Chapter 6 - Polymer and composites additive manufacturing: material extrusion processes

Among all the additive manufacturing (AM) technologies, the material extrusion-based AM is the most popular, and widely used for prototyping, rapid tooling, and direct part production in the current scenario. In material extrusion AM (MEAM), a solid form of material is converted to a semi-molten form and extruded out of a nozzle to deposit in a layer-by-layer manner. Nowadays MEAM three-dimensional (3D) printers are very popular among different application sectors. This motivates us to develop various commercial as well as open-source entry-level 3D printers such as the development of filament extruder 3D printers, pellet extruder 3D printer, or a combination of both. However, to develop or operate these printers, preliminary knowledge of various extrusion parameters is required to obtain the desired quality in the fabricated parts. Therefore, an initial attempt has been made to provide information related to various extrusion parameters such as definition, working range, and the effect of these parameters on performance measured. The impact of process parameters on the performance of either filament extrusion AM or pellet/granules extrusion AM has only been investigated in the studies available in the literature. However, this study provides a comprehensive review of the effects of process parameters on performance measurements for both types of extrusion-based AM processes. In this research work, along with a detailed review of previous works, a study of various process parameters, modeling, and methodology has been provided. The statistical modeling approach, histogram normal plots have been provided along with mean, standard deviation, and percentage to decide the optimum process parameters based on data available in the existing literature.

For the last few decades, the rapid growth of Additive Manufacturing (AM) technologies has been seeable. It is expected to keep maturing continuously due to its advantages compared to conventional manufacturing technologies: flexibility, reliability, energy consumption, and material efficiency. This research article addresses the development and production of a stapler using the Material Extrusion AM process. It is intended to show the development steps to redesign an everyday stapler, into an added-value tool, from the selection and fixture of the CAD model and generative design through Fusion 360 to its optimization on nTopology, simulation, and plot of the part in Eiger.

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

Digital manufacturing techniques, such as computer numerically controlled (CNC) machining and additive manufacturing (AM), have greatly increased the design space available to engineers with respect to both geometry and materials. Obviously, certain geometries cannot be realized by anything other than a layer-by-layer AM process, and certain materials can only be formed using traditional subtractive approaches, leaving designers with little choice. However, for the wide array of structures that could be realized in a variety of different ways, the choice of manufacturing process is just another faction in the overall design process. Planar lattice structures have complex repeating geometric features which are best designed using computer aided design (CAD) tools, and these structures can easily be manufactured using a variety of different digital manufacturing processes. For this study a set of planar lattice structures were designed and fabricated from acrylonitrile butadiene styrene (ABS) plastic using both a CNC milling machine and a material extrusion AM machine. All test lattices were then loaded in tension to failure to determine their mechanical performance. The determined properties were then compared to analytical predictions from the literature, and an analysis of the impact of a chosen manufacturing process was performed. The results obtained show good agreement with publish models for lattice performance, and additionally suggest that the choice of manufacturing process has a measurable effect on the mechanical performance of the resulting structures for this specific material and geometry combination, but it is important to note that this study is just an example of how to mitigate the uncertainties associated with the freedoms offered by advanced manufacturing processes, and more work in needed in this important area of study.

This work presents a decision-making methodology that allows the merging of quantitative and qualitative decision variables for selecting the optimal metal Additive Manufacturing (AM) technology. The approach is applied on two competing technologies in the field of metal AM industry, i.e., the metal extrusion AM process (metal FFF) and the Laser Powder Bed Fusion process (LPBF) with single and multiple lasers, which represent the benchmark solution currently on the market. A comprehensive techno-economical comparison is presented where the two processes are analysed in terms of process capabilities (quality, easiness of use, setup time, range of possible materials, etc.) and costs, considering two different production scenarios and different parts’ geometries. In the first scenario, the AM system is assumed to be dedicated to one single part production while in this second scenario, the AM system is assumed to be saturated, as devoted to producing a wide mix of part types. For each scenario, two different part types made of 17–4 PH stainless steel are considered as a reference to investigate the effect of shape complexity, part size and production times to select the best technology when metal FFF and LPBF must be considered. The first part type refers to an extrusion die, to represent typical shapes of interest in the tooling industry, while the second part type is an impeller which can be used in many different industrial sectors, ranging from oil and gas to aerospace. In order to include quantitative and qualitative criteria, a decision-making model based on Analytic Hierarchy Process (AHP) is proposed as the enabler tool for decision making. The proposed approach allows to determine the most effective solution depending on the different production configurations and part types and can be used as a guideline and extended to include other technologies in the field of metal AM. On the other side, the critical discussion of the criteria selected, and the results achieved allow to highlight the pros and cons of the competing technologies, thus defining the existing limits to define directions for future research.

Dimensional quality is still a major concern in additive manufacturing (AM) processes and its improvement is key to closing the gap between prototype manufacturing and industrialized production. Mass production requires the full working space of the machine to be used, although this arrangement could lead to location-related differences in part quality. The present work proposes the application of a multi-state machine performance perspective to reduce the achievable tolerance intervals of features of linear size in material extrusion (MEX) processes. Considering aspecific dimensional parameter, the dispersion and location of the distribution of measured values between different states are analyzed to determine whether the production should be treated as single-state or multi-state. A design for additive manufacturing strategy then applies global or local size compensations to modify the 3D design file and reduce deviations between manufactured values and theoretical values. The variation in the achievable tolerance range before and after the optimization of design is evaluated by establishing a target machine performance index. This strategy has been applied to an external MEX-manufactured cylindrical surface in a case study. The results show that the multi-state perspective provides a better understanding of the sources of quality variability and allows for a significant reduction in the achievable tolerance interval. The proposed strategy could help to accelerate the industrial adoption of AM process by reducing differences in quality with respect to conventional processes.

Improving mechanical performance in material extrusion parts via optimized toolpath planning

Designing efficient closed-loop control algorithms is a key issue in Additive Manufacturing (AM), as various aspects of the AM process require continuous monitoring and regulation, with temperature being a particularly significant factor. Here we study closed-loop control for the temperatures in the extruder of a Material Extrusion AM system, specifically a Big Area Additive Manufacturing (BAAM) system. Previous approaches for temperature control in AM either require the knowledge of exact model parameters, or involve discretisation of the state and action spaces to employ traditional data-driven control techniques. On the other hand, modern algorithms that can handle continuous state and action space problems require a large number of hyperparameter tuning to ensure good performance. In this work, we circumvent the above limitations by making use of a state space temperature model while focusing on both model-based and data-driven methods. We adopt the Linear Quadratic Tracking (LQT) framework and utilise the quadratic structure of the value function in the model-based analytical solution to produce a data-driven approximation formula for the optimal controller. We demonstrate these approaches using a simulator of the temperature evolution in the extruder of a BAAM system and perform an in-depth comparison of the performance of these methods. We find that we can learn an effective controller using solely simulated input–output process data. Our approach achieves parity in performance compared to model-based controllers and so lessens the need for estimating a large number of parameters of the often intricate and complicated process model. We believe this result is an important step towards achieving autonomous intelligent manufacturing.

PurposeMaterial extrusion (MEX) is a class of additive manufacturing (AM) process based on MEX principle. In the viewpoint of Industry 4.0 and sustainable manufacturing, AM technologies are gaining importance than conventional manufacturing route (subtractive manufacturing). Because of the ease of use and lesser operation skills, MEX had wide popularity in industry for product and prototype development. This study aims to analyze energy consumption of MEX-based AM process and its influencing factors.Design/methodology/approachA group of factors were identified pertaining to MEX-based AM process. In this viewpoint, this study presents the configuration of a structural model using interpretive structural modeling (ISM) to depict dominant factors in MEX-based AM process. A total of 18 influencing factors are identified and ranked using ISM methodology for MEX process. The Impact Matrix Cross-reference Multiplication Applied to a Classification analysis was done to categorize influencing factors into four groups for MEX-based AM process.FindingsThe derivation of structural model would enable AM practitioners to systematically analyze the factors and to derive key factors which enable comprehensive energy modeling and energy assessment studies. Also, it facilitates the development of energy efficient AM system.Originality/valueThe development of structural model for analysis of factors influencing energy consumption of MEX-based AM is the original contribution of the authors.

Fused Filament Fabrication (FFF) is a widely embraced material extrusion (MEX) additive manufacturing (AM) process to produce complex three-dimensional structures, and it is typically used in the fabrication of biodegradable polymers for biomedical applications. However, FFF as a fabrication process for blended polymeric materials needs to be optimized for enhanced mechanical properties. In this work, biodegradable polylactic acid (PLA)/polyhydroxyalkanoate (PHA) dog-bone and notched specimens are printed to determine optimum printing parameters for superior mechanical properties in FFF additive manufacturing. The effect of layer thickness, infill density, and printing bed temperature on mechanical properties is investigated by employing a design of experiments (DoE) approach using response surface methodology (RSM). Experimental results showed the significance of the opted parameters for mechanical properties of the PLA/PHA blend. Then, optimum values for layer thickness, infill density, and printing bed temperature are identified for tensile and impact strength and an empirical relationship between parameters is formulated for low density and cost-effective fabrication. Finally, the analysis of variance (ANOVA) is performed to check the adequacy of the model for the influence of process parameters and their mutual interactions. The verification experiments validated the adequacy of the proposed model for PLA/PHA blend in FFF additive manufacturing.

Laminated polymer composites have emerged as a promising class of materials that provide exceptional mechanical and functional properties owing to multilayered architectures. In addition, additive manufacturing (AM) offers boundless opportunities to fabricate complex and intricate geometries with a wide variety of materials. Utilizing AM processes for producing laminated polymer composites can open new pathways for producing these intricate structures with fine control over geometry, layer thickness, and material distribution. In this study, we demonstrate the use of the stereolithography (SLA) process to fabricate laminated polymer composites to overcome the limitations of extrusion-based AM processes, i.e., challenges in high precision, strong interlayer bonding and uniform particle distribution. Photocurable polymer composites were prepared by adding different reinforcing particles, i.e., cobalt iron oxide (CoFe2O4), graphene (G), magnesium (Mg) and iron (II,III) oxide (Fe3O4), into the photocurable resin. Ultrasonication and mechanical mixing processes were used to prepare stable photocurable composites suitable for the SLA process. SLA process was also optimized, varying the process parameters (exposure time, bottom exposure time and bottom layer count) to achieve optimum dimensional accuracy and surface quality. Microscopic analysis confirmed the distinct and well-adhered composite layer sandwiched between the unfilled resin, validating the structural integrity of the multilayer design. Mechanical testing revealed significant improvement in the tensile properties of the laminated composites compared to pure resin, with resin/CoFe2O4 exhibiting 35.6% and 50.1% improvement in tensile strength and Young’s modulus compared to the pure resin, respectively. These results highlight the feasibility of SLA for producing multilayered polymer composites with improved mechanical performance and controlled architecture, broadening its potential for advanced engineering and biomedical applications.

Graphene is emerging as an excellent material choice for high-performance electrical and electrochemical devices such as humidity sensors. The rapid technological evolution in additive manufacturing (AM) enables freedom to design and process multiple materials for scalable fabrication of sensors. Herein, we introduce a hybrid AM approach to fabricate a graphene-based humidity sensor and study its sensing performance. A powder-bed binder-jetting AM technique is used to build a porous 3D-structure of thermally reduced graphene oxide as the humidity sensing element. In parallel, a material extrusion AM approach is used to make a silicone-based hollow cube as the housing of the graphene structure. The AM fabricated sensor shows high sensitivity when tested in the relative humidity (RH) range of 6.4–97.3% with a rapid response time of 7 s at 45% RH due to the open porous structure formed by the binder-jetting approach. Sensing performance was investigated at low and medium RH in the temperature range of 25–80 °C and the device demonstrated a negligible temperature dependence. The presented graphene-based humidity sensor also shows good repeatability in RH measurements.

Although the additive manufacturing (AM) market continues to grow, industries face barriers to AM adoption due to a shortage of skilled designers in the workforce that can apply AM effectively to meet this demand. This shortage is attributed to the high cost and infrastructural requirements of introducing high- barrier-to-entry AM processes such as powder bed fusion (PBF) into in-person learning environments. To meet the demands for a skilled AM workforce, it is important to explore other mediums of AM education, such as computer-aided instruction (CAI) and virtual reality (VR), which can increase access to hands-on learning experiences for inaccessible AM processes. However, limited work compares virtual and physical AM instruction or explores how the differences in immersion and presence between mediums can affect the knowledge gained and the mental effort exerted when learning about different AM processes. To address this gap in the literature, this research evaluates the use of CAI, VR, and in-person instruction in AM process education when learning about material extrusion (ME) and PBF. Our findings show that the differences in immersion and presence between CAI, VR, and in-person instruction do not have a statistically significant effect when learning about ME, but do have a significant effect when learning about PBF. Specifically, we found that VR generally yields equivalent effects in knowledge gain and cognitive load to in-person PBF education while offering advantages in both metrics over CAI learning. The findings from this work thus have significant implications for using VR as an alternative to in-person training to improve designer development in process-centric AM education of typically high-barrier-to-entry AM processes.

Recently, Additive Manufacturing (AM) has been widely used in many applications. For a particular AM component, the choice of available AM processes is critical to the component’s quality, mechanical properties, and other important factors. In that context, this article presents an efficient decision support system for the selection of an appropriate AM process. A novel hybrid Multi-Criteria Decision Making (MCDM) technique has been proposed to select an appropriate AM process from available AM processes. The Best Worst Method (BWM) is used to determine optimal weights of criteria and the Proximity Indexed Value (PIV) method is employed to rank the available AM processes. For benchmarking the abilities of an AM process, a conceptual model of spur gear was fabricated by four available AM processes viz., Vat Photopolymerization (VatPP), Material Extrusion (ME), Powder Bed Fusion (PBF), and Material Jetting (MJ). Additionally, Dimensional accuracy (A), surface roughness (R), tensile strength (S), percentage elongation (%E), heat deflection temperature (HDT), process cost (PC) and build time (BT) has been considered as most significant criteria. Further, sensitivity analysis has been performed to validate the reliability of the results. The results suggested that the Material Jetting (MJ) process produces dimensionally accurate and quality parts among available alternatives AM processes. The ranking obtained using the PIV method is consistent and reliable.

Sinter-based metal additive manufacturing (AM) processes that decouple the geometric shaping and consolidation of AM have several advantages over melt-based AM technologies (one-step consolidation and shaping). The sinter-based AM technologies, led by binder jetting, has the potential to significantly increase the productivity and attain more isotropic microstructures in the final product. Sinter-based AM includes the ability to cover a wide spectrum of productivity, including prototyping, low volume serial production, and high-volume mass production. Recently, several non-traditional sinter-based metal AM processes (relatively less common) have emerged. This paper will review some of these emerging and lesser-known sinter-based metal AM technologies along with a few processes based on modifications of binder jet and material extrusion techniques that have gained traction.