Fused Deposition Modeling Process Research Articles

Development of reduced volume endosseous cuspid tooth implant using topology optimization by SIMP technique for improved osseointegration

The article aims to design and develop a topology-optimized endosseous cuspid tooth implant of the maxilla region. The manuscript presents a numerical analysis of the resulting von Mises stresses and effective strain resulting in the topology-optimized implant with occlusal loading of 110 N. Solid Isotropic Material with Penalization (SIMP) method is employed for topology optimization and four different models, namely model-1, model-2, model-3, and model-4, are developed based on volume reduction rates of 8%, 16%, 24%, and 32%, respectively. FEA results highlight that the maximum stress and strain in the screw increases with volume reduction rates. The comparative analyses of the resulting stresses in the compact and cancellous bone along with the strain in the screw led to the conclusion that model-1, model-2, and model-3 resulted in moderate stresses on compact and cancellous bone compared to the original model of the implant. However, the screw and bones are subjected to maximum stress and strain in the model-4. The study concludes that model-2, with 16% reduced volume and 14.2% reduced mass as compared to the original implant, may be considered as the optimized design of the model. The resulting model offers a significant reduction in the weight and volume with a minor increase in effective stress and strain without negatively impacting the functionality and bio-mechanical performance of the implant. The optimized dental implant prototype is also fabricated as a proof of concept by the Fused Deposition Modelling process.

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.

Desirability function analysis approach for optimization of fused deposition modelling process parameters

Abstract This study uses desirability function analysis to optimize the process parameters namely layer thickness, fill pattern, fill density, and build orientation for fused deposition modelling on polyethylene terephthalate glycol material. The Taguchi-desirability function analysis is one of the optimization techniques for the multi-objective decision-making process. Taguchi L9 orthogonal array is employed in the test trials during the fused deposition modelling process. The output responses measured are dimension error, ductility, and tensile strength. The goal of process parameter optimization is to attain the best of the trials for minimal dimension error and maximum tensile strength and ductility. The best feasible amalgamation of input variables is discovered for the investigated and assessed response features utilizing desirability function analysis. As per the results obtained from experiments, it is concluded that the optimal combination of parameters is layer thickness of 0.1 mm, build orientation of 0°, fill density of 90 % and hexa fill pattern. By following this combination, the error in the dimensions was slightly increased which needed to be reduced, but both tensile strength and ductility were increased which are more desirable characteristics.

Insights into Temperature Simulation and Validation of Fused Deposition Modeling Processes

In fused deposition modeling (FDM), the cooling history impacts the bonding between filaments and layers. The existence of thermal gradients can cause non-homogeneous properties and localized stress points that may affect the individual filaments, resulting in distortion and detachment. Thermal analysis can aid in understanding the manufacturing flaw, providing necessary tools for the optimization of the printing trajectory. The present work is intended to deepen understanding of the thermal phenomena occurring during the extrusion of polymeric materials, aiming at more efficient three-dimensional (3D) printing methods. A one-dimensional (1D) finite differential method was implemented using MATLAB to simulate the temperature evolution of an extruded filament, and the results were compared with two-dimensional (2D) COMSOL Multiphysics simulations, and experimentally validated using infrared thermography. Acrylonitrile–butadiene–styrene (ABS) was used as a test material. The energy dissipation includes forced convection and radiation heat losses to the surrounding medium.

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.

Effects of combined infill angle with honeycomb pattern on the mechanical properties of HIPS and polypropylene in FDM process

The additive manufacturing Process (AMP) is a leading-edge production process in the manufacturing industry. Fused deposition modelling (FDM) is frequently employed for the production of complicated shapes. The most critical parameters are built orientation, layer thickness, infill pattern and nozzle diameter. This study examines the impact of a combined infill angle (55°) with a honeycomb pattern in a 3D-printed end product. The combined samples are used to analyze their effects on mechanical properties under static conditions. The HIPS (high-impact polystyrene) and PP (polypropylene) are used to print the samples. The 3D printed specimen has undergone uniaxial tensile, three-point bend, compression, and hardness tests. The result shows that the value of tensile, flexural, and hardness tests for HIPS is 47.37%, 49.44%, and 8.7% improvement than the PP. But in the compression test, PP shows a significant performance than the HIPS (approx. 55.75%). The validation results show improvement of HIPS and PP with a combination of infill angle 55° and honeycomb pattern. The tensile strength and Flexural strength have been improved upto 8.8% and 53.9%, respectively, for HIPS. Moreover, the percentage of elongation increased upto 23.4% for PP, but 17.9% decrease for HIPS. This study introduces a unique viewpoint that adds to the existing knowledge in this field.

Adaptive Scaling of Components in the Fused Deposition Modeling Process

AbstractCurrently, the fused deposition modeling (FDM) process is the most common additive manufacturing technology. The principle of the FDM process is the strand wise deposition of molten thermoplastic polymers, by feeding a filament trough a heated nozzle. Due to the strand and layer wise deposition the cooling of the manufactured component is not uniform. This leads to dimensional deviations which may cause the component to be unusable for the desired application. In this paper, a method is described which is based on the shrinkage compensation through the adaption of every single raster line in components manufactured with the FDM process. The shrinkage compensation is based on a model resulting from a DOE which considers the main influencing factors on the shrinkage behavior of raster lines in the FDM process. An in‐house developed software analyzes the component and locally applies the shrinkage compensation with consideration of the boundary conditions, e.g., the position of the raster line in the component and the process parameters. Following, a validation using a simple geometry is conducted to show the effect of the presented adaptive scaling method.

A comparative study on the effect of infill density, shape structure, and materials on tensile properties of FDM printed components

The Fused Deposition Modeling (FDM) 3D-printing method makes it possible to create high-complexity geometries quickly. The strength of the FDM printed components depends on various process factors, such as the infill density, the infill shape structure, the material, and a great number of other variables. This work evaluates the effect of infill structure and infill density on the tensile strength of different materials. PLA, ABS, and PETG materials were considered during the work with different infill densities and structures. It compares the deformation behavior of different materials with the same infill structures and density. The experimental results show that the tensile deformation of material depends on the infill structure and density. For PLA and ABS, triangular infill structures show higher tensile strength at each infill density, whereas for ASA tri-hexagonal structure shows higher strength. This work helps the researchers and academician to enhance their knowledge in the field of FDM process.

Machine vision assisted fused deposition modelling process optimization using grey level co-occurrence matrix

Taguchi's Design of Experiments was applied to determine the effect of Fused Deposition Modeling (FDM) parameters such as layer thickness, infill density, and infill line thickness and deposition pattern on surface roughness. Nylon material was fused to produce samples. Conventional probe surface area measurements were performed to extract the Ra values of the samples. Subsequently, the non-contact measurement of 3D printed textures using the Gray Level Co-Occurrence Matrix (GLCM) algorithm was introduced through image processing. The FDM-printed surface image parameters such as correlation and homogeneity are Ra responsive and therefore a signal-to-noise ratio (S/N) analysis was performed to identify optimal parameters. A slight, unexplained deviation in surface image parameters was detected, and this inaccurate deviation may be due to image noise, image distortion, and different lighting associated with capturing and processing printed surface images. The result of the analysis shows that a good surface quality is achieved if a layer thickness of 0.2 mm, 100% bulk density and 1.0 mm bulk thickness are selected. When optimal parameters were reached, the FDM print was repeated and then the results were validated to ensure the accuracy of the method used. The results obtained in the present study could be proposed in the field of additive manufacturing, which improves the surface quality of the FDM sample.

Prediction of compressive strength in additively fabricated part using long short term memory based neural network

With ability to produce complex geometries and accurate parts, additive manufacturing (AM) could be applied in various sectors including aerospace and biomedical, though the frequent occurrence of defects and unavailability of feasible monitoring and control system prohibits their effective utilization. Technological developments and use of artificial intelligence, machine learning models could be a perfect solution to the problems. In this study, a predictive model is developed using deep learning (DL) algorithm and investigated the compressive strength of fused deposition modelling (FDM) printed part. An improved recurrent neural network (RNN) model called long short-term memory (LSTM) is used for predicting the compressive strength of part produced by FDM process. Model developed with LSTM utilizes layer-wise relevance propagation (LRP) algorithm to establish relation between the FDM part and LSTM layers. Predictive model developed in this study uses the back propagation algorithm to establish the relation between the compressive strength of part and input process variables. A total of 81 poly lactic acid samples were printed and tested, out of which 57 samples were used for training the model and 16 data were used for validating the model and remaining data were used for testing the model. Effects of process variables including infill density, print orientation, raster orientation and layer thickness have been analyzed. The values obtained from the predictive model was in good agreement with experimental results along 1% error. LSTM based DL model predicted the compressive strength closer to the measured values. From the results obtained, the developed model holds good for predicting the compressive strength of FDM printed part.

3D printing of wrist splint using low-temperature thermoplastic (LTTP) based polycaprolactone (PCL) material

ABSTRACT Fused Deposition Modelling (FDM) is a 3D printing process that allows layer-wise fabrication of a component by depositing thermoplastic material from a heated nozzle. The splints fabricated using the FDM technique should be mouldable so that they can easily be fitted on the body part. However, for some of the biomedical devices where remoulding is required, it cannot be fulfilled by the traditional polymer filaments used in the FDM process. Therefore, Low-Temperature Thermoplastic (LTTP) materials, having lower activation temperatures, can be used for such applications. Polycaprolactone (PCL) is a biocompatible LTTP material used in the FDM process that can be easily mouldable. In the present work, a wrist splint is fabricated using PCL filament by the FDM process. A detailed study of the influence of three FDM processing conditions viz. infill density, layer height and raster angle on the compressive properties is carried out to get the optimised values of the FDM processing parameters. The wrist splint has been fabricated using the optimised values of FDM parameters. The 3D printed wrist splint is found to fit comfortably with the user and it provides a suitable fit to immobilise an injured arm.

Parametric optimization for enhancing process sustainability: evidence from fused deposition modeling process

Parametric optimization for enhancing process sustainability: evidence from fused deposition modeling process

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.

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.

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.

An investigation on tensile strength of 3D printed parts of Acrylonitrile butadiene styrene (ABS) material

Fused Deposition Modelling (FDM) components' mechanical properties reflect processing parameters' effectiveness. Tensile Strength’s parameter sequence of a FDM 3D-printed Acrylonitrile butadiene styrene (Abs) material specimen is examined. Design Expert tools were utilized to conduct a variance analysis (ANOVA) to assess tensile strength components and interactions. Four FDM processing parameters—layer thickness, raster angle, infill pattern, and infill width—are examined using the Taguchi DOE. The ASTM D638-compliant specimen measurements were compared to the 3D computer-aided design model. When there are ranges of processing parameters and no linear processing criteria, design of experiments (DOEs) become difficult to determine. Identifying the best processing parameters requires a certain number of specimens. Optimum tensile strength of ABS component was 9.22 MPa at 0.3 mm layer thickness, 60° raster angle, infill width of 0.25 mm.

Surrogate recycling for structures with spatially uncertain stiffness

This study expands the existing methods for non-destructively identifying the spatially varying material properties of a structure using modal data. It continues a recently published approach to this inverse problem that employed Bayesian inference in conjunction with the Karhunen-Loève expansion to solve it. Here, we present two developments. Firstly, eigenvectors are used instead of eigenvalues, improving the results significantly. Secondly, a generalized polynomial chaos surrogate accelerates the inversion procedure. Finally, we develop a methodology for reusing the surrogate model across inversion tasks. We demonstrate the efficacy and efficiency of this methodology via the field of additive manufacturing and the fused deposition modeling process. The good results promise profound computational cost saving potential for large-scale applications.

Design of customized implants and 3D printing of symmetric and asymmetric cranial cavities

A methodology has been developed in this work to design customized cranial implants from computed tomography (CT) scan images for symmetric as well as asymmetric defects. The two-dimensional CT scan images were converted into three-dimensional geometric models using software packages. Two cases of cranial cavities at different locations were considered for implant design using two different approaches. Case 1 is having a symmetric cranial cavity while Case 2 has an asymmetric frontal cranial cavity. The craniums with defects were 3D reconstructed. Customized cranial implants were made for the two cases. In Case 1, symmetry was used to design the cranial implant. Symmetry cannot be used in Case 2. In Case 2, the implant was designed by blending from the surface available adjacent to the missing portion of the cranium. 3D reconstructed bone models and customized implants were 3D printed in poly-lactic acid (PLA) using a fused deposition modeling process for form and fit evaluation. Finite element analysis was performed to compare the mechanical behavior of bone, and the two biomaterials – polyether ether ketone (PEEK), and Ti6Al4V. Static structural finite element analysis was performed to simulate the impact of falling off a bicycle with an impact on the cranial implants in the two cases. The load was modeled as a normal force acting on the surface of the implant. It was found that the stresses in the titanium alloy are comparable to those of PEEK for both the cases. However, the strains and deformation were found to be much smaller compared to those in PEEK. Therefore, the titanium alloy is the material of choice for both the cases among the materials under consideration. The designed implants are solid hence may face the challenge in bone ingrowth. In future studies, the implant can be made porous by incorporating a lattice structure to enhance osseointegration and promote bone ingrowth. Implants for both symmetric and asymmetric defect cases in cranium were successfully designed.

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.

Chemical characterization of particulates and volatile organic compounds emitted during fused deposition modeling

Additive manufacturing (AM) is largely regarded as an environmentally friendly alternative to traditional manufacturing techniques. However, some studies have pointed out that AM may carry its own environmental implications. It has been reported that high concentrations of ultrafine particle emissions as well as volatile and semi volatile organic compound emissions from the fused deposition modeling (FDM) 3D printing process. The health consequences of exposure to these ultrafine particles and organic compounds are unknown. Determining the chemical composition of the ultrafine particles and the volatile organic compounds is the first step toward understanding potential health risks of FDM process. In this study, we collect air emissions from FDM printing using ABS plastic filament for chemical characterization. Using solid phase microextraction (SPME), compounds in samples are adsorbed or absorbed on a fiber coating and analyzed with gas chromatography mass spectrometry (GC–MS). Several compounds are successfully identified in printer emissions and compared with samples collected from filament wash prior to extrusion. The most predominant compound was styrene, followed by various benzene aromatics, nitrogenated aromatics, and siloxanes. Measurements identifies substantial cross over between compounds found from printer emission and filament wash tests, suggesting that some emissions could possibly be reduced, or in some cases, even eliminated by washing 3D printer filament prior to use.