Fused Deposition Modeling Parts Research Articles

Enhanced Interlayer Adhesion of 3D Printed Poly(lactide acid) with Ultralow Hyperbranched Poly(ester imide)s

The layer-by-layer process of fused deposition modeling (FDM) is well-known for introducing anisotropy in 3D printed parts due to weak interlayer adhesion. Moreover, the existing physical blend modification methods still face the side effects caused by the heavy use of additives, such as high cost, poor biocompatibility and weakening of primary properties. In this study, hyperbranched polymers poly(ester imide)s (HBPEIs) were blended into the poly(lactic acid) (PLA) matrix to enhance the interlayer adhesion of 3D printed parts. The application effect of HBPEIs in different processing modes was discussed. The effect of different amounts of HBPEIs on the mechanical properties of PLA specimens was investigated. Results showed that by adding just 0.1 wt% HBPEIs, the maximum tensile strength for samples printed with a 90° raster orientation increased from 23.1 MPa to 46.6 MPa while maintaining an isotropic behavior at around 95.9 %. Microstructures, rheological behaviors, and thermal properties of the printed specimens were characterized and compared to understand the underlying enhancement mechanism. It suggested that HBPEIs significantly improved interlayer adhesion by promoting chain diffusion and cold-crystallization between layers. This study showed that using ultralow additives can significantly enhance the interlayer adhesion in FDM parts.

Various FDM Mechanisms Used in the Fabrication of Continuous-Fiber Reinforced Composites: A Review.

Fused Deposition Modeling (FDM) is an additive manufacturing technology that has emerged as a promising technique for fabricating 3D printed polymers. It has gained attention recently due to its ease of use, efficiency, low cost, and safety. However, 3D-printed FDM components lack sufficient strength compared to those made using conventional manufacturing methods. This low strength can be mainly attributed to high porosity and low sinterability of layers and then to the characteristics of the polymer used in the FDM process or the FDM process itself. Regarding polymer characteristics, there are two main types of reinforcing fibers: discontinuous (short) and continuous. Continuous-fiber reinforced composites are becoming popular in various industries due to their excellent mechanical properties. Since continuous reinforcing fibers have a more positive effect on increasing the strength of printed parts, this article focuses primarily on continuous long fibers. In addition to polymer characteristics, different mechanisms have been developed and introduced to address the issue of insufficient strength in 3D-printed FDM parts. This article comprehensively explains two main FDM mechanisms: in-situ fusion and ex-situ prepreg. It also provides relevant examples of these mechanisms using different reinforcing elements. Additionally, some other less frequently utilized mechanisms are discussed. Each mechanism has its own advantages and disadvantages, indicating that further development and modification are needed to increase the strength of 3D-printed FDM parts to be comparable to those produced using traditional methods.

Process parametric optimization of fused deposition modeling for manufacturing of acrylonitrile butadiene styrene parts

Additive manufacturing has been a revolution in the last decade and has led to a number of product innovations. Rapid prototyping (RP) has significantly reduced the product development cycle time and RP techniques are now replacing many conventional plastic processing techniques where production volume is not an issue. In order to ascertain the utility of additive manufacturing techniques for the development of fully functional parts, it’s important to establish the optimal set of process parameters for achieving maximum mechanical performance. In the current experimental investigation, the dogbone-shaped parts of acrylonitrile butadiene styrene (ABS) were fabricated by the fused deposition modeling (FDM) process. The process parameters were optimized for dogbone-shaped specimens fabricated by the FDM process. Among the parameters, it has been found that orientation and infill density are the most dominant factors affecting the tensile strength of FDM parts printed with ABS material.

The influence of contour on FDM parts for the Shear Test

Additive manufacturing (AM) is a process that can achieve many complexities in the production of parts. The most important aspect of AM is that there is no more material wastage, so costs are reduced. Additive manufacturing is starting to be used in many fields, including medicine, automotive, aircraft, etc. The method of adding layer by layer is used for a wide range of materials: plastics, metals, and ceramics.This paper investigated the influence of nutshells on shear specimens. To obtain the specimens Prusa 3D printer and PETG filament were used. The specimens were built using Fused Deposition Modeling (FDM) technology. The shear properties were investigated in-plane, where the force was measured with a load cell, and the displacement was measured with Dantec Q400 digital image correlation. The difference between nutshell and without nutshell specimens in the shear strength was investigated. It has been seen that the specimens behave differently when the nutshell exists or not. On the other hand, digital image correlation (DIC) was used to calibrate the strain that appears on the fracture.

Effects of FDM 3D printing parameters on PLA biomaterial components dimensional accuracy and surface quality

Since precise manufacturing is very important in different industries, the produced part surface quality and dimensional accuracy have been brought to attention in recent years. In this regard, in Fused Deposition Modeling (FDM) as a prototyping method, controlling the process parameters in order to customize the produced part has been a value-added ability which should be considered. Optimizing the process parameters helps to enhance the mentioned products’ properties. In this paper, influential process parameters, including print speed (PS), infill percentage (IP), and nozzle temperature (NT) were selected to examine. That was concluded NT increase from 195°C to 240°C, IP increase from 10% to 50% and PS decrease from 150 to 50 mm/min enhance dimensional accuracy including inner and outer ovality up to 30%, 53%, and 50%, respectively. PS is the most significant parameter on inner and outer ovality by 43.92% and 66.60% contribution, respectively. The density of the FDM part was affected mostly by the IP by 92.94% contribution which leads to the printed part density increment up to 13%. Other parameters have small effect on the density value. According to desirability method, that was resulted NT of 240°C, IP of 50%, and PS of 54 mm/min are the optimum parameters. Besides, verification tests were carried out and a maximum error of 7.5% was reported, which shows optimization confidence. In the following, part geometry influence on the final part surface roughness and dimensional accuracy was examined. The experiments demonstrated that cube geometry has the least surface roughness of 20 µm and dimensional error of 0.17 mm due to the independent motion of the axes. Also cone geometry has the highest dimensional error of 0.32 mm and surface roughness of 51 µm which was due to simultaneous axes motion and lateral face lower than 90°.

Experimental investigation on fatigue life and tensile strength of carbon fiber-reinforced PLA composites based on fused deposition modeling

Fused deposition modeling (FDM) is a widely used additive manufacturing (AM) method that offers great flexibility in fabricating complex geometries without requiring expensive equipment. However, compared to other manufacturing methods, FDM-produced parts generally exhibit lower strength and fatigue life. To overcome this limitation, researchers have explored the use of fibers and reinforcements to enhance the mechanical properties of FDM parts. Nevertheless, the performance of FDM-produced parts can be significantly affected by various manufacturing parameters, including infill density, which is a key factor in balancing time and cost. In this study, the tensile strength and fatigue life of carbon fiber-reinforced polylactic acid (PLA) composites produced by FDM were investigated by varying the infill density (50 and 75%) and raster angle (0°, 45°, and 90°). The effects of 100% filling density, raster width, and nozzle diameter on mechanical properties were also examined. The experimental results demonstrated that increasing the infill density and decreasing the raster angle can enhance the tensile strength, although the fatigue behavior was found to be more complex and dependent on the infill density. The optimal parameters for producing FDM parts with improved mechanical properties were identified based on the analysis of the tensile strength and fatigue life data. This research has yielded significant findings concerning the diverse fatigue behavior associated with the raster angle at different infill densities. Specifically, noteworthy observations reveal that a raster angle of 45 degrees at 50% infill density, and a raster angle of 0 degrees at 75% infill density, exhibited the most prolonged fatigue life. This outcome can be ascribed to the specific loading conditions and the inherent strength of the sediment layer at the critical point of stress concentration.

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.

Predictive modeling of mechanical properties for fused deposition modeling parts: A focus on processing and environmental parameters

Fused Deposition Modelling (FDM) is a widely used additive manufacturing technique for prototyping and manufacturing functional parts due to its cost-effectiveness and low waste generation. The mechanical properties of FDM parts depend on multiple processing parameters and environmental conditions. Due to the complex physical phenomena of FDM, it is a challenge to accurately determine part properties. These properties are generally determined after performing experimental investigation which is time consuming and expensive. Alternatively, predictive models developed for determining part properties have proved to be a valuable tool. However, these models are typically developed using data from a single printer and do not consider environmental parameters as an input factor. To address these shortcomings, a data-driven multi-step scalable modeling technique that provide the ability to gather data from multiple sources is presented. This technique is used to develop models for predicting mechanical properties using experimental data from multiple sources resulting in models that are robust compared to models developed using data from one printer. Additionally, one environmental parameter has been introduced as the input factor. Regression and Artificial Neural Network models are developed to predict mechanical properties using experimental data from two printers. Five processing and environmental parameters namely, extrusion temperature, layer thickness, print bed temperature, print speed and humidity are used as the input factors. These models predict the toughness and tensile strength of the parts. The experimental values from the two printers are compared to the predicted values to demonstrate the superior predictive capabilities of the models. Finally, to demonstrate the scalability and utility of the proposed technique, data from one more printer is introduced and is used to develop regression models.

Experimental study of the fracture of CT specimens printed in PLA as a function of the raster width

The FDM (Fused Deposition Modelling) additive manufacturing process is characterised by a large number of process variables that determine the mechanical properties and quality of the manufactured parts. When printing layer by layer, the filaments constituting the layer are welded on the one hand between them in the same layer and on the other hand between the superimposed layers, this welding develops on the contact surfaces (raster width) along the deposited filaments. The quality of this welding determines the resistance to crack propagation between filaments and between layers. This article aims to study the effect of the width of the raster on the resistance to crack propagation in a structure obtained by FDM.We have developed an experimental approach from CT specimens to determine the tensile strength of polylactic acid (PLA) polymers, considering the J-Integral method. And given the complexity of the problem, three cases of raster width (l=0.42 mm, l=0.56 mm and l=0.68 mm) have been treated.According to the results obtained (J, ∆a), the resistance to crack propagation in the parts printed by FDM seems to be better when the width of the filament is small. Indeed, the energy necessary to break the specimen is relatively greater than in the case of a larger width. This finding was confirmed by comparing the values of J for a given advancement of the crack for the three cases studied.In order to present an exhaustive study, we focused on the effect of raster widths (including 0.42 mm, 0.56 mm to 0.68 mm) on the crack propagation of printed PLA. This study is in progress for other printing parameters. To highlight the cracking mechanisms, microscopic observations will be developed in greater depth at the SEM.Our analysis can be used as decision support in the design of FDM parts. In effect, we can choose the raster width that would provide the resistance to crack propagation desired for a functional part.In this article, we analysed the damage mechanism of CT specimens printed by FDM. This subject represents a new direction for many lines of research. For our study, we used the J-Integral theoretical approach to study the fracture behaviour of these parts by determining the resistance curves (J-∆a).

Yielding and post-yielding behavior of FDM parts under compression stress at different strain rates

AbstractThe present study investigates the compression behavior of components made by material extrusion, also known as fused filament fabrication (FFF) or fused deposition modeling (FDM). An experimental plan was conducted by adopting a high-density fulfillment and varying the material flow. Additional tests were performed by thermomechanical compaction to produce full-density samples. Compression tests were performed at various strain rates ranging between 5 × 10−4 and 5 × 10−1 s−1. Yielding and post-yielding behaviors were analyzed. Morphological analysis was carried out to determine the mesostructural features (interlayer neck and void sizes) and how they behave during the compression test. The results indicated that the principal dimension of the voids ranged between 65 mm and 170 mm depending on the adopted value of the extrusion multiplier. On the other hand, thermomechanical compaction enabled the restriction of the voids of printed samples to 10 mm. The cross-sectioning of samples at different strains indicated the formation of shear banding strain localization. In addition, printed samples behaved like porous media during the compression tests and showed different characteristic regions with different void dimensions. The samples printed at the higher material extrusion showed similar behavior to compacted samples. Post-yielding analysis indicated that strain softening observed on compacted samples was more severe as compared to that observed on printed samples. This behavior is dramatically reduced by decreasing the extrusion multiplier.

A Method for Predicting Surface Finish of Polylactic Acid Parts Printed Using Fused Deposition Modeling

Accurately predicting the surface finish of fused deposition modeling (FDM) parts is an important task for the engineering application of FDM technology. So far, many prediction models have been proposed by establishing a mapping relationship between printing parameters and surface roughness. Each model can work well in its specific context; however, existing prediction models cannot meet the requirements of multi-factor and multi-category prediction of surface finish and cope with imbalanced data. Aiming at these issues, a prediction method based on a combination of the adaptive particle swarm optimization and K-nearest neighbor (APSO-KNN) algorithms is proposed in this paper. Seven input variables, including nozzle diameter, layer thickness, number of perimeters, flow rate, print speed, nozzle temperature, and build orientation, are considered. The printing values of each specimen are determined using an L27 Taguchi experimental design. A total of 27 specimens are printed and experimental data for the 27 specimens are used for model training and validation. The results indicate that the proposed method can achieve a minimum classification error of 0.01 after two iterations, with a maximum accuracy of 99.0%, and high model training efficiency. It can meet the requirements of predicting surface finish for FDM parts with multiple factors and categories and can handle imbalanced data. In addition, the high accuracy demonstrates the potential of this method for predicting surface finish, and its application in actual industrial manufacturing.

Geometric void-multiscale model for evaluating the effect of bead width and layer height on voids in FDM parts

PurposeThis paper aims to present a geometrical void model in conjunction with a multiscale method to evaluate the effect of interraster distance, bead (raster) width and layer height, on the voids concentration (volume) and subsequently calculate the final mechanical properties of the fused deposition modeling parts at constant infill.Design/methodology/approachA geometric model of the voids inside the representative volume element (RVE) is combined with a two-scale asymptotic homogenization method. The RVEs are subjected to periodic boundary conditions solved by finite element (FE) to calculate the effective mechanical properties of the corresponding RVEs. The results are validated with literature and experiments.FindingsBead width from 0.2 to 0.3 mm, reported a decrease of 25% and 24% void volume for a constant layer height (0.1 and 0.2 mm – 75% infill). It is reported that the void’s volume increased up to 14%, 32% and 36% for 75%, 50% and 25% infill by varying layer height (0.1–0.2  and 0.3 mm), respectively. For elastic modulus, 14%, 9% and 10% increase is reported when the void’s volume is decreased from 0.3 to 0.1 mm at a constant 75% infill density. The bead width and layer height have an inverse effect on voids volume.Originality/valueThis work brings values: a multiscale-geometric model capable of predicting the voids controllability by varying interraster distance, layer height and bead width. The idealized RVE generation slicer software and Solidworks save time and cost (<10 min, $0). The proposed model can effectively compute the mechanical properties together with the voids analysis.

A COMPARATIVE STUDY ON PRECISION METROLOGY SYSTEMS FOR ADDITIVE MANUFACTURING

This paper presents a comparative study on precision metrology systems such as Coordinate Measuring Machine (CMM), 3-Dimensional Scanning (3DS) and Computed Tomography (CT) for polymer additive manufacturing. A special test sample was designed and manufactured by Fused Deposition Modeling (FDM) and Selective Laser Sintering (SLS) AM systems. The manufactured parts were then measured by three different precision metrology systems and the results were compared in terms of different measurement and AM methods. Uncertainty analyses were conducted based on the results of CMM measurements. The benchmark highlighted the difference between part characteristics manufactured by FDM and SLS, where FDM part represented higher surface roughness and more deviation to the nominal design. Furthermore, expanded uncertainties computed for the FDM manufactured part were almost three times of the uncertainties computed for the SLS manufactured part. It was also demonstrated that one of the major contributors to the expanded uncertainty occurred because of rougher surface of FDM manufactured part. Similar tendency of part to nominal deviations were observable in all metrology systems including CMM, CT and 3DS. Findings of the study revealed the need of standardized measurement for inspection and control of AM parts.

Improving surface smoothness in FDM parts through ironing post-processing

Additive manufacturing using fused deposition modelling (FDM) is an affordable and efficient way to produce functional parts and prototypes. However, the FDM-manufactured parts, particularly those made of polylactic acid (PLA), has some drawbacks regarding accuracy and surface finish. To address this, post-processing techniques such as ironing can be applied. However, little is known about the effect of ironing process parameters on surface roughness, and there is a lack of comparative studies on contact and non-contact measurement methods. This study aims to address these gaps by investigating the impact of path speed, ironing spacing, and flow rate on the surface roughness of FDM-produced PLA samples using both contact and non-contact measurement methods. The results show that the ironing process can lead to a significant reduction in surface roughness values, and careful optimization of process parameters can further minimize roughness. The study highlights the advantages and limitations of contact and non-contact methods for surface roughness analysis and provides guidance on selecting the appropriate method for a given application and material.

Effect of post-process treatments on mechanical properties and surface characteristics of 3D printed short glass fiber reinforced PLA/TPU using the FDM process

The extrusion-based fused deposition modeling (FDM) has become the most frequently used additive manufacturing method because of its shorten production time and cost. However, low surface quality remains a limitation to meet the regulatory standards set by traditional manufacturing, which requires post-processing to improve it. In this work, annealing and acetone vapor treatments are applied to short glass fiber reinforced PLA/TPU composites parts obtained by FDM, respectively. Their effects on the surface finishing, dimensional accuracy and mechanical properties of FDM parts using different post-processing methods are compared. The result shows that the annealing treatment dramatically improves the surface finish, but can reduce the dimensional accuracy and ductility of the FDM parts. Better ductility and dimensional accuracy but lower tensile strength are obtained by using acetone vapor treatment.

Influence of filament angle orientation on the dynamic characteristics of additively-manufactured beams

Three-dimensional printing provides design flexibility, resulting in shorter product development lifetime as prototypes can be rapidly produced. The most common commercially adopted method for 3D printing is Fused Deposition Modeling (FDM). While the advantages for FDM are apparent, a variation of mechanical, thermal, and dynamic properties arises in FDM components. To gain deeper insight into the dynamical properties of FDM parts, this study investigates a cantilever beam manufactured using a commercially available filament and printer. All printing parameters are kept constant, aside from varying the filament angle, measured from the major axis of the beam. Four cases of varying filament angle orientation are considered to quantify the dynamic response of the systems. Further, duplicates of each beam are manufactured and compared. Experimental results are conducted and analyzed to characterize the dynamical responses of the beams. The free vibration results confirm uncertainty of FDM parts by uncovering inconsistent dynamic properties between identically printed beams. The results of the pseudo-random vibration excitation indicate that varying responses in all system’s axes are observed for varying filament angle orientations. It is also demonstrated that softening nonlinearity for the first mode of vibration is activated for all additively-manufactured beams which is not consistent with typical isotropic responses. Additionally, an explanation on the interaction between the geometric, inertial, and other present nonlinearities is explored and discussed. The outcome of this work shows the strong dependence of additively-manufactured systems to the orientation of the filament angle and their dynamical responses and hence, the need for vibratory characterization for each sample unlike conventional systems.

Mechanical characterization of FDM parts through instrumented flat indentation

Evaluating local mechanical properties of parts made by additive manufacturing processes can improve the deposition conditions. This study proposes a non-destructive characterization test to determine the mechanical behavior of fused deposition modeling (FDM) components. Indentation and compression tests were conducted on samples produced by the FDM process, which were created by varying the material flow during the deposition. An empirical relationship was determined between yield strength determined through compression and indentation tests. R2 = 0.92 characterized the correlation between the compression and indentation test. The results indicated that both the yield strength measured through compression tests and that measured by the indentation tests increased linearly with the density of the components. Indentation tests provided more insights concerning the tested surface’s local characteristics than the compression test.

FDM-based additive manufacturing of recycled thermoplastics and associated composites.

Hailed since the fourth industrial revolution, three-dimensional (3D) printing or additive manufacturing (AM) has been extensively implemented in various manufacturing sectors. This process is popular for generating regular products and incorporating innovative designs into the components like auxetic structures, such as fabrication of engineering products, customized implants and sophisticated biomedical devices. Over the years, one of the interesting outputs of this emerging technology is the reuse of waste thermoplastic materials to produce competent products through the fused deposition modeling (FDM) technique. The strength of FDM components produced from thermoplastic waste is lower than that of virgin plastic FDM counterparts. So, there is a need to understand the significant changes in the recycled thermoplastic material during subsequent extrusions, which are chain scission, change in viscosity and breaking strength. The use of additives has been a promising solution to improve the performance of recycled material for 3D printing applications. Hence, this study aims to provide an overview of reusing plastic waste through FDM-based 3D printing. This review summarizes the current knowledge about the effect of processing on thermo-mechanical properties of recycled plastic FDM parts and the use of various additives to improve the overall quality. In addition, two case studies from open literature have been demonstrated to explain the use of FDM and associated technology for plastic recycling.

Numerical Modeling Based on Finite Element Analysis of 3D-Printed Wood-Polylactic Acid Composites: A Comparison with Experimental Data

Wood-polymer composites are increasingly produced through fused deposition modeling (FDM)—an additive manufacturing technique. The versatility of this technology has attracted several industries to print complex shapes and structures. This underscores the importance of studying the mechanical properties of the FDM parts, specifically, their elastic properties. A numerical homogenization methodology is introduced in the present study, focusing on the fundamental aspect of the elastic properties. Investigations were carried out on the influence of various parameters like wood volume fraction, aspect ratio, and internal porosity. The numerical results were validated using analytical models and experimental data. The comparison showed a satisfactory agreement with experimental data, where the relative error did not exceed 10%, leading to a strong conclusion about the validity and effectiveness of the proposed approach.

Rapid prediction and optimization of the impact of printing parameters on the residual stress of FDM-ABS parts using L27 orthogonal array design and FEA

Fused Deposition Modelling (FDM) is now one of the most widely used techniques for building complicated 3D prototypes straightforwardly from an STL model. In this process, the part is fabricated as a layer-upon-layer deposition of a filamentwire. This deposition technique has numerous benefits; however, it causes rapid heating and cooling cycles of the filamentmaterial, which introduces residual stresses in the component during the building-up process. As a result of residual stresses, warping and distortions of the componentsduring the print process are common concerns in FDM parts. In the FDM process, there are many printing parameters that affect the mechanical properties of printed parts, such as printing speed, layer thickness, printing temperature, printing orientation, and infill density. Experimentally investigating these parameters is costly and time-consuming. This work employs a numerical solution to find the correlation between the residual stress and three printing parameters: printing orientation, raster angle, and infill pattern. The results showed that the printing orientation has the most significant influence on the residual stress value. The raster angle was the second important parameter, while the infill pattern did not influence the residual stress.