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The Additive Manufacturing (AM) technology initially was developed as a rapid prototyping tool for visualization and validation of designs. The recent development of AM technologies, such as Fused Deposition Modelling (FDM), is driving it from rapid prototyping to rapid manufacturing. However, building end-user functional parts using FDM proved to...
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... the infill patterns control how the nozzle fills and raster across the infill layers. Three infill patterns were used in this study; those are linear, diamond and hexagonal as shown in Figure 3, where Diamond F has the same shape as the Diamond infill pattern, but using faster printing G codes due to the difference in partitioning of the pattern itself. However, this faster printing pattern puts higher loads on the extruder for changing its directions abruptly. ...
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... evaluate the effect of processing parameters over the dimensional accuracy and repeatability all the printed specimens were measured and compared to designed CAD model. In total, the study included 9 measurements for each specimen, which incorporated the overall length (OL) of the specimen, the total width (OW), the thickness (T) and width (W) of the reduced section as shown in Figure 3. The length was measured using a Vernier calliper. ...
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Research included in this article were conducted with a project: ‘Additive technology used in conduction with optical methods for rapid prototyping of 3D printed models’. In this article intellectualized three various 3D printing technologies: Fused Deposition Modelling (FDM), Selective Laser Sintering (SLS) and Material Jetting (PolyJet). Also, th...
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... Studies by Onwubolu and Rayegani [10] and Domingo-Espin et al. [11] indicate that mechanical quality improves with adjustments in design parameters, such as printing orientation and brim and skirt selection. Alafaghani et al. [12] have proposed a Design for Manufacturing (DfM) application for FDM to enhance the mechanical performance of AM parts. To predict the behavior of materials under different loading conditions, it is important to consider that AM products exhibit different characteristics distinct from those of molded or extruded parts made from the same materials. ...
Fused deposition modeling (FDM) is widely utilized in additive manufacturing, with the product quality significantly dependent on the strand cross-section geometry. This paper investigates how process parameters affect strand morphology in FDM-printed parts. This is achieved by monitoring the nozzle's position throughout the printing process and analyzing the cross-sectional dimensions of sample strands produced under varying conditions through microscopy. The study is conducted in two phases: In the first phase, real-time data on nozzle positioning is collected, and in the second phase, cross-sectional dimensions of the printed samples are measured through the examination of 150 microscope images from 15 samples. An ANOVA analysis is then applied to establish a relationship between the selected process parameters and the dimensions of the products. The findings reveal that the nozzle height plays a pivotal role in determining the height of a strand, and along with nozzle temperature and travel speed, influences the width and adhesion length of the strand. These results provide a foundation for developing a closed-loop control system aimed at enhancing the quality of products produced by 3D printers. Additionally, the novel method for real-time nozzle position measurement introduced in this study contributes to further research on dimensional accuracy and mechanical performance in additive manufacturing products.
... This is due to the increase in gaps between the layers, which leads to more defects in the component and ultimately results in premature failure. In contrast, Alafaghani et al. [13] and Yang et al. [10] did not observe this trend. Ladder showed that an improvement in mechanical properties is obtained with increasing layer thickness. ...
... With these conditions considered, the test plan was designed to investigate all parameters with the minimum number of samples. The test speed is set to 2 mm/min, based on similar studies (1-5 mm/min) [8,13,16,17]. Three specimens are produced and tested for each design variant (ID). ...
Fused Deposition Modeling (FDM) is a well-established manufacturing method for producing both prototype and functional components. This study investigates the mechanical properties of FDM components by material and process-related influencing variables. Tensile tests were conducted on seven different materials in their raw filament form, two of which were fiber-reinforced, to analyze their material-related influence. To cover a wide range from standard to advanced materials relevant for load-carrying components as well as their respective variations, polylactic acid (PLA), 30% wood-fiber-reinforced PLA, acrylonitrile butadiene styrene (ABS), polycarbonate (PC), a blend of ABS and PC, Nylon, and 30% glass-fiber-reinforced Nylon were selected. The process-related influencing variables were studied using the following process parameters: layer thickness, nozzle diameter, build orientation, nozzle temperature, infill density and pattern, and raster angle. The first test series revealed that the addition of wood fibers significantly worsened the mechanical behavior of PLA due to the lack of fiber bonding to the matrix and significant pore formation. The polymer blend of ABS and PC only showed improvements in stiffness. Significant strength and stiffness improvements were found by embedding glass fibers in Nylon, despite partially poor fiber–matrix bonding. The materials with the best properties were selected for the process parameter analysis. When examining the impact of layer thickness on part strength, a clear correlation was evident. Smaller layer thicknesses resulted in higher strength, while stiffness did not appear to be affected. Conversely, larger nozzle diameters and lower nozzle temperatures only positively impacted stiffness, with little effect on strength. The part orientation did alter the fracture behavior of the test specimens. Although an on-edge orientation resulted in higher stiffness, it failed at lower stresses. Higher infill densities and infill patterns aligned with the load direction led to the best mechanical results. The raster angle had a significant impact on the behavior of the printed bodies. An alternating raster angle resulted in lower strengths and stiffness compared to a unidirectional raster angle. However, it also caused significant stretching due to the rotation of the beads.
... It was revealed that a raster orientation of 0 • was the best for maximizing tensile strength. Alafaghani et al. [9] studied the effect of build orientation, infill density, infill patterns, print speed, extrusion temperatures, and layer thickness. They found that build orientation, layer thickness, infill density, and extrusion temperature were all significant for tensile properties (Young's modulus, tensile strength, and yield strength) among the six parameters studied. ...
Fused Deposition Modelling (FDM) is one of the layer-based technologies that fall under the umbrella term “Additive Manufacturing”, where the desired part is created through the successive layer-by-layer addition process with high accuracy using computer-aided design data. Additive manufacturing technology, or as it is commonly known, 3D (three-dimensional) printing, is a rapidly growing sector of manufacturing that is incorporated in automotive, aerospace, biomedical, and many other fields. This work explores the impact of the Additive Manufacturing process on the mechanical proprieties of the fabricated part. To conduct this study, the 3D printed tensile specimens are designed according to the ASTM D638 standards and printed from a digital template file using the FDM 3D printer Raise3D N2. The material chosen for this 3D printing parameter optimization is Polylactic acid (PLA). The FDM process parameters that were studied in this work are the infill pattern, the infill density, and the infill cell orientation. These factors’ effects on the tensile behavior of printed parts were analyzed by the design of experiments method, using the statistical software MINITAB2020.
... Additive manufacturing (AM), also called "3D printing", is a production method that builds up layers of material to make a three-dimensional object. In contrast to subtractive manufacturing procedures like machining, which removes material to form an object, AM adds material to create the thing [1]. It is used in many fields, including engineering, aerospace, automobile industry, and medical [2,3]. ...
... For PLA specimens subjected to tensile testing, ultimate tensile strength decreases as the layer height increases [39]. In addition, Alafaghani suggested that an increase in layer thickness would increase the mechanical properties due to the need for fewer layers [1]. This shows that the printer type, model, or parameter level can have different effects on the printing parameters. ...
Fused deposition modeling (FDM), a prominent AM technique, involves the layer-by-layer deposition of material to construct objects. Various design and production process parameters influence the mechanical properties of FDM-printed components. This study aims to analyze and compare the impacts of printing parameters, specifically nozzle diameter, layer height, and printing speed, on the tensile and flexural properties of 3D-printed parts utilizing two distinct 3D printers. Polylactic acid (PLA) filament material was employed, and the printing parameters were determined based on prior research findings on mechanical properties. The temperature distribution of the printers was analyzed using a thermal camera during the production process. The Taguchi method was applied to ascertain the optimal parameter levels, and subsequently, an analysis of variance (ANOVA) was executed to evaluate the significance of each parameter. Tensile and flexural tests were conducted on the printed samples, followed by an in-depth analysis of the results. The printer's structure has little effect on temperature distribution, but its impact on sample strength is uncertain. The results revealed that the printing parameters influenced the mechanical properties of the printed parts in different ways in the two unique printers. Printing speed was the most influential parameter for the Ender 3 V2, while layer height had the highest impact on the Ultimaker 2 + . The nozzle diameter also played a significant role in both printers. Visual analysis of the printed samples showed the printing parameters' effects on the printed lines' bonding and quality. This study provides insights into the effects of nozzle diameter, layer height, and printing speed on the tensile and flexural properties of the printed samples. The results contribute to understanding how different printers may require specific parameter settings to achieve optimal performance. Further research is recommended to explore additional parameters and materials, considering particular applications and their requirements.
... 7 Besides, some manufacturing parameters involved in the FDM technique play a crucial role in controlling the mechanical performance and quality of the final fabricated part. Printing speed, 8 layer orientation, in-plane raster angle, 9 nozzle diameter, nozzle and bed temperatures, 10 infill density, 11 and layer height 12 are the main manufacturing parameters of the FDM process that can be varied for different materials. Exploring the effects of the mentioned parameters on the mechanical performance of the FDM parts is a challenging issue; thus, many researchers have tried to answer the question: how do these parameters affect the mechanical behavior of the printed parts? ...
Fused deposition modeling (FDM) is a material extrusion technique that works by extruding molten filament on a heated platform. In this method, several manufacturing parameters are engaged which affect the mechanical performance of printed parts. In this study, the effects of nozzle diameter on tensile (under two loading rates) and mixed‐mode I/II fracture behavior of printed specimens were examined. The obtained results revealed that testing speed had a minor effect on basic mechanical properties. For instance, ultimate tensile strengths of tensile specimens printed with 1 mm nozzle diameter were 32.56 and 39.71 MPa for test speeds of 1 and 5 mm/min, respectively. Higher nozzle diameters resulted in higher mechanical properties and fracture resistance (e.g., specimens printed with 45/−45° raster angle with 0.4 and 1 mm nozzle diameters indicated fracture loads of 4520 and 5071 N, respectively). Besides, fracture loads were predicted through the equivalent material concept coupled with J‐integral method.
... The results showed that a high infill percentage level produced parts with fewer voids and higher hardness values. Optimizing the processing factors has also been used for dynamic mechanical performance [12], surface roughness [18][19][20], build time [19], yield and shear strength [21], elasticity [22], dimensional accuracy [23,24], and ductility [23]. ...
... The results showed that a high infill percentage level produced parts with fewer voids and higher hardness values. Optimizing the processing factors has also been used for dynamic mechanical performance [12], surface roughness [18][19][20], build time [19], yield and shear strength [21], elasticity [22], dimensional accuracy [23,24], and ductility [23]. ...
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.
... Some previous researchers focused on optimizing process parameters via the Design of DoE [74][75][76] and proposed compensation models [77] to avoid quality issues during the AM process. It was argued that the optimal parameter combinations obtained by offline quality control techniques could improve the part quality. ...
Additive Manufacturing (AM) has emerged as a promising technology in recent years. However, the complexity of the fabrication process poses significant challenges in ensuring the quality and consistency of AM parts. To address these challenges, this dissertation proposes the utilization of sensors for real-time monitoring and control of the fabrication process, enabling the achievement of quality requirements for a repeatable and reliable process. Given the vast amount of information that can be obtained from embedded sensors, it is crucial to employ data analytics and predictive models for efficient processing of sensory data. The proposed solutions encompass effective methodologies to enhance part quality and machine reliability by integrating sensory data analysis with AI/ML models and closed-loop control systems.
In this study, the part quality attributes are identified by dimensional errors, layer-wise surface anomalies, and abnormal machine vibrations. In addition, the cost of quality and machine reliability are included to provide a maintenance strategy for the AM machine to assure the required quality output. Different methods are developed and investigated for their utility and effectiveness in solving each of these quality problems. Initially, the dimensional accuracy problem is studied using three different analytical models which are compared to compensate for the dimensional deviation resulting from the Fused Filament Fabrication (FFF) process. Secondly, layer-wise surface anomalies in FFF are investigated using real-time quality monitoring and control methods. An online sensor-based monitoring system for FFF is developed to identify layer-wise surface anomalies using a Convolutional Neural Network (CNN) model. Based on the online monitoring results, feedback controllers are designed to improve dimensional accuracy and layer-wise surface quality measures. To study abnormal machine vibrations, a data analytic approach based on a deep neural network is developed. To improve the quality of the AM process, machine maintenance and reliability are critical factors. In this study, a multi-objective optimization model is developed to consider both maintenance cost and machine reliability criteria for improved part quality.
Further, an online monitoring system is developed for the metal Direct Energy Deposition (DED) system. For this system, an image-based gaussian process model is developed to accurately predict the metal melt pool morphological features during the fabrication process. The proposed methodologies in this research offer a comprehensive system and associated models to address the challenges of quality assurance in advanced additive manufacturing and have the potential for wider applications in other manufacturing processes as well as the automation industry.
... At the same time, it is crucial to investigate how different processing temperatures affect the specific material used for printing. [11][12][13] Another important factor to analyze is the filament diameter. The estimated size of the printed details, the printing accuracy and the properties of the final product are directly linked to the filament diameter. ...
... However, Li et al. [19], Li et al. [20], Rodríguez-Panes et al. [24], and Liu et al. [29] observed an inversely proportional relationship between layer thickness and tensile strength-higher layer thickness resulted in reduced tensile strength. On the other hand, some researchers [32][33][34][35] concluded that the increase in layer thickness enhances the tensile strength. ...
... Nevertheless, studies [16,19,24,32,33] investigating the link between the tensile strength of a part and its infill density have consistently found comparable outcomes. The findings indicate that an increase in infill density enhances the mechanical strength of the printed parts. ...
... In essence, a higher infill density leads to a stronger and more robust part because the material structure inside is more continuous and less prone to weaknesses that can arise from gaps or voids. These findings align with prior researches [16,17,20,24,32,33]. The ANOVA analysis indicates that infill density is the most critical factor affecting tensile strength, accounting for 73.14% of the variation in tensile strength. ...
Fused Deposition Modeling (FDM) is an additive manufacturing (AM) technique based on the principle of forced extrusion. It is the most commonly used 3D printing processes, subjected to its ease of utilization. With the increase in product customizations, the use of 3D printing technique for manufacturing of the end-use product is on the rise. Therefore, the strength and other mechanical properties of the 3D-printed finished component are of great importance. These mechanical properties of an FDM-produced part are greatly affected by the selection of different values for printing parameters. Due to operational simplicity and low cost, FDM is widely researched, and a number of scholars have examined the effects of varying the values of parameters on the mechanical properties of the FDM-printed specimens. Where tensile strength of the 3D-printed parts is the mostly studied property among all mechanical properties. However, the effect of changing values of parameters on the tensile strength in relation to build time is least researched. The objective of this research is not only to examine the influence of printing parameters such as layer thickness, print angle, and infill density on the tensile strength of the 3D-printed components and optimize them but also to achieve the desired strength in a faster and timely manner. In this study, tensile test specimens were printed and tested according to ISO-527–2 standards. Analysis of variance (ANOVA) is also performed to check the significance of print parameters. The results suggested that an increase in layer thickness has an inverse impact on the tensile strength, whereas an increase in print angle and infill density has a direct impact on the tensile properties of the FDM produced specimens. Furthermore, the print time is reduced with an increase in layer thickness and a decrease in infill density, as both lead to fewer passes required to print the part. However, print time has variable relationship with the print angle, with the least value at a 90° print angle and the maximum value at a 15° print angle.
... The physics of FFF printing are well understood in the literature [17,18] and it is likely possible to develop full-scale models of the FFF process that could relate material models directly to machine models in order to pick optimal slicer configurations. However, to our knowledge no-one has made substantial effort to apply these models to automatically select parameters for FFF machines, although much work has been done to evaluate the effects of parameter selection on the quality of printed outputs [19][20][21][22][23][24]. The focus in this work is on how to rapidly select operating parameters from a short, online rheological experiment. ...
To describe a new method for the automatic generation of process parameters for fused filament fabrication (FFF) across varying machines and materials. We use an instrumented extruder to fit a function that maps nozzle pressures across varying flow rates and temperatures for a given machine and material configuration. We then develop a method to extract real parameters for flow rate and temperature using relative pressures and temperature offsets. Our method allows us to successfully find process parameters, using one set of input parameters, across all of the machine and material configurations that we tested, even in materials that we had never printed before. Rather than using direct parameters in FFF printing, which is time-consuming to tune and modify, it is possible to deploy machine-generated data that captures the fundamental phenomenology of FFF to automatically select parameters.
