion, delamination, and quality of the finished product remain issues associated with the FDM process parameters. These issues need to be addressed in order to satisfy the requirements commonly imposed by the conventional manufacturing industry. This work is focused on the optimization of the FDM process and post-process parameters for Polylactic acid (PLA) samples in an effort to maximize their tensile strength. Infill density and pattern type, layer height, and print temperature are the process parameters, while annealing temperature is the post-process parameter considered for the investigation. Analysis based on the Taguchi L18 orthogonal array shows that the gyroid infill pattern and annealing cycle at 90 °C results in a maximum ultimate tensile strength (UTM) of 37.15 MPa. Furthermore, the regression model developed for the five variables under study was able to predict the UTS with an accuracy of more than 96%.

Optimization and experimental analysis of mechanical properties and porosity on FDM based 3D printed ABS sample

A basic thermoplastic used in Fused Deposition Modeling (FDM) additive manufacturing methods is acrylonitrile–butadiene–styrene (ABS). However, the mechanical qualities of ABS components may be regulated by their porosity. This study aims to optimize the FDM process parameters to minimize porosity and increase ABS parts' tensile and compressive strengths. The experiment has been designed based on Taguchi's philosophy. The machining parameters have been considered as Extrusion multiplier, Temperature (oC), and Layer thickness (mm). The machining output has been analyzed using the mean effect plot. Furthermore, a hybrid optimization called grey relational analysis (GRA) and genetic algorithm (GA) is used to obtain the optimum parameter setting. the multi-optimization parameter setting is achieved as extrusion multiplier = 1, temperature = 240 °C, and layer thickness = 0.1 mm. The confirmation is also conducted to verify the optimum parameter setting. The findings showed that the suggested approach effectively enhanced the FDM process parameters for ABS components, leading to better mechanical characteristics and decreased porosity.

Improvement of tensile strength of fused deposition modelling (FDM) part using artificial neural network and genetic algorithm techniques

PurposeThis paper presents an experimental investigation in establishing the relationship between FDM process parameters and tensile strength of polycarbonate (PC) samples using the I-Optimal design.Design/methodology/approachI-optimal design methodology is used to plan the experiments by means of Minitab-17.1 software. Samples are manufactured using Stratsys FDM 400mc and tested as per ISO standards. Additionally, an artificial neural network model was developed and compared to the regression model in order to select an appropriate model for optimisation. Finally, the genetic algorithm (GA) solver is executed for improvement of tensile strength of FDM built PC components.FindingsThis study demonstrates that the selected process parameters (raster angle, raster to raster air gap, build orientation about Y axis and the number of contours) had significant effect on tensile strength with raster angle being the most influential factor. Increasing the build orientation about Y axis produced specimens with compact structures that resulted in improved fracture resistance.Research limitations/implicationsThe fitted regression model has a p-value less than 0.05 which suggests that the model terms significantly represent the tensile strength of PC samples. Further, from the normal probability plot it was found that the residuals follow a straight line, thus the developed model provides adequate predictions. Furthermore, from the validation runs, a close agreement between the predicted and actual values was seen along the reference line which further supports satisfactory model predictions.Practical implicationsThis study successfully investigated the effects of the selected process parameters - raster angle, raster to raster air gap, build orientation about Y axis and the number of contours - on tensile strength of PC samples utilising the I-optimal design and ANOVA. In addition, for prediction of the part strength, regression and ANN models were developed. The selected ANN model was optimised using the GA-solver for determination of optimal parameter settings.Originality/valueThe proposed ANN-GA approach is more appropriate to establish the non-linear relationship between the selected process parameters and tensile strength. Further, the proposed ANN-GA methodology can assist in manufacture of various industrial products with Nylon, polyethylene terephthalate glycol (PETG) and PET as new 3DP materials.

Research Status of and Prospects for 3D Printing for Continuous Fiber-Reinforced Thermoplastic Composites.

Continuous fiber-reinforced thermoplastic composites (CFRTPCs) have advantages such as high specific strength, high specific modulus, corrosion resistance, and recyclability and are widely used in the fields of aerospace, rail transit, new energy, and so on. However, traditional methods for preparing CFRTPCs, such as placement and molding, rely more on forming molds, resulting in high manufacturing costs and a slow response speed, which limits the promotion and application of the new generation of CFRTPCs with complex configurations and designable performance. Three-dimensional printing can efficiently create products with multiple materials, complex structures, and integrated functions, introducing new ways and opportunities for the manufacturing of CFRTPCs. However, poor mechanical properties are the bottleneck problem in achieving 3D printing of CFRTPCs. This paper summarizes the research status of the fused deposition modeling (FDM) 3D printing process and the corresponding mechanical properties of CFRTPCs. The focus is on analyzing the influences of the FDM process parameters, such as the material type, printing temperature, speed parameters, layer thickness, scanning space, stacking direction, and fiber volume content, on the mechanical properties of CFRTPCs. Finally, the main problems and future prospects of current CFRTPCs-FDM are analyzed and forecasted, providing new references and ideas for 3D printing of high-performance CFRTPCs.

Experimental investigation and Taguchi optimization of FDM process parameters for the enhancement of tensile properties of Bi-layered printed PLA-ABS

Additive manufacturing (AM) technology has gained significant popularity, among which Fused Deposition Modeling (FDM) has emerged as the predominant technique for 3D printing. FDM offers the unique ability to achieve the desired and tailored engineering properties required for specific applications. This experimental study investigates the influence of varying FDM process parameters on the mechanical properties and highlights the optimal set of parameters for better tensile strength for a bi-layered composite of PLA-ABS (polylactic acid and acrylonitrile butadiene styrene). Also, it investigates the most-to-least influential printing parameters. Four process parameters were played out i.e., Infill density (50%, 75%, and 100%), number of layers (20,25 and 30), printing speed (20 mm sec−1, 40 mm sec−1, and 60 mm sec−1), and bed temperature (90 °C, 95 °C, and 100 °C), while keeping other parameters constant. Taguchi optimization technique was used for optimization and experiments were designed according to Taguchi orthogonal array L9 (3 4). After printing, the samples were evaluated for tensile properties and the results were analyzed. It is found that the infill density is the most influential parameter while bed temperature is the least influential. Infill density of 75%, 30 layers per part, a printing speed of 20 mm sec−1, and a bed temperature of 100 °C are the optimal set of parameters for better tensile strength. Alongside, percent elongation, printing time, and strength-to-weight ratio were also analyzed and correlated.

Analyzing Surface Roughness Variations in Material Extrusion Additive Manufacturing of Nylon Carbon Fiber Composites.

In recent years, fused deposition modeling (FDM) based on material extrusion additive manufacturing technology has become widely accepted as a cost-effective method for fabricating engineering components with net-shapes. However, the limited exploration of the influence of FDM process parameters on surface roughness parameters, i.e., Ra (average surface roughness), Rq (root mean square surface roughness), and Rz (maximum height of the profile) across different sides (bottom, top, and walls) poses a challenge for the fabrication of functional parts. This research aims to bridge the knowledge gap by analyzing surface roughness under various process parameters and optimizing it for nylon carbon fiber printed parts. A definitive screening design (DSD) was employed for experimental runs. The Pareto chart highlighted the significant effects of layer height, part orientation, and infill density on all surface roughness parameters and respective sides. The surface morphology was analyzed through optical microscopy. Multi-response optimization was performed using an integrated approach of composited desirability function and entropy. The findings of the present study hold significant industrial applications, enhancing the quality and performance of 3D printed parts. From intricate prototypes to durable automotive components, the optimized surfaces contribute to production of functional and visually appealing products across various sectors.

Optimization of polylactic acid 3D printing parameters based on support vector regression and cuckoo search

Abstract3D printing, also known as additive manufacturing (AM), is evolving from a rapid prototyping tool to a pillar of the Industry 4.0 revolution and widely used in various industries, since it can quickly and efficiently create workpieces with complex structures and integrated functions. After analyzing the 3D printing principle of fused deposition modeling (FDM), this paper proposes a deformation control‐oriented optimization method for process parameters of FDM of the polylactic acid (PLA) materials, based on support vector regression (SVR) and cuckoo search (CS). First, FDM printing principle and its main process parameters were analyzed. Two key parameters, printing temperature and printing speed, were selected for research, with the maximum shrinkage deformation as the workpiece contour accuracy index. Combining Latin Hypercube Sampling (LHS) with Finite Element Analysis (FEA), finite sample sets were generated. Second, learning from the sample data, the SVR surrogate model was built to predict the workpiece contour accuracy, and the nonlinear relationship between FDM process parameters and PLA shrinkage deformation was obtained. Finally, taking the combination of printing temperature and speed as the design variable and minimizing the maximum shrinkage deformation as the optimization objective, the FDM process parameters optimization model was established, and CS was used to search for the optimal parameters combination. Experimental comparison showed that the FEA results and SVR model in this method are correct and effective, and the PLA workpiece shrinkage deformation is smaller under the optimal parameters combination, which effectively improves the shape accuracy of FDM workpieces.Highlights Combining support vector regression (SVR) and cuckoo search (CS) can optimize polymer 3D printing process parameters. Effective finite element model was built for polymer 3D printing. SVR model established by simulation data can predict workpiece deformation. The optimal process parameters were found using CS algorithm.

A review on the melt extrusion-based fused deposition modeling (FDM): background, materials, process parameters and military applications

AbstractThe contemporary military sector needs a tailor-made product at a low cost and time. Fused Deposition Modeling (FDM) is a melt extrusion-based Additive Manufacturing technology for processing thermoplastics, composites, and biomaterials. FDM is competent in fabricating complex parts in different industries, including military, aerospace, automotive, biomedical, and jewellery. The FDM can process various materials and is ideal for fabrication prototypes, functional parts, visualization, concept proofs and fast product development. FDM is currently used in the military for novel components developments and maintenance, opening new logistics and supply chain management methods. Integrating sensors into weaponry for real-time physiological feedback and threat information is also possible. Additionally, FDM can fabricate trauma models for surgical planning and educating military surgeons. This paper presents the FDM background, filaments, and process parameters. Also, the work provides information to readers on the applications and possibilities of the FDM process from a military standpoint.

Understanding compressive viscoelastic properties of additively manufactured PLA for bone-mimetic scaffold design.

Bone tissue engineering has been recognized as a promising strategy to repair or replace damaged bone tissues. The mechanical properties of bone scaffolds play a critical role in successful bone regeneration, as it is essential to match the mechanical properties of the scaffold with the surrounding bone tissue. In this study, we investigated the effects of fused deposition modeling (FDM) process parameters, including printing speed, printing temperature, and layer thickness, on the compressive viscoelastic properties of polylactic acid (PLA) scaffolds. The compressive viscoelastic properties of bulk PLA specimens were characterized using a Zhu-Wang-Tang (ZWT) constitutive model under different compressive strain rates. A comprehensive statistical analysis comprising multivariate and univariate analysis of variance (MANOVA and ANOVA) and Tukey's post hoc analysis was utilized to quantify the effect of each FDM parameter on the viscoelastic mechanical properties of the PLA specimens. Subsequently, we fabricated modified face-centered cubic (MFCC) scaffolds using FDM and varied the FDM process parameters to achieve a compressive viscoelastic response that matched the natural trabecular bone tissue. The viscoelastic performance of the MFCC scaffolds was compared with traditional orthogonal cylindrical struts (OCS) scaffolds. Our methodology contributes to the design of bone-mimetic scaffolds with optimized mechanical properties by controlling FDM process parameters.

Wear prediction of 3D-printed acrylonitrile butadiene styrene-carbon nanotube nanocomposites at elevated temperatures

Abstract In this study, multi-wall carbon nanotube (MWCNT) reinforced acrylonitrile butadiene styrene (ABS) nanocomposite filaments are produced. Filaments are examined through thermogravimetric analysis (TGA) and definitive scanning calorimetry (DSC) analysis. Produced nanocomposite filaments are used in the fused deposition modeling (FDM) process to manufacture parts. Wear tests are conducted on 3D-printed parts using wear test apparatus with an attached heating module under different ambient temperatures. Hence, the influence of CNT reinforcement, along with different FDM process parameters and varying test conditions on the wear behavior of 3D-printed ABS-CNT parts, are examined. Worn surfaces of the specimens are examined by scanning electron microscopy (SEM). Nonlinear autoregressive exogenous (NARX) models are proposed for the prediction of the wear behavior of 3D-printed ABS-CNT nanocomposites. While wear rate is taken as output, ambient temperature and amount of nanofiller are accounted as input parameters along with the variation of coefficient of friction (COF) which is obtained from measured frictional force and three input-one output model structure is proposed for NARX. The use of multiple input-single output (MISO) model structure and examining the wear behavior of 3D-printed ABS-CNT samples under different wear test conditions with different FDM process parameters are the novelties in this work.