Popular Additive Manufacturing Research Articles

A data integration framework of additive manufacturing based on FAIR principles

Abstract Laser-powder bed fusion (L-PBF) is a popular additive manufacturing (AM) process with rich data sets coming from both in situ and ex situ sources. Data derived from multiple measurement modalities in an AM process capture unique features but often have different encoding methods; the challenge of data registration is not directly intuitive. In this work, we address the challenge of data registration between multiple modalities. Large data spaces must be organized in a machine-compatible method to maximize scientific output. FAIR (findable, accessible, interoperable, and reusable) principles are required to overcome challenges associated with data at various scales. FAIRified data enables a standardized format allowing for opportunities to generate automated extraction methods and scalability. We establish a framework that captures and integrates data from a L-PBF study such as radiography and high-speed camera video, linking these data sets cohesively allowing for future exploration. Graphical abstract

Effects of heat treatment on the mechanical properties of 3D-printed polylactic acid: Study of competition between crystallization and interlayer bonding

Fused deposition modeling (FDM) is a popular additive manufacturing technique because of its flexibility, customization, cost-effectiveness, and eco-friendliness. However, due to the layer-by-layer fabrication process and the formation of voids, the mechanical properties of printed parts are lower than other manufacturing techniques. As a solution, heat treatments can effectively mitigate additive manufacturing limitations and improve the mechanical properties of printed parts. The interlayer bonding and subsequently mechanical properties of 3D-printed PLA were affected by the competition between crystallization and chain diffusion of 3D-printed polylactic acid during the heat treatment process. This competition highlights the importance of optimization of heat treatment. Additionally, crystallite growth could cause a reduction in some mechanical properties, like elongation. The size, distribution, and content of voids are also influenced by heat treatment, playing a crucial role in the mechanical properties of 3D-printed parts. In this research, printed samples were heat-treated at temperatures of 75 ºC, 110 ºC, and 130 ºC for two hours. Furthermore, compression-molded samples were fabricated to inspect a comparison of mechanical properties between the traditional and additive manufacturing methods and investigate the effect of layer-by-layer fabrication on these properties. Sample AM-75 showed a 15.5% improvement in bonding strength compared to the non-annealed printed sample, resulting in an increase of 20.2% in tensile strength and 275% in impact strength. Moreover, annealing the sample at 130 ºC resulted in a 7.6% reduction in porosity and a significant improvement of 220% in crystallinity, heat deflection temperature, flexural modulus, and flexural strength, with improvements of 121%, 29.5%, and 15.2% respectively.

Optimizing FDM process parameters: predictive insights through taguchi, regression, and neural networks

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.

Fatigue Performance and Impact Toughness of PBF-LB Manufactured AlSi 10Mg

Additive manufacturing (AM) has transformed the production of complex geometries and customized components.Powder Bed Fusion with Laser Beam (PBF-LB) is a popular AM technique known for its ability to produce parts with excellent mechanical properties. This study focuses on the characterization of AlSi10Mg, an aluminum alloy widely used in aerospace and automotive industries, manufactured through PBF-LB. The influence of printing orientation on the mechanical properties of the material is investigated. Previous research has shown that PBF-LB manufactured AlSi10Mg can exhibit superior mechanical properties compared to traditional material, but the anisotropic nature of parts produced by PBF-LB can significantly affect their properties. Tensile, impact, and fatigue testing are conducted to assess the mechanical behavior of the printed AlSi10Mg specimens under different loading conditions. Microstructural analysis is performed using Field-Emission Scanning Electron Microscopy (FESEM) equipped with Electron Backscatter Diffraction (EBSD) to examine the microstructural features introduced during the PBF-LB process. The results provide insights into the mechanical behavior of AlSi10Mg produced through PBF-LB and contribute to the design and utilization of components manufactured using this AM technique.

Strain rate dependence of 3D printed continuous fiber reinforced composites

Fused Deposition Modeling (FDM), one of the most popular additive manufacturing technologies in polymer 3D printing, has been increasingly used in fabricating continuous fiber reinforced composites (CFRCs) in recent years. Intensive research has been conducted on the quasi-static mechanical properties of FDM made CFRCs and the corresponding failure mechanisms. However, limited research has been reported on the dynamic mechanical behavior of CFRCs fabricated using FDM. This paper presents a comprehensive experimental study to understand the deformation and load carrying capacity of FDM-fabricated continuous fiber reinforced Onyx (CFRO), with a focus on the dynamic tensile loadings up to strain rate of 100/s. The deformation and failure mechanisms of FDM-fabricated CFROs under dynamic loadings have been identified after microstructural analyses of fractured specimens. The effects of fiber type, fiber volume fraction, and strain rate on the tensile behavior have been revealed for Onyx reinforced by carbon fiber filaments (CFF), glass fiber filaments (GFF), and Kevlar fiber filaments (KFF). Empirical formulas have also been derived to describe the relationships between the elastic modulus, ultimate tensile strength (UTS) and fiber volume fraction. The results demonstrate that increasing the strain rate from 6.7 × 10−4/s to 100/s substantially enhances the UTS of all three types of printed CFRO.

Incorporating surface roughness into numerical modeling for predicting fatigue properties of L-PBF AlSi10Mg specimens

Laser-powder bed fusion (L-PBF) is a popular additive manufacturing method for fabricating metal parts with complex geometries. However, one of the primary setbacks lies in the surface quality of the as-built components. Poor surface quality influences the dynamic fatigue properties of L-PBF parts. Experimental investigation of fatigue properties is time-intensive and expensive. Hence, computational modeling can be implemented to critically evaluate the impact of surface roughness on fatigue. In this work, a numerical modeling framework is developed that considers the influence of part-scale surface roughness on fatigue properties. To calibrate and validate the numerical modeling framework, mechanical investigations of miniature AlSi10Mg specimens are conducted in a custom-built tensile-fatigue testing apparatus. First, the tensile properties are evaluated to find out the reliability of the apparatus. Thereafter, the fatigue investigations of two sets of specimens are conducted: five specimens (FS1-FS5) for calibrating the numerical model and three specimens (VFS1-VFS3) for model validation. The numerical modeling framework involves the 3D reconstructed specimen geometries from computed tomography (CT) imaging of the VFS1-VFS3 specimens. The resultant 3D geometries are discretized using Ansys finite element software and analyzed to simulate the fatigue behavior. The numerical results of fatigue life and failure locations of the VFS1-VFS3 specimens are compared with the experimental observations. The comparison shows that the numerical results are within 5 % of the experimental observations. Additionally, the location of maximum strain localization in numerical modeling matches the crack initiation location. In conclusion, the numerical modeling can predict low cycle fatigue life and failure location of L-PBF AM specimens.

Neosanding postprocessing for improving surface roughness of extrusion-based 3D printing of PLA parts: a comparative analysis of stylus profilometer and confocal profilometry methods

Extrusion-based 3D printing (E3DP) is a popular additive manufacturing technique known for its versatility in creating prototypes and functional parts. However, achieving high surface quality has posed challenges regarding accuracy and finish. To address this issue, this study aims to enhance the surface quality of E3DP components fabricated by the fused filament fabrication (FFF) method and polylactic acid (PLA) material by applying neosanding postprocessing. The research investigates the impact of key neosanding process factors on surface roughness, namely neosanding spacing, neosanding speed, and flow rate. To ensure a comprehensive evaluation, each factor is examined at four levels, covering a wide range of values relevant to the neosanding process. Surface roughness is quantified using the average roughness parameter (Ra) and measured using both stylus profilometer and confocal profilometry methods. The results highlight a substantial decrease in surface roughness achieved through the neosanding method. At default factor levels of the neosanding method, the stylus profilometer method achieves an impressive 83% reduction in surface roughness, while the confocal profilometry method achieves an 80% reduction. Among the neosanding process factors, neosanding spacing significantly influences surface roughness values. Understanding and optimizing this factor is crucial for achieving desired surface quality in FFF-produced PLA parts. This study makes a valuable contribution to the field by optimizing surface roughness in FFF-produced PLA parts through neosanding postprocessing. By exploring the influence of neosanding tool factors and comparing measurement methods, manufacturers can enhance the surface quality of FFF-manufactured parts, paving the way for broader applications across various industries.

Effect of in situ thermal treatment on interlayer adhesion of 3D printed polyetherimide (PEI) parts produced by fused deposition modeling (FDM)

Polyetherimide (PEI) possesses remarkable mechanical and thermal properties that make it a promising high-performance material for a wide range of applications. In addition to its simple processability, adhesive properties, and minimal smoke emission when heated, PEI is a suitable material for additive manufacturing. Because of its low cost and straightforward method, fused deposition modeling, or FDM, is a popular additive manufacturing technique for extruding thermoplastic filaments. Parts printed with the FDM approach, on the other hand, have common drawbacks such as low mechanical strength, shape inaccuracy, high porosity, void formation, interlayer adhesion problems, and anisotropic properties. The purpose of this study was to evaluate the effect of the thermal process during printing on the interlayer adhesion strength and the mechanical performances of PEI. The thermal treatment was done on a radiant heating system at 390 °C with a printing speed of 35 mm/s. In this study, parts printed with and without radiant heating system were characterized and analyzed with scanning electron microscopy (SEM), optical microscopy (OM), X-ray microtomography (µ-CT), optical profilometry (OP), atomic force microscopy (AFM), dynamic mechanical analysis (DMA), and tensile tests. The results revealed that the treated specimen exhibited better interlayer adhesion between printed layers. The interfacial voids were less visible in treated specimens. Furthermore, the treated specimen had lower porosity (5%) than the untreated one (8%). Moreover, increases of 183% in tensile strength, 22% in elastic modulus, and 190% in elongation at break were observed for treated specimens when compared to untreated ones.

Electrically conductive and flexible filaments of hot melt adhesive for the fused filament fabrication process

Fused filament fabrication technique is the most popular additive manufacturing that has received extensive attention, revolutionizing industrial production processes. In addition to the standard thermoplastic polymers, much emphasis is placed on developing electrically conductive functional filaments being the most interesting. While there are conductive filaments that are commercially available, the market lacks a wide range of flexible options. Hence, this paper presents innovative filaments based on the combination of thermoplastic hot melt copolyester adhesive (HMA) with multi-walled carbon nanotubes (MWCNT). The incorporation of carbon nanotubes into the HMA was carried out through a two-step process. First, a masterbatch of 10 wt. % MWCNT was diluted with pure polymer using a half-industrial twin-screw extruder to obtain concentrations in the range of 1–9 wt. %. Consequently, the nanocomposite pellets were extruded again into the form of filaments. The rheological analysis demonstrates that adding MWCNT to the HMA increases both the viscous and elastic behavior of the composites. The homogenously dispersed nanotubes in the polymer matrix led to electrical conductivity of 1.39 S/m for the filaments containing 10 wt. % MWCNT. They are also characterized by the stiffness and tensile strength of about 300 and 13 MPa, respectively. With high thermal stability up to 360 °C, low porosity, and high flexibility, the developed filaments are suitable for 3D printing. The printability of all filaments was confirmed, exhibiting lack of breakage during printing and visibly better quality of the parts with the higher nanotube content.

Theoretical and experimental study on the fatigue property of material extrusion products and improvement by using vibration machining during manufacturing process

Nowadays, material extrusion (ME) is one of the most popular additive manufacturing technology, which offers a new way of manufacturing parts with specific complex geometry and mechanical properties. However, with the expansion of the application range of ME products, it is urgent to investigate their fatigue property so as to ensure the reliability and safety. In this paper, the damage mechanical fatigue life prediction model of ME products considering void defects was established. Fatigue test specimens built with different processing parameters were then fabricated, and tensile fatigue tests were carried out under different stress levels. In addition, a novel method, utilizing vibration during ME process, was proposed to improve the built products’ fatigue property, and corresponding theoretical and experimental investigations were performed to reveal the influencing mechanism of the vibration utilized. The results showed that the proposed model was validated by comparing the theoretical predictions with the experimental measurements. As layer height and printing speed were increased, the fatigue life of ME specimens dropped, while it rised with the increasing extrusion temperature. The built products’ fatigue life could be improved obviously by using vibration.

Machine learning applications for quality improvement in laser powder bed fusion: A state-of-the-art review

As one of the most popular additive manufacturing methods, laser powder bed fusion (L-PBF) builds 3D components with complex geometries layer by layer using alloy powders. This technique has found widespread adoption in various industrial applications, including biomedical and aerospace fields. However, L-PBF encounters challenges related to poor process repeatability and inconsistency in fabricated part quality, which hinder its broader adoption. Various quality improvement methods have been proposed to address these challenges and achieve high-quality, reliable parts. Given the abundance of parameters and the intricate phenomena that occur during the process, machine learning (ML) methods play a critical role in enhancing the quality of L-PBF, providing an optimum solution for improving the quality of manufactured parts. This review paper begins with a comprehensive and straightforward introduction to ML, focusing primarily on different learning approaches. Subsequently, the paper explores different ML methods applied to parameter optimization and in situ monitoring, both contributing to enhanced quality control. In parameter optimization, ML is employed to extract relationships between input parameters and key factors such as melt pool characteristics, porosity, and mechanical properties. Shifting the focus to in situ monitoring, the paper introduces the application of ML in analyzing various sensor data generated throughout the L-PBF process. Accomplished tasks include segmentation, regression, and classification of quality measurement. In summary, this review underscores the critical role of machine learning in addressing challenges associated with L-PBF, providing an optimal solution for quality enhancement.

Optimizing the printing parameters for dimensional accuracy of distal femur bone by using Taguchi’s method

BackgroundFused deposition modelling (FDM) is a popular additive manufacturing technique with capability of producing complex and integrate shapes. One of the critical aspects of FDM is the dimensional accuracy of 3D (three-dimension) printed model, especially in medical science applications, as proper fit and function with human body can prevent patient’s discomfort, complication or even harm.ObjectiveIn this research work, the optimisation of print parameters: layer height, nozzle temperature, printing speed, infill pattern and infill density for improving the dimensional accuracy of distal femur bone, an irregular and complex shaped geometry is carried out using Taguchi’s method and to study its influence using ANOVA (analysis of variance).Methodology3D CAD (computer-aided design) model of the distal femur bone is generated from a CT (computerized tomography) scan using 3D slicer and its associated errors are corrected using Ansys SpaceClaim. The model is prepared for printing using Ultimaker Cura as per L16 orthogonal array experimental layout where TEA (trans epicondylar axis), which is the distance between the most prominent point of the lateral and medial epicondyle, is set at 45° from X-axis in XY plane, i.e. diagonally on the plane of printing bed. It is then printed with PLA (polylactic acid) filament. Length along TEA is compared accordingly with 3D CAD model. Taguchi’s method of ‘smaller the better’ is applied for reducing deviation. Further, ANOVA analysis is done on the data set and a linear regression model is also developed.ResultThrough Taguchi’s method, the optimum parameters were found to be triangle for infill pattern, 200 °C for nozzle temperature, 30 mm/s for nozzle speed, 0.1 mm for layer height and 40% for infill density. ANOVA analysis shows that all parameters contribute significantly with layer height being the most influential parameter, followed by infill pattern, nozzle speed, nozzle temperature and infill density. Mathematical model through multiple linear regression method was developed with determination of coefficient value of 96.91% and standard residual value is within the acceptable range of ± 2 indicating that there is no outliner in the data.

Highlights on the Influence of Thermal Reprocessing of PLA on Mechanical Properties

FDM is one of the most popular additive manufacturing techniques through 3D printing. Within this technique, one of the intensively studied subjects is the analysis of the factors that influence the mechanical behavior of the 3D printed parts. However, despite numerous studies that show the influence of parameters such as raster orientation, layer height/thickness, build orientation, number of layers, there are some other process parameters which are less analyzed. Due to some interactions between the effects due to different parameters, it becomes challenging to analyze influences on the mechanical behavior of 3D printed materials. Less studied subject is the mechanical behavior of the extruded filament through the nozzle and the interaction between deposited traces. The mechanical behavior of the air extruded filament, the one trace, two traces and fourteen traces deposition were experimentally analyzed in the paper by tensile testing. Also, the mechanical behavior of the raw filament was checked. In addition, the differential scanning calorimetry (DSC) was conducted on raw filament and extruded filament in order to identify the relation between the crystallinity of the polymer after thermal reprocessing with the mechanical properties. The raw filament is typically a continuous and homogenous material with consistent diameter and structure. However, when the filament is extruded trough the nozzle its mechanical behavior will significantly change due to thermal reprocessing. Among the factors that influence the properties are: extruder temperature, platform temperature, extruding velocity, cooling, flow rate of the extruded filament. All can affect the crystallinity of the polymer on one hand and the strength of the adhesion between the consecutive traces on the other hand, as the results are quantifying it.

Evaluation on the performance of sustainable wire arc additive manufacturing using micro plasma arc welding

Metal additive manufacturing has gained popularity due to its significant advancement in minimising waste. The product design using additive manufacturing (AM) does not require mould and less material required, which is less waste. In this way, AM has the potential to transform the manufacturing industries towards greater sustainability. Besides, AM allow more complex designs and shape than conventional manufacturing. Wire arc additive manufacturing (WAAM) is one of the most popular metal AM that has received significant attention due to its usefulness for localising, repairing, and modifying instead of discarding and replacing entire components. However, conventional welding technology faced the issue of significant heat input, low surface quality, and low dimensional accuracy of WAAM components. Micro-plasma arc welding (MPAW) is a new alternative to the WAAM process. The speciality of MPAW is low current provided of 1A to 25A current range, leading to low heat input and energy-efficient manufacturing. The present study shows various metallic structures developed using an MPAW-based WAAM system, and the relative error between the CAD model and four different structures was determined. Overall, the study outcome shows the geometric uniformity of parts produced with an MPAW-based WAAM system and demonstrates smooth surface finish can be obtained.

Reliability Investigations in Printed Electronic Assemblies

Aerosol-Jet Printing (AJP) technology is a popular additive manufacturing method for achieving complex net geometry with micron-level accuracy. This opens up new possibilities in fabrication of electronic assemblies in 3D curvilinear form factors. Therefore, it is vital to understand not only the performance of AJP printed electronics, but also their reliability under different kinds of life-cycle operational and environmental stresses. Electrochemical migration (ECM) under temperature-humidity-bias (THB) conditions can lead to dendritic growth, which can cause dielectric breakdown, leakage current, and unexpected short circuit. ECM propensity and life prediction models for THB conditions were studied for AJP printed coupons based on silver-nanoparticle (AgNP) inks. Cyclic thermo-mechanical fatigue was studied under accelerated temperature cycling testing and modeling. Failures were found to occur mostly at interconnect transition regions due to stress concentrations caused by sudden transitions in local architecture. Drop/Shock failures for extreme accelerations were studied using a high-G drop tower. Deformations were characterized using high-speed photography and transient finite element modeling was used to study high strain-rate failures. Acknowledgments: Jason Fleischer from the Laboratory for Physical Sciences, Harvey Tseng and Jian Yu from the Army Research Lab

Additive Manufacturing of SS316L/IN718 Bimetallic Structure via Laser Powder Bed Fusion.

Laser powder bed fusion (LPBF) is a popular additive manufacturing (AM) technique that has demonstrated the capability to produce sophisticated engineering components. This work reports the crack-free fabrication of an SS316L/IN718 bimetallic structure via LPBF, along with compositional redistribution, phase transformations and microstructural development, and nanohardness variations. Constituent intermixing after LPBF was quantitatively estimated using thermo-kinetic coefficients of mass transport and compared with the diffusivity of Ni in the austenitic Fe-Ni system.

Development of a comprehensive model for predicting melt pool characteristics with dissimilar materials in selective laser melting processes

Selective laser melting (SLM) is a popular additive manufacturing (AM) technique for the fabrication of precise components. The success of SLM relies on accurate control of the melt pool, where the metal powder melts and solidifies. Fluid flow inside the melt pool is driven by forces like Marangoni force and recoil pressure. SLM process begins with the fabrication of support, which involves the process of joining dissimilar metals. A strong support-base substrate connection ensures the success of subsequent additive manufacturing. In this study a comprehensive model was developed to predict the dimensions and characteristics of the melt pool with a focus on the first layer of the SLM process. An initial numerical simulation was done to obtain transient melt pool temperature and velocity distribution during the SLM process. The results were then coupled with a theoretical analysis development of the proposed model. The effectiveness of the model was validated by experimental results and melt pool characteristics from previous publications. The model shows Root Mean Square Error (RMSE) of 0.53 and 0.49 for height-to-width ratio and normalized area, respectively. The Mean Absolute Percentage Error (MAPE) of normalized area is 15.7% for the majority of the data, which demonstrates a good accuracy of the model. In addition, the transition from conduction mode to keyhole mode can be identified with the model. This model can be used to rapidly and effectively predict the melt pool characteristics for SLM and other similar AM processes with a wide range of processing parameters.

The Influence of Thermoplastic Composite Recycling on the Additive Manufacturing Process and In-Use Phase as Candidate Materials for Wearable Devices Applications.

Fused filament fabrication (FFF) is a popular additive manufacturing (AM) method for creating thermoplastic parts with intricate geometrical designs. Pure thermoplastic materials utilized in FFF, whose polymeric matrix is reinforced with other materials, such as carbon fibers (CFs), introduce products with advanced mechanical properties. However, since not all of these materials are biodegradable, the need for recycling and reuse immediately emerges to address the significant problem of how to dispose of their waste. The proposed study evaluates the printability, surface morphology and in vitro toxicity of two thermoplastic-based composite materials commonly used in wearable device manufacturing to provide enhanced properties and functionalities, making them suitable for various applications in the field of wearable devices. Tritan Copolyester TX1501 with 7.3% chopped CFs (cCFs) and Polyamide 12 (PA12) with 8.6%cCFs and 7.5% iron Magnetic Nanoparticles (MNPs)-Fe4O3 were used in the discrete ascending cycles of recycling, focusing on the surface quality performance optimization of the printed parts. Through stereoscopy evaluation, under-extrusion, and over-extrusion defects, as well as non-uniform material flow, are assessed in order to first investigate the influence of various process parameters' application on the printing quality of each material and, second, to analyze the optimal value fluctuation of the printing parameters throughout the recycling cycles of the materials. The results indicate that after applying certain adjustments to the main printing parameter values, the examined recycled reinforced materials are still effectively 3D printed even after multiple cycles of recycling. A morphology examination using scanning electron microscope (SEM) revealed surface alterations, while a cytotoxicity assessment revealed the adverse effects of both materials in the form of cell viability and the release of proinflammatory cytokines in the cell culture medium.

Impact of track length, track shape, and track location on thermal distortion in laser powder bed fusion of IN625: Single laser vs. three lasers

Laser powder bed fusion is a popular additive manufacturing process for creating complex metallic components. However, it has limitations in terms of build rate and component size. One potential solution to overcome these limitations is the utilization of multiple lasers for part fabrication. Nevertheless, thermal distortion poses a significant challenge in this approach. While several process parameters can be optimized to minimize distortion, the scan pattern is a critical factor. This study focuses on the design of efficient scan patterns to improve distortion in multi-laser powder bed fusion parts. Previous research has primarily explored default scan patterns like raster, spiral, and Hilbert. In contrast, this paper takes a fundamental approach by introducing customizable variables such as track length, track angle, and track location. The resulting paths are simulated using a commercially available thermo-mechanical solver to systematically evaluate their impact on melt pool dimensions, temperature evolution, stresses, and distortion. The findings indicate that multi-laser simulations consistently exhibit lower thermal distortion compared to single-laser configurations. Longer track lengths result in higher maximum displacement, while straight paths with a 0° track angle reduce distortion. Additionally, boundary conditions significantly affect distortion, and printing farther away from constrained edges proves beneficial in minimizing distortion. In conclusion, the insights derived from this study can be used to design of intelligent scan paths.

Fused filament fabrication of Nylon beyond the glass transition temperature in a thermally-insulated machine

Fused filament fabrication (FFF) is a popular additive manufacturing process by extruding the molten thermoplastic from a nozzle to build an object layer-by-layer. Nylon is an engineering plastic that has good mechanical and chemical properties for assistive devices such as orthoses and prostheses (O&P). Nylon has been successfully applied in FFF of custom O&P. Based on the low glass transition temperature around 50-70℃ of Nylon, an idea to thermally-insulate the build chamber of an FFF machine to raise the temperature in the build volume to above Nylon’s glass transition temperature without additional heating is proposed. Without extensive active heating and cooling, such thermally-insulated FFF machines may achieve cost-effective FFF of custom O&P for practical use. This paper studies this FFF machine concept for FFF of dog bone specimens. The chamber of the FFF machine was thermally insulated. COMSOL Multiphysics® simulation software was adopted to predict the chamber’s temperature level and verified by experimental measurements with eight thermocouples. The effectiveness of the method was validated by the tensile test and microscopic observation of the printed tensile specimens. The strength of the FFF printed tensile specimens with thermally insulated chamber was 26.4% higher than that of the specimens printed at room temperature and close to the annealed FFF specimens. A better interlayer connection was also observed in the specimens FFF printed with the proposed method.