Search results

Additive manufacturing (AM) is growing economically because of its cost-effective design flexibility. However, it faces challenges such as interlaminar weaknesses and reduced strength because of product anisotropy. Therefore, the purpose of this study is to develop a methodology that integrates design for additive manufacturing (AM) principles with fused filament fabrication (FFF) to address these challenges, thereby enhancing product reliability and strength.

Developed through case analysis and literature review, this methodology focuses on design methodology for AM (DFAM) principles applied to FFF for high mechanical performance applications. A DFAM database is constructed to identify common requirements and establish design rules, validated through a case study.

Existing DFAM approaches often lack failure theory integration, especially in FFF, emphasizing mechanical characterizations over predictive failure analysis in functional parts. This methodology addresses this gap by enhancing product reliability through failure prediction in high-performance FFF applications.

While some DFAM methods exist for high-performance FFF, they are often specific cases. Existing DFAM methodologies typically apply broadly across AM processes without a specific focus on failure theories in functional parts. This methodology integrates FFF with a failure theory approach to strengthen product reliability in high-performance applications.

Additive manufacturing (AM) is a layer-by-layer technique that helps to create physical objects from a three-dimensional data set. Fused deposition modeling is a widely used material extrusion (MEX)-based AM technique that melts thermoplastic filaments and selectively deposits them over a build platform. Despite its simplicity and affordability, it suffers from various printing defects, with partial warping being a prevalent issue. Warpage is a physical deformation caused by thermal strain incompatibility that results in the bending of the printed part away from the build platform. This study aims to investigate the warpage characteristics of printed parts based on geometrical parameters and build orientations to reduce the warpage extent.

Cuboidal samples of thermoplastic acrylonitrile butadiene styrene ranging from 5 to 80 mm were printed using a commercial MEX system. A Taguchi method-based design of experiment trial was performed to optimize the placement and orientation of the part for minimal warpage.

It was found that a lower value of the “in-plane” aspect ratio and a more prominent part thickness are favorable for minimal warpage. The part should always be placed near the region with the highest temperature (least thermal gradient) to minimize the warpage.

A novel dimensionless parameter (Y) is proposed that should be set to a minimum value to achieve minimal warpage. The results of this study can help improve the design and part placement for the MEX technique, thus elevating the print quality.

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

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

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

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

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

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

This study aims to examine the impact of specific printing factors, such as layer height, line width and build orientation, on the overall quality of fused filament fabrication (FFF) 3D printed structures. The project also intends to use response surface methodology (RSM) to maximize ultimate tensile strength (UTS) while lowering surface roughness and printing time.

This study used an FFF printer to fabricate samples of polylactic acid (PLA), which were then subjected to assessments of tensile strength and surface roughness. A tensile test was conducted under standardized conditions according to the ASTM D638 standard test method using the AG-50 kN Shimadzu Autograph. The Mitutoyo Surftest SJ-210, which utilizes a needle-tipped inductive method, was used to determine surface roughness. RSM was used for optimization.

This work provides useful insights into how the printing parameters affect FFF 3D printed structures, which may be used to optimize the printing process and improve PLA-based 3D printed products' qualities. The determined optimal values for building orientation, layer height and line width were 0°, 0.1 mm and 0.6 mm, respectively. The total desirability value of 0.80 implies desirable outcomes, and good agreement between experimental and projected response values supports the suggested models.

Previous RSM studies for 3D printing parameter optimization focused on mechanical properties or surface aspects, however, few examined multiple responses and their interactions. This study emphasizes the relevance of FFF parameters like line width, which are often overlooked but can dramatically impact printing quality. Mechanical properties, surface quality and printing time are integrated to comprehend optimization holistically.

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

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

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

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

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

This paper aims to present a predictive model approach to estimate the tensile behavior of polylactic acid (PLA) under uncertainty using the fused deposition modeling (FDM) and American Society for Testing and Materials (ASTM) D638’s Types I and II test standards.

The prediction approach combines artificial neural network (ANN) and finite element analysis (FEA), Monte Carlo simulation (MCS) and experimental testing for estimating tensile behavior for FDM considering uncertainties of input parameters. FEA with variance-based sensitivity analysis is used to quantify the impacts of uncertain variables, resulting in determining the significant variables for use in the ANN model. ANN surrogates FEA models of ASTM D638’s Types I and II standards to assess their prediction capabilities using MCS. The developed model is applied for testing the tensile behavior of PLA given probabilistic variables of geometry and material properties.

The results demonstrate that Type I is more appropriate than Type II for predicting tensile behavior under uncertainty. With a training accuracy of 98% and proven presence of overfitting, the tensile behavior can be successfully modeled using predictive methods that consider the probabilistic nature of input parameters. The proposed approach is generic and can be used for other testing standards, input parameters, materials and response variables.

Using the proposed predictive approach, to the best of the authors’ knowledge, the tensile behavior of PLA is predicted for the first time considering uncertainties of input parameters. Also, incorporating global sensitivity analysis for determining the most contributing parameters influencing the tensile behavior has not yet been studied for FDM. The use of only significant variables for FEA, ANN and MCS minimizes the computational effort, allowing to simulate more runs with reduced number of variables within acceptable time.

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

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

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

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

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

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

New sustainable approaches to fashion products are needed due to the demand for customization, better quality and cost reduction. Therefore, the decoration of fashion products using 3D printing technology can create a new direction for manufacturing science.

This study aims to optimize the 3D printing of soft TPU material on textiles. In the past decade, trials of using 3D printing in tailored fashion products have been done due to the 3D printing simplicity, low cost of materials and time reduction. Therefore, soft polymers can be multi-layer stepped-deposited smoothly with the fused filament fabrication process.

Even though there have been many attempts in the literature to 3D print multilayer polymer filaments directly onto textile fabrics by special-purpose 3D printers, only a few reports of decorative or personalized artefact 3D printing using open-platform filament material extrusion 3D printers. Printing speed, nozzle Z distance, textile fabric thickness and deposited strand height significantly affect 3D printing on textile fabric.

This study investigates the potential of 3D printing on textiles by changing the printing speed, nozzle hot end, Z distance and layer thickness. It presents two critical case studies of 3D printing soft thermoplastic polyurethane material on a cotton T-shirt and on a tulle textile to reveal the 3D printing on textile fabrics manufacturing challenges.

Material extrusion (ME) is a low-cost additive manufacturing (AM) technique that is capable of producing metallic components using desktop 3D printers through a three-step printing, debinding and sintering process to obtain fully dense metallic parts. However, research on ME AM, specifically fused filament fabrication (FFF) of 316L SS, has mainly focused on improving densification and mechanical properties during the post-printing stage; sintering parameters. Therefore, this study aims to investigate the effect of varying processing parameters during the initial printing stage, specifically nozzle temperatures, T_n (190°C–300°C) on the relative density, porosity, microstructures and microhardness of FFF 3D printed 316L SS.

Cube samples (25 x 25 x 25 mm) are printed via a low-cost Artillery Sidewinder X1 3D printer using a 316L SS filament comprising of metal-polymer binder mix by varying nozzle temperatures from 190 to 300°C. All samples are subjected to thermal debinding and sintering processes. The relative density of the sintered parts is determined based on the Archimedes Principle. Microscopy and analytical methods are conducted to evaluate the microstructures and phase compositions. Vickers microhardness (HV) measurements are used to assess the mechanical property. Finally, the correlation between relative density, microstructures and hardness is also reported.

The results from this study suggest a suitable temperature range of 195°C–205°C for the successful printing of 316L SS green parts with high dimensional accuracy. On the other hand, T_n = 200°C yields the highest relative density (97.6%) and highest hardness (292HV) in the sintered part, owing to the lowest porosity content (<3%) and the combination of the finest average grain size (∼47 µm) and the presence of Cr23C6 precipitates. However, increasing T_n = 205°C results in increased porosity percentage and grain coarsening, thereby reducing the HV values. Overall, these outcomes suggest that the microstructures and properties of sintered 316L SS parts fabricated by FFF AM could be significantly influenced even by adjusting the processing parameters during the initial printing stage only.

This paper addresses the gap by investigating the impact of initial FFF 3D printing parameters, particularly nozzle temperature, on the microstructures and physical characteristics of sintered FFF 316L SS parts. This study provides an understanding of the correlation between nozzle temperature and various factors such as dimensional integrity, densification level, microstructure and hardness of the fabricated parts.

Additive manufacturing (AM), also known as three-dimensional (3D) printing technology, has been used in the health-care industry for over two decades. It is in high demand in the health-care industry due to its strength to manufacture custom-designed and personalized 3D constructs. Recently, AM technologies are being explored to develop personalized drug delivery systems, such as personalized oral dosages, implants and others due to their potential to design and develop systems with complex geometry and programmed controlled release profile. Furthermore, in 2015, the US Food and Drug Administration approved the first AM medication, Spritam® (Apprecia Pharmaceuticals) which has led to tremendous interest in exploring this technology as a bespoke solution for patient-specific drug delivery systems. The purpose of this study is to provide a comprehensive overview of AM technologies applied to the development of personalized drug delivery systems, including an analysis of the commercial status of AM based drugs and delivery devices.

This review paper provides a detailed understanding of how AM technologies are used to develop personalized drug delivery systems. Different AM technologies and how these technologies can be chosen for a specific drug delivery system are discussed. Different types of materials used to manufacture personalized drug delivery systems are also discussed here. Furthermore, recent preclinical and clinical trials are discussed. The challenges and future perceptions of personalized medicine and the clinical use of these systems are also discussed.

Substantial works are ongoing to develop personalized medicine using AM technologies. Understanding the regulatory requirements is needed to establish this area as a point-of-care solution for patients. Furthermore, scientists, engineers and regulatory agencies need to work closely to successfully translate the research efforts to clinics.

This review paper highlights the recent efforts of AM-based technologies in the field of personalized drug delivery systems with an insight into the possible future direction.