Activity-based Cost Model for Material Extrusion Processes Along the Additive Manufacturing Process Chain

Additive manufacturing (AM), also known as 3D printing, has revolutionized the landscape of advanced manufacturing. Among various AM techniques, material extrusion (MEX) stands out for its exceptional design freedom, unmatched potential for customization, and significantly reduced product development times. Thermoplastic materials are deposited layer-by-layer in a meticulously controlled manner, enabling the creation of intricate geometries and functional prototypes with remarkable ease. This process simplicity, coupled with the vast array of available materials, has cemented MEX’s position as the most widely adopted AM technique. However, the success of MEX hinges on a critical factor: the precise extrusion and subsequent deposition of material onto the printing surface. Inconsistencies in this process can lead to a multitude of defects, compromising the final product’s structural strength, aesthetics, and functionality. Unfortunately, current limitations exist in real-time monitoring and error detection within MEX processes. Traditional methods often rely on manual inspections, which are time-consuming, prone to human error, and incapable of immediate corrective actions. This research addresses this critical gap by proposing a novel approach that leverages the power of machine learning and cost-effective vision systems. A customized camera module was installed on the MEX extruder, providing a clear view of the localized material deposition area. This strategic placement allowed the camera to capture high-resolution images under various process parameters and layer heights. Flow rate and z-height platform offset were first identified as the primary factors influencing MEX’s material extrusion quality. The total of 291,444 captured images, from different combinations of the flow rates and z-height offsets, formed the foundation for training a modified deep learning algorithm (DL) with convolutional neural network (CNN) and multioutput classification capabilities. By meticulously analyzing the vast dataset of captured images, the proposed DL model learned to identify subtle variations and patterns associated with different material deposition scenarios. This empowered the algorithm to predict multiple process parameter values simultaneously. The trained model demonstrated remarkable performance in real-time prediction of the critical process parameters in a separate test dataset. These exceptional results showcased the immense potential of this novel approach in ensuring consistent and high-quality material deposition within MEX processes. This research presents an effective approach that harnesses the combined power of machine learning and vision systems to revolutionize MEX processes. This work offers a significant advancement over existing literature in material extrusion quality categorical prediction. The proposed system surpasses prior approaches by directly inferring process parameters with high accuracy. This capability paves the way for the development and implementation of automated error detection and correction systems, offering a novel approach for real-time quality control in material extrusion processes. Such advancements will not only minimize process inconsistencies and improve product quality but also unlock the potential for increased automation and intelligence within MEX.

Additive manufacturing (AM) processes, such as material extrusion, are part of a popular growing technology field; with limited human assistance required and advanced rapid prototyping capabilities, this technology is advertised to have limitless possibilities. The common challenge faced by users is a lack of design control of the surface roughness, which is highlighted by a characteristic “stair case” layering effect at the boundary. Focusing on material extrusion processes, the goal of this research is to model representative bead shapes, and highlight the surface roughness challenges for assessing boundary-fill regions. Various bead shapes are explored and compared through virtual simulation. Unique material extrusion AM-related issues arise, and it is shown that machining solutions may not provide the desired surface smoothness. This research illustrates that specific physical and virtual assessment tools and standards need to be further developed to convey surface roughness attributes for material extrusion additive manufactured components.

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

Additive manufacturing (AM) has garnered significant attention because of its numerous benefits and transformative potential across industries, particularly in internet of things manufacturing systems. The application of AMs with gravitational acceleration has considerable scope for exploration. Material extrusion (MEX) is a versatile technology, to evaluate the effects of high gravitational forces on MEX, an experimental approach involving controlled gravitational acceleration up to 5G was implemented using a specialized high-gravitational material extrusion system. This system, leveraging centrifugal acceleration, facilitated a comprehensive assessment of material flow dynamics and resultant surface quality under varying gravitational conditions. The results clearly indicate that applying high gravitational acceleration, reaching up to 5G, notably improves the material extrusion quality and appearance. Gravity affects the flow of materials during the extrusion process, high gravity can help achieve better material deposition. The experimental assessments clearly demonstrate that a high gravitational field enhances the surface quality and appearance of fabricated parts.

The development of sustainable and functional biocomposites remains a robust research and industrial claim. Herein, the efficiency of using eco-friendly biochar as reinforcement in Additive Manufacturing (AM) was investigated. Two AM technologies were applied, i.e., vat photopolymerization (VPP) and material extrusion (MEX). A standard-grade resin in VPP and the also eco-friendly biodegradable Polylactic Acid (PLA) in the MEX process were selected as polymeric matrices. Biochar was prepared in the study from olive trees. Composites were developed for both 3D printing processes at different biochar loadings. Samples were 3D-printed and mechanically tested after international test standards. Thermogravimetric Analysis and Raman revealed the thermal and structural characteristics of the composites. Morphological and fractographic features were derived, among others, with Scanning Electron Microscopy (SEM) and Atomic Force Microscopy (AFM). Biochar was proven to be sufficient reinforcement agent, especially in the filament MEX process, reaching more than 20% improvement at 4 wt.% loading in tensile strength compared to the pure PLA control samples. In the VPP process, results were not as satisfactory, still, a 5% improvement was achieved in the flexural strength with 0.5 wt.% biochar loading. The findings prove the strong potential of biochar-based composites in AM applications, too.Graphical

The additive manufacture (AM) of plastic components can be accomplished by a variety of methods which are classified according to how material layers are consolidated layer-by-layer to create physical objects from digital data. Two of the most widely used methods include Material Extrusion (MEX) and Vat Photopolymerization (VP). These methods include a range of commercialized AM processes often referred to by various trademarked terms such as Fused Deposition Modelling (FDM) and Stereolithography (SLA). Compared to conventional subtractive or formative manufacturing process, MEX and VP are able to manufacture complex parts with high ability for customization, as they impose few constraints on part geometry, and require low setup effort with no custom tooling. Furthermore, MEX and VP are both well-established additive manufacturing processes and through ongoing refinement have achieved compatibility with a broad range of materials and part geometries, and comparatively low operating costs. Due to their high versatility, MEX and VP have been widely used in a broad range of industries including applications in chemical sciences, biotechnology, aerospace, defense, and automotive engineering. However, despite their high versatility, AM processes such as MEX and VP are subject to unique technological characteristics associated with the manufacturing process, the material properties, part design and suitable application areas. This chapter provides an overview of MEX and VP processes characteristics critical to the effective application of these additive manufacturing technologies to high performance products.KeywordsAdditive manufacturingDesign for additive manufactureMaterial extrusionPhotopolymerizationPolymersThermoplasticsVat photopolymerization

The present study permeates the frontier of additive manufacturing (AM) by extruding composite materials with continuous reinforcement (Continuous fibers reinforced polymer - CFRP). The growing demand for the development of composites with greater capacity for mechanical resistance due to relative density that meet the needs of projects in the aeronautical and automobile industries, for example, has fostered research around the world on this technology. The current world scenario of technology still has a small number of startups and companies, thus resulting in the opportunity to study and apply it in Brazil. The current stage of CFRP AM by extrusion of material in desktop printers with Cartesian axis presents a fiber volumetric fraction (Vf) of 30%, non-homogeneous reinforcement distribution and the presence of voids. This research aimed to develop and characterize a composite filament of thermoplastic PLA matrix and hybrid reinforcement of short carbon fibers and continuous Kevlar29. With the purpose of application in AM by material extrusion, PLA, short carbon fibers, Kevlar29 and the composites produced were characterized by FTIR-ATR, TGA, DSC, mechanical tensile test, XRD, SEM and µCT. The TGA and DSC results indicated the ideal drying temperature for Kevlar29, where the present humidity triggered porosity. SEM and µCT demonstrated the level of impregnation obtained in the manufacture of the composite, which, due to the absence of internal pressure control in the coating chamber, was limited to the external continuous fibers. FTIR-ATR, TGA, DSC, and XRD indicated that the crystalline structure of the matrix and reinforcement were not altered, indicating the non-formation of adhesion between the phases of the composite. Mechanical tensile performance demonstrated premature failure of the matrix to separate from the reinforcement. Improvement processes were identified, such as pre-drying the continuous reinforcement and adjusting the sealing of the coating chamber nozzles. The studies pointed out that the result of the manufacture of the composite is in the TRL4 stage of technological development, which means testing the product in the laboratory.

Tools for implementing a systematic quality management are necessary for the use of material extrusion as an additive manufacturing process for products with high quality requirements. Well-defined quality classes are crucial for ensuring that the requirements for a product can be communicated transparently and that the existing properties can be evaluated. Furthermore, there is a lack of capable measurement equipment for the acquisition of process data during the production process. To address these challenges, the present paper introduces an image processing system that determines quality indicators for individual layers in terms of imperfect surface percentages and the number of imperfections. The central element of the hardware is an adaptive darkfield illumination, which leads to high-contrast images. In addition, five types of layer subareas are identified in a segmentation step. Unsupervised machine learning methods are then used to detect imperfections in each layer subarea. In the segmentation, the current layer can be distinguished from irrelevant image background regions with an F-measure of 0.981. For the layer-wise measurement of the quality indicators, relative measurement errors with standard deviations of 25 to 76.1% are found. After evaluating the capabilities of the image processing system, a proposal for limits of quality classes is derived by monitoring several material extrusion processes. For this purpose, three quality classes for each of the five layer subareas are deduced from the process scatter measured by the image processing system. The results are an important contribution to the industrialization of material extrusion in safety–critical areas such as medical technology or the aerospace industry.

Custom-form iron trifluoride Li-batteries using material extrusion and electrolyte exchanged ionogels

Personalization and Precision: Innovative Applications and Future Challenges of Additive Manufacturing in Orthopedic Implants.

With the development of wearable electronic devices, conductive polymer composites (CPCs) based flexible strain sensors are gaining tremendous popularity. In recent years, the applications of additive manufacturing (AM) technology (also known as 3D printing) in fabricating CPCs based flexible strain sensors have attracted the attention of researchers due to their advantages of mold-free structure, low cost, short time, and high accuracy. AM technology, based on material extrusion, photocuring, and laser sintering, produces complex and high-precision CPCs based wearable sensors through layer-by-layer stacking of printing material. Some high-performance CPCs based strain sensors are developed by employing different 3D printing technologies and printing materials. In this mini-review, we summarize and discuss the performance and applications of 3D printed CPCs based strain sensors in recent years. Finally, the current challenges and prospects of 3D printed strain sensors are also discussed to provide an insight into the future of strain sensors using 3D printing technology.

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.

The rapid expansion of Additive Manufacturing (AM) technologies within the framework of Industry 4.0 raises important questions about their sustainability. Specifically, the widespread use of Material Extrusion (MEx) processes for polymeric materials necessitates a thorough evaluation of the possible balance between performance efficiency and sustainability, highlighting the need for further research into optimizing process parameters to ensure high product quality and reduced energy consumption. The present work focuses on Fused Filament Fabrication (FFF) technology, investigating the relationship between sustainability and print quality. A causal technology model is proposed to elucidate this relationship, underlining the impact of process parameters on defect generation and energy use. A real-time monitoring system for energy consumption measurements was employed to create an energy model that accounts for each component of the CreatBot F430 printer. An image analysis procedure was performed on a special configuration of the HIROX RH-2000 digital microscope to determine the void fraction of each sample. An experimental campaign was conducted, producing single-layer samples in polylactic acid (PLA) following a full factorial plan with four process parameters. A void fraction of up to 18 % was observed, with energy consumption per sample ranging between 638 J and 8843 J. Statistical analysis was performed to assess the impact of each process parameter and to determine operational ranges through the Response Surfaces Method. These results showed good agreement with the predicted conditions, demonstrating the model's effectiveness in supporting decision-making processes in technology selection.

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.