Environmental drawbacks of lightweight design algorithms in material extrusion additive manufacturing: a case study

The sustainability of additive manufacturing (AM) has attracted attention nowadays, with different aspects needed to be considered. A key effort in this direction is the replacement of fossil fuel–originating polymers with biologically derived polymers. To achieve this, biologically derived polymers should exhibit a performance similar to that of commonly used polymers. Bacterially produced pure polyhydroxyalkanoate (PHA) was recently proposed by filament vendors for material extrusion (MEX) AM. Despite extensive research on the utilization of PHA in polymeric blends, the literature on the printability metrics of pure PHA is limited. At the same time, it has a high potential as an alternative to common petrochemical polymers. In this study, the impact of the main generic settings on the mechanical response of pure bio-sourced PHA in MEX AM was investigated. Four critical 3D printing parameters (print speed, layer height, nozzle temperature, and strand width) were evaluated using a robust design to optimize the tensile and impact metrics. Regression modeling was performed, and predictive equations were compiled and verified using validation testing. The quality of the samples was evaluated using electron microscopy. The analysis proved the importance of layer thickness in the tensile score, whereas nozzle temperature was found to be the most influential setting for impact loading. An increase of approximately 20% can be achieved by selecting appropriate 3D printing settings in the tensile test, whereas the impact strength can be radically improved by up to 550%.Graphical

Fused deposition modeling (FDM) is one of the most common additive manufacturing (AM) technologies for thermoplastic materials. With the development of carbon fiber-reinforced polymer (CFRP) filament for FDM, AM parts with improved strength and functionality can be realized. CFRP is anisotropic material and its mechanical properties have been well studied, however, AM printing strategy for CFRP parts has not been developed. This paper proposes a systematic optimization of the FDM 3D printing process for CFRP. Starting with standard coupon specimen tests to obtain mechanical properties of CFRP, finite element analyses (FEA) were conducted to find principal directions of the AM part and utilized to determine fiber orientations. A specific tool-path algorithm has been developed to distribute fibers with the desired orientations. To predict/assess the mechanical behavior of the AM part, the 3D printing process was simulated to obtain the anisotropic mechanical behavior induced by the customized tool-path printing. Bolt hole plate and spur gear were selected as case studies. FE simulations and associated experiments were conducted to assess their performance. CFRP parts printed by the optimized tool-path shows about 8% higher stiffness than those printed at regular infill patterns. In summary, assisted by FEA, a customized 3D printing tool-path for CFRP has been developed with case studies to verify the proposed AM design optimization methodology for FDM.

Two types of additive manufacturing (AM) techniques, fused filament fabrication (FFF) and selective laser sintering (SLS), were employed to fabricate specimens from three widely used AM polymers: polylactic acid (PLA), acrylonitrile butadiene styrene (ABS), and polyamide (PA): the first two using FFF and PA using SLS. The single‐edge notch bending (SENB) test was employed to measure the mode‐I fracture toughnesses of bulk and AM specimens in three directions relative to the printing direction. Unlike their bulk counterparts, AM polymers exhibited anisotropic fracture toughness. The degree of anisotropy in FFF‐PLA was much larger than FFF‐ABS and SLS‐PA. The smallest fracture toughness value for each AM polymer was achieved along the transverse printing direction. Different AM techniques affect fracture toughness differently. For instance, FFF is shown to increase PLA's plastic deformation, while SLS reduces PA's ductility. The decrease in fracture toughness is consistently observed when transitioning from bulk polymers to AM ones, reaching as much as 75%. This study provides insights into the fracture toughness of AM parts made via popular plastics, which are crucial for designing structural parts using FFF and SLS processes.

Characterization of mechanical properties and fracture mode of additively manufactured carbon fiber and glass fiber reinforced thermoplastics

Material extrusion additive manufacturing (MEAM) is an advanced manufacturing method that produces parts via layer-wise addition of material. The potential of MEAM to prototype lattice structures is remarkable, but restrictions imposed by manufacturing processes lead to practical limits on the form and dimension of structures that can be produced. For this reason, such structures are mainly manufactured by selective laser melting. Here, the capabilities of fused filament fabrication (FFF) to produce custom-made lattice structures are explored by combining the 3D printing process, including computer-aided design (CAD), with the finite element method (FEM). First, we generated four types of 3D CAD scaffold models with different geometries (reticular, triangular, hexagonal, and wavy microstructures) and tunable unit cell sizes (1–5 mm), and then, we printed them using two nozzle diameters (i.e., 0.4 and 0.8 mm) in order to assess the printability limitation. The mechanical behavior of the above-mentioned lattice scaffolds was studied using FEM, combining compressive modulus (linear and nonlinear) and shear modulus. Using this approach, it was possible to print functional 3D polymer lattice structures with some discrepancies between nozzle diameters, which allowed us to elucidate critical parameters of printing in order to obtain printed that lattices (1) fully comply with FFF guidelines, (2) are capable of bearing different compressive loads, (3) possess tunable porosity, and (3) overcome surface quality and accuracy issues. In addition, these findings allowed us to develop 3D printed wrist brace orthosis made up of lattice structures, minimally invasive (4 mm of thick), lightweight (<20 g), and breathable (porosity >80%), to be used for the rehabilitation of patients with neuromuscular disease, rheumatoid arthritis, and beyond. Altogether, our findings addressed multiple challenges associated with the development of polymeric lattice scaffolds with FFF, offering a new tool for designing specific devices with tunable mechanical behavior and porosity.

Additive manufacturing (AM) processes, such as Fused Filament Fabrication (FFF), enable the production of lightweight parts with high stiffness-to-weight ratios, making them highly suitable for a wide range of engineering applications. However, ensuring the mechanical reliability of these components, particularly for load-bearing purposes, requires systematic mechanical testing of well-designed specimens to asses their suitability. While the tensile properties of additively manufactured materials have been extensively studied, the compressive behavior of components produced via AM, particularly those made from thermoplastic materials, remains comparatively underexplored and insufficiently characterized in the existing body of research. Among these materials, polylactic acid (PLA)—a biodegradable thermoplastic derived from renewable resources—has gained prominence in AM applications. Recent studies have investigated the compression properties of PLA in reinforced materials; however, the focus has primarily been on solid, semi-solid, or porous specimens. These investigations largely overlook thin-walled structures, which are integral to weight-saving designs and commonly feature in topology-optimized structures. Understanding the mechanical behavior of monolayers, the fundamental building blocks of most AM components, is essential for accurately predicting the overall performance of multilayer structures. Monolayers represent the smallest, most basic structural elements of AM parts, and their properties directly influence the behavior of the final, more complex assemblies. Establishing a methodology that correlates monolayer properties with those of multilayer components could significantly streamline testing procedures. By performing mechanical tests on monolayers, instead of on more intricate multilayer specimens, manufacturers could reduce testing complexity and cost while accelerating the development process. The current literature reveals a gap in the design and analysis of thin-walled AM specimens, especially monolayers, under compressive loads. Specifically, the design of monolayer or thin-walled AM compression specimens without infill has not been thoroughly explored. This article addresses this gap by investigating the design and testing of AM monolayer compression specimens produced using FFF of PLA. Three distinct specimen geometries are considered—circular, helicoidal, and S-shaped—to evaluate their potential for understanding and predicting the compressive behavior of AM monolayer structures.

The very first application in the Additive Manufacturing (AM) is the development of prototypes, that too by using plastic in fused deposition modeling (FDM) 3D printing processes, Stereolithography, SLS and others. Apart from printing ordinary shaped objects, Additive Manufacturing is also able to produce composites by using different materials on a single production platform. Continuous research and development has improved Additive manufacturing capability to develop various composite materials including fiber-reinforced composite, Biocomposite, Nanocomposites, Polymer matrix composites and Polymers. The primary purpose of this work is the literature-based study on FDM printed composite materials. For this purpose, the keyword “FDM Composites” is used in SCOPUS search and research papers from reputed publishers and Journals were identified and studied. Further, discussed the methods for the development of composite using FDM, and different composite materials with their types which can be printed by using FDM are discussed in a tabular form. It is learned that FDM provides an extraordinary chance to develop typical AM parts with the use of composite materials. Exploration, expansion and commercialization of AM materials are a significant extent of the study in the field of a composite at present.

Effects of auxiliary heat on warpage and mechanical properties in carbon fiber/ABS composite manufactured by fused deposition modeling

Additive manufacturing (AM) offers unique opportunities for agile and flexible manufacturing; however, there are concerns that AM supply chains may present risks in the form of inferior materials, poor process control, or counterfeit parts. There is a need for new technologies capable of tracking and validating AM parts and materials. This study demonstrates a method to predict the source of AM parts using images captured by a smartphone and analyzed using deep learning models. For this study, 918 parts were manufactured using industrial fused deposition modeling (FDM) machines from six contract manufacturers. The parts include nine different designs representative of common industrial parts with diverse geometric features. A smartphone camera collected high-resolution photographs of each part with consistent lighting. The smartphone images show minimal visual differences between parts that are produced by different suppliers, and it is not possible for a human to identify the manufacturing source by viewing the images or inspecting the parts. A deep machine learning (ML) model was trained to predict the supplier that produced each part. The part images are 4000 × 3000 pixels in size, which is typical for a smartphone but much higher than typical computer vision deep learning models that use images that are 224 × 224 pixels. A novel sampling strategy allows the high-resolution data to be interpreted with deep learning. The source identification model achieves 99.9% accuracy identifying the origin of 459 testing parts across the six suppliers and the nine part designs. The model is data driven, recognizing patterns from raw smartphone images, removing the need for manually designed or specialized features extracted with computation geometry. The model also demonstrates design generalizability and can extrapolate to make predictions for part designs never previously seen by the model. When the training dataset consists of five part designs, the model achieves 98.8% accuracy when applied to four previously unseen part designs. The source identification model is also data efficient, enabling the rapid addition of new manufacturers into the system with minimal training data. When trained using only 10 parts per manufacturer, the model achieves over 90% accuracy. Increasing the training set to 30 parts per manufacturer allows the model to exceed 95% accuracy. The model can authenticate the source of parts without the cooperation of the supply chain enabling anti-counterfeiting methods by detecting if parts originate from different sources or if the supplier has altered the production process. This research demonstrates the potential of source identification using simple mobile imaging to track the quality and authenticity of AM parts.

Recycling meal-ready-to-eat (MRE) pouches into polymer filament for material extrusion additive manufacturing

The flexibility offered by additive manufacturing (AM) technologies to fabricate complex geometries poses several challenges to non-destructive evaluation (NDE) and quality control (QC) techniques. Existing NDE and QC techniques are not optimized for AM processes, materials, or parts. Such lack of reliable means to verify and qualify AM parts is a significant barrier to further industrial adoption of AM technologies. Electromechanical impedance measurements have been recently introduced as an alternative solution to detect anomalies in AM parts. With this approach, piezoelectric wafers bonded to the part under test are utilized as collocated sensors and actuators. Due to the coupled electromechanical characteristics of piezoelectric materials, the measured electrical impedance of the piezoelectric wafer depends on the mechanical impedance of the part under test, allowing build defects to be detected. This paper investigates the effectiveness of impedance-based NDE approach to detect internal porosity in AM parts. This type of build defects is uniquely challenging as voids are normally embedded within the structure and filled with unhardened model or supporting material. The impact of internal voids on the electromechanical impedance of AM parts is studied at several frequency ranges.

Purpose With recent advances in additive manufacturing (AM), polymer-based three-dimensional (3D) printers are available for relatively low cost and have found their way even in domestic and educational uses. However, the optimum conditions for processing and post-processing of different materials are yet to be determined. The purpose of this paper is to examine the effects of printing temperature, pattern and annealing conditions on tensile strength and modulus of samples printed with polylactic acid (PLA). Design/methodology/approach This study focuses on fused deposition modelling according to ISO/ASTM 52900 material extrusion AM. To print parts with maximum mechanical properties, the printing variables must be optimised. To determine the printing and annealing condition on physical and mechanical properties of PLA-based parts, dogbone-shaped tensile samples were printed at four different nozzle temperatures and five different filling patterns embedded in a 3D printing software. The samples were further annealed at three different temperatures for three different time intervals. The mechanical properties were evaluated and the changes in mechanical properties were analysed with the help of rheometrical measurements. Findings The results showed that printing condition has a significant influence on final properties, for example, the strain at break value increases with increasing nozzle temperature from 34 to 56 MPa, which is close to the value of the injected sample, namely, 65 MPa. While tensile strength increases with printing temperature, the annealing process has negative effects on the mechanical properties of samples. Originality/value The authors observed that traditional findings in polymer science, for example, the relationship between processing and annealing temperature, must be re-evaluated when applied in 3D printing because of major differences in processing conditions resulting from the layer-by-layer manufacturing.

PurposeThe purpose of this paper is to communicate a method to perform simultaneous topology optimization of component and support structures considering typical metal additive manufacturing (AM) restrictions and post-print machining requirements.Design/methodology/approachAn integrated topology optimization is proposed using two density fields: one describing the design and another defining the support layout. Using a simplified AM process model, critical overhang angle restrictions are imposed on the design. Through additional load cases and constraints, sufficient stiffness against subtractive machining loads is enforced. In addition, a way to handle non-design regions in an AM setting is introduced.FindingsThe proposed approach is found to be effective in producing printable optimized geometries with adequate stiffness against machining loads. It is shown that post-machining requirements can affect optimal support structure layout.Research limitations/implicationsThis study uses a simplified AM process model based on geometrical characteristics. A challenge remains to integrate more detailed physical AM process models to have direct control of stress, distortion and overheating.Practical implicationsThe presented method can accelerate and enhance the design of high performance parts for AM. The consideration of post-print aspects is expected to reduce the need for design adjustments after optimization.Originality/valueThe developed method is the first to combine AM printability and machining loads in a single topology optimization process. The formulation is general and can be applied to a wide range of performance and manufacturability requirements.

In this study, we present the first results of a newly developed melt pool monitoring tool for selective laser melting, called DMP-meltpool. A manual data analysis method is given, and the events indicated by the analysis (DMP-meltpool events) are shown to correlate to the static tensile properties of the samples built. These events indicate the probability of material discontinuities (defects) in the metal additive manufacturing (AM) parts. In order to do so, cylindrical bars of Ti-6Al-4V ELI were built and monitored using DMP-meltpool. The tensile properties of the printed cylinders were correlated with the events detected by DMP-meltpool. An inverse relation between plastic elongation and the DMP-meltpool event density was observed. These results show that DMP-meltpool can be used to predict the quality of AM parts by detecting variations in the signals and tagging these events throughout the build as defects. Thus the technique can be employed for first stage in-line quality control of AM parts and for sorting out parts with potential defects non-destructively. The DMP-meltpool events could have significant correlations with other mechanical properties (like fatigue, hardness, fracture toughness, and crack propagation) since such properties are influenced by defects originating from the process instabilities.

PurposeDue to the complexity of and variations in additive manufacturing (AM) processes, there is a level of uncertainty that creates critical issues in quality assurance (QA), which must be addressed by time-consuming and cost-intensive tasks. This deteriorates the process repeatability, reliability and part reproducibility. So far, many AM efforts have been performed in an isolated and scattered way over several decades. In this paper, a systematically integrated holistic view is proposed to achieve QA for AM.Design/methodology/approachA systematically integrated view is presented to ensure the predefined part properties before/during/after the AM process. It consists of four stages, namely, QA plan, prospective validation, concurrent validation and retrospective validation. As a foundation for QA planning, a functional workflow and the required information flows are proposed by using functional design models: Icam DEFinition for Function Modeling.FindingsThe functional design model of the QA plan provides the systematically integrated view that can be the basis for inspection of AM processes for the repeatability and qualification of AM parts for reproducibility.Research limitations/implicationsA powder bed fusion process was used to validate the feasibility of this QA plan. Feasibility was demonstrated under many assumptions; real validation is not included in this study.Social implicationsThis study provides an innovative and transformative methodology that can lead to greater productivity and improved quality of AM parts across industries. Furthermore, the QA guidelines and functional design models provide the foundation for the development of a QA architecture and management system.Originality/valueThis systematically integrated view and the corresponding QA plan can pose fundamental questions to the AM community and initiate new research efforts in the in-situ digital inspection of AM processes and parts.