Additive Manufacturing Methodology Research Articles

Deformation Evolution and Perceptual Prediction for Additive Manufacturing of Lightweight Composite Driven by Hybrid Digital Twins

This paper proposes a deformation evolution and perceptual prediction methodology for additive manufacturing of lightweight composite driven by hybrid digital twins (HDT). In order to improve manufacturing quality of irregular lightweight composite through boosting conceptual design in aeronautic and aerospace engineering, the HDT meaning hybridization of physical and digital domains, including deformation and energy efficiency can be built, where the essential parameters can be perceptually predicted in advance, by virtue of the fusion of physical sensors and digital information. The long short term memory (LSTM) can be employed to void vanishing gradient problem and improve predicting precision via Recurrent Neural Networks, thereby laying a foundation for the HDT. The diverse manufacturing requirements of different regions are integrated into the parameters designing phase by attaching region weights confirmed via empiricism and in-service simulation. The effects of slicing strategy and external support structures on manufacturing quality are considered from the perspective of improving dimensional accuracy. The manufacturing efficiency and comprehensive costs are accounted as consideration factors, which are perceptually predicted via LSTM. The designed manufacturing parameters through HDT were virtually examined by evaluating the deformation and equivalent stress distributions of fabricated lightweight component with composite material through AM process simulation. The physical experiments were conducted to verify the HDT-based pre-designing and optimization method of manufacturing parameters via fused deposition modeling (FDM). The energy consumption of actual manufacturing process was measured via digital power meter and applied to evaluate accuracy of perceptual prediction outcomes. The dimensional accuracy and distortion distribution of the manufactured lightweight prototype made with composite material were measured through the coordinate measuring machine (CMM) and 3D optical scanner. The proposed method demonstrates effectiveness in improving manufacturing quality and accurately predicting energy consumption, which have been verified with a three-way solenoid valve element, in which the maximum deformation was reduced by 39.78% and the mean absolute percentage error for perceptual prediction was 3.76%.

3D volume construction methodology for cold spray additive manufacturing

Cold spraying has proved as an attractive and rapidly developing solid-state material deposition process that allows for fast formation of high quality, large 3D volume objects. Low risks of undesirable heat effects lead to increased interest in cold spraying based rapid additive manufacturing. However, by continuous powder spraying and high-pressure gas operation, cold spray additive manufacturing in terms of shape building is sensitive to operating parameters and imposes high requirements on the control of process conditions and locally needed kinematics. Every step of the manufacturing process therefore needs to be meticulously conceived and planned, especially the toolpath planning and implementation. This is not only essential to meet basic performance requirements, but also needed for the desired geometric accuracy. To address these needs, this study presents a new implementation method for cold spray additive manufacturing to improve manufacturing accuracy and flexibility. The workflow and principles of the proposed method are explained and strategies are presented for building up various component types, including rotational geometries as well as freestanding parts. The developed algorithms for 3D applications can be well integrated into the cold spray additive manufacturing process for toolpath planning and path parameter determination. Reliable and accurate robot programs were developed to make the process easy to repeat and to follow. Toolpath planning and robot programming are supported and performed by applying virtual cells to simulate the automation process for the real scenario. Path parameter adjustment and kinematic optimization can then be conducted according to the feedback from the simulation result. Applied benchmarking tests prove acceptable shape accuracy and demonstrate that the current method can enhance the capabilities of cold spray additive manufacturing for near-net shape construction. This implies that careful planning and manufacturing strategies should enable overcoming the challenges associated with cold spray additive manufacturing.

Tailoring surface roughness through the temporal variation of additive manufacturing process parameters

The benefits that additive manufacturing (AM) offers to the industry are generally well understood and appreciated. However, the current design for additive manufacturing (DfAM) methodologies and computer-aided manufacturing (CAM) packages neglect to exploit the full potential that AM can offer through its unique ability to vary material characteristics whilst the final component geometry is being formed. The purpose of this research is to demonstrate that additional design control can be gained through temporal DfAM (TDfAM). In this study, the ability to tailor the surface roughness of fused deposition modelling (FDM) AM polylactic acid (PLA) parts through the variation of two process parameters, nozzle temperature and print speed, is explored. The underpinning hypothesis is that variation of temperature and printing speed, can provide a significant change of surface roughness within one homogeneous part. This research demonstrated that nozzle temperature and print speed have a statistically significant effect on the surface roughness of the top and side surfaces. By increasing temperature and speed, the roughness of the side surfaces decreased and the roughness of the top surface increased. Furthermore, the in-silico implementation of TDfAM is demonstrated. As such, the research supports the hypothesis that TDfAM can enable additional control over the surface characteristics of a homogeneous part.

Design and Optimization of a Gripper Clamp

Additive Manufacturing, among the many developing advanced manufacturing technologies, stands out as the one with the greatest potential for changing the distribution of manufacturing, society, and sustainability. To produce sustainable and competitive products, component material and design selection is an essential and critical topic in the industry. The production of parts designed using the Design for Additive Manufacturing methodology (DfAM) has grown in popularity in recent years. Topological optimization can be used as a design tool in the early stages of the design process to meet strength and endurance requirements on a component level. This study explores the topology optimization of a gripper clamp through nTopology and Fusion 360, using AISI 316L stainless steel as material, for possible production through Additive Manufacturing. The final component demonstrated reliable results.

Topology Optimization of Additive – Manufactured Autonomous Ground Vehicle Mount

Introduction: The existence of substantial amounts of comparatively unused material inside a certain section indicates a deficiency in resource utilization. The existence of stress concentrations leads to a decrease in both the rigidity and durability of the component Method: Using finite element analysis (FEA), additive manufacturing (AM), and topology optimization (TO) together in this work led to a new patent for making a lightweight part for an unmanned ground vehicle (UGV). Topology optimization is crucial for improving the performance of additive manufacturing and achieving design goals. The process of engineering design frequently leads to mechanical design being characterized by heightened complexity and improved efficiency, owing to its need for exploration and iteration. Employing additive manufacturing and topological optimization methodologies, we developed the optimal prototype. Using ANSYS software makes it possible to change the engineering principle of topology optimization so that it works with additive manufacturing. At the same time, we are doing a full evaluation to see if adding topology optimization is possible, taking into account its pros and cons. Result: The results of the study reveal that the proposed intervention has the potential to address other stress-related illnesses. The preliminary design proposal projected a cost of 400 Jordanian dinars (JD). Conclusion: However, through the process of optimization, we successfully achieved the ultimate design at a reduced cost of 200 JD. The revised design not only provides cost savings but also exhibits greater eco-friendliness.

Mechanical Properties, DMA and Structural Analysis of Cassia Auriculata/PLA Blended Hybrid bio Matrix Composite

In this study, hybrid composite material composed of Poly Lactic Acid (PLA) and Cassia Auriculata (CA) were fabricated and the mechanical properties were evaluated to assess the impact of bio filler loading in the bio polymer (PLA) matrix. In order to fabricate the composite specimens, the CA bio filler was added to the PLA matrix in proportions of 10%, 20%, 30%, 40% and 50% by volume. In line with ASTM specifications, experiments were conducted to establish how the hybrid bio matrix, which was composed of both PLA and CA influenced the composite s mechanical characteristics. The test results indicated that adding 30 percent CA to PLA enhanced the material s mechanical, dynamic mechanical analysis, thermogravimetric properties. The composite composition with better properties was considered for further fabrication of a colposcopy fixture, the component was fabricated using additive manufacturing methodology.

PARAMETRIC MODELING OF BIO-INSPIRED LUMINAIRES FOR ADDITIVE MANUFACTURING TECHNOLOGIES

The development of 3D modeling tools has allowed the construction of geometries that were unattainable computationally until a few decades ago. Specifically, parametric design and modeling is gaining significant importance in the conceptualization of new designs and products. This communication shows some of the resulting prototypes, designed and manufactured by INNANOMAT research group (TEP 946) of Cadiz University. A product design methodology for Additive Manufacturing (AM) and functional luminaire models proposed in the scope of two projects: ADICORK and 3DBLEND. In these projects, bio-inspired texture designs were explored in natural elements and environments of the province of Cádiz, in order to highlight this region and the value of its ecosystem. In both projects, sustainable polymeric blends were developed that served as base material for the manufacture of part of the prototypes shown in this communication. For these designs, parametric design programs such as Grasshopper, a Rhinoceros plugin, were used to obtain complex models that achieve a unique and differentiating aesthetic. The result of the modeled texturing is combined with surfaces in Rhinoceros. Once the textured model is defined, it is detailed and finished in modeling programs where dimensional control is greater, and the design is adjusted to the specific AM technology chosen. In this case two were used, Fused Filament Fabrication (FFF) and Fused Granular Fabrication (FGF). Keywords: Parametric design; Fused Granular Fabrication; Additive Manufacturing; product design; Biomimicry

Embedded 3D Bioprinting for Engineering Miniaturized In Vitro Tumor Models.

Embedded extrusion 3D bioprinting is a rapidly emerging additive manufacturing methodology that provides a precise spatial deposition of synthetic or natural-origin low-viscosity bioinks during the extrusionprinting process. Such a strategy has to date unlocked the freeform extrusion biofabrication of complex micro-to-macro-scale living architectures for numerous applications, including tissue engineering and in vitro disease modeling. In this chapter, we describe a suspension bioprinting methodology leveraging a continuous viscoelastic biopolymer supporting bath functionalized with divalent calcium cations to enable a rapid processing of user-defined bioinks toward architecturally complex 3D in vitro tumor models. This highly simple and cost-effective viscoelasticsupporting bath enables a full freeform biofabrication of cell-laden 3D tumor-mimetic architectures that exhibit structural stability in culture post-printing. The cytocompatibility of the supporting bath, its ease of removal from biofabricated living constructs, and its adaptability for processing different ECM-mimetic bioinks open avenues for multi-scale fabrication of numerous types of physiomimetic 3D tumor models for preclinical screening of candidate therapeutics.

S-FMECA Based Collaborative Design Proposal for Additive Manufacturing Methodology

S-FMECA Based Collaborative Design Proposal for Additive Manufacturing Methodology

A Novel Hybrid Additive Manufacturing Methodology for the Development of Ti6Al4V Parts

A Novel Hybrid Additive Manufacturing Methodology for the Development of Ti6Al4V Parts

Realization and Control of Bulk and Surface Modes in 3D Nanomagnonic Networks by Additive Manufacturing of Ferromagnets.

The high-density integration in information technology fuels the research on functional 3D nanodevices. Particularly ferromagnets promise multifunctional 3D devices for nonvolatile data storage, high-speed data processing and non-charge-based logic operations via spintronics and magnonics concepts. However, 3D nanofabrication of ferromagnets is extremely challenging. In this work, wereport an additive manufacturing methodology and fabricate unprecedented 3D ferromagnetic nanonetworks with a woodpile-structure unit cell. Weinvestigate their collective spin dynamics (magnons) at frequencies up to 25 GHz by Brillouin Light Scattering (BLS) microscopy and micromagnetic simulations. A clear discrepancy of about 10 GHz is found between the bulk and surface modes which weengineer by different unit cell sizes in the Ni-based nanonetworks. The angle- and spatially-dependent modes demonstrate opportunities for multi-frequency signal processing in 3D circuits via magnons. The developed synthesis route will allow one to create 3D magnonic crystals with chiral unit cells which are a prerequisite towards surface modes with topologically protectedproperties. This article is protected by copyright. All rights reserved.

Understanding the Effects of Process Conditions on Thermal–Defect Relationship: A Transfer Machine Learning Approach

AbstractThis study aims to develop an intelligent, rapid porosity prediction methodology for additive manufacturing (AM) processes under varying process conditions by leveraging knowledge transfer from the existing process conditions. Conventional machine learning (ML) algorithms are extensively used in porosity prediction for AM processes. These approaches assume that the underline distribution of the source (training) and target (testing) is the same and that target labels are available for modeling purposes. However, the source and target sometimes follow different distributions in real-world manufacturing environments as the diversity of industrialization processes leads to heterogeneous data collection under different production conditions. This will reduce the ability of decision-making with conventional approaches. Transfer learning (TL) is one of the robust techniques that enables transferring learned knowledge between the target and source to establish a robust relationship while the target has fewer data. Therefore, this paper presents an unsupervised grouping-based transfer learning method to characterize the relationship between an unknown target and sources. The similarities between sources and targets are learned by forming a new mixed domain, which organizes data into identity groups. Then, a group-based learning process is designated to transfer knowledge to make target predictions. The effectiveness of the proposed method is evaluated by predicting porosity based on thermal images collected from the AM process under different process conditions, i.e., single-source and multi-source transfer to target porosity prediction. The performance comparison demonstrates that the in situ porosity prediction using the proposed method outperformed state-of-art classification models support vector machine (SVM), convolutional neural network (CNN), and different TL methods such as TL with NNs (TLNN), and TL with CNNs (TLCNN).

Influence of resin composition on rheology and polymerization kinetics of alumina ceramic suspensions for digital light processing (DLP) additive manufacturing

Alumina ceramics with optimized microstructures and mechanical properties were obtained by the attractive digital lighting processing (DLP) additive manufacturing methodology in the present study. A acrylate-based resin system was designed for the alumina powders with a mean particle size of 0.5 μm. The influence of oligomer on the viscosity and polymerization kinetics of the ceramic suspensions has been elaborately discussed by rheology, curing depth and photo-DSC characterizations. The results indicated that the introduction of oligomer has improved the cross-linking density of resins and decreased the critical dose of energy for resin polymerization, which contributed to a tougher ceramic-resin slice with higher dimensional accuracy. Densifying processes including debinding and high temperature sintering of the ceramic parts were conducted according to the TG-DTA characterizations, alumina ceramics with uniform microstructures and eliminated delamination or intralaminar cracks were finally obtained. The flexural strength was 471 MPa for the ceramics obtained from the resin composition containing 20 wt% oligomer, Weibull modulus for the ceramics were determined to be 17.31 by evaluating thirty all sides polished ceramics, indicating the highly uniform property of the ceramics fabricated by DLP additive manufacturing.

A collaborative multidisciplinary design methodology for additive manufacturing with a left-handed mouse as a case study

A collaborative multidisciplinary design methodology for additive manufacturing with a left-handed mouse as a case study

Design De-Identification of Thermal History for Collaborative Process-Defect Modeling of Directed Energy Deposition Processes

Abstract There is an urgent need for developing collaborative process-defect modeling in metal-based additive manufacturing (AM). This mainly stems from the high volume of training data needed to develop reliable machine learning models for in-situ anomaly detection. The requirements for large data are especially challenging for small-to-medium manufacturers (SMMs), for whom collecting copious amounts of data is usually cost prohibitive. The objective of this research is to develop a secured data sharing mechanism for directed energy deposition (DED) based AM without disclosing product design information, facilitating secured data aggregation for collaborative modeling. However, one major obstacle is the privacy concerns that arise from data sharing, since AM process data contain confidential design information, such as the printing path. The proposed adaptive design de-identification for additive manufacturing (ADDAM) methodology integrates AM process knowledge into an adaptive de-identification procedure to mask the printing trajectory information in metal-based AM thermal history, which otherwise discloses substantial printing path information. This adaptive approach applies a flexible data privacy level to each thermal image based on its similarity with the other images, facilitating better data utility preservation while protecting data privacy. A real-world case study was used to validate the proposed method based on the fabrication of two cylindrical parts using a DED process. These results are expressed as a Pareto optimal solution, demonstrating significant improvements in privacy gain and minimal utility loss. The proposed method can facilitate privacy improvements of up to 30% with as little as 0% losses in dataset utility after de-identification.

Systematic Review of Difference Between Topology Optimization and Generative Design

This paper aims to study the concept and highlight the methodical difference between the results acquired by design for additive manufacturing methodologies: Topology Optimization (TO) and Generative Design. By meeting particular requirements and reducing a specified cost function, topology optimization (TO) is a mathematical technique that spatially optimizes the distribution of material inside a particular domain which is frequently mistaken for generative design. Software providers claim that generative design is more holistic because it is based on part requirements and restrictions and considers the design, manufacturing process, function, and many other crucial factors. TO as a classical approach has been around for over three decades and due to additive manufacturing, interest in TO is re-established. whereas generative design is an innovative approach that has recently gained acceptance through Computer-Aided Design (CAD) software. The bracket engine problem from General Electric is used as a case study to show how the TO and generative design approaches differ. Examples from earlier literature are taken into account for TO, and Autodesk Fusion 360 (educational license) software is used to produce the outputs for generative design. After carefully examining the entire procedure and the outcomes, a comparison chart is added to highlight the key distinctions between the two approaches.

Additive manufacturing of titanium alloys – Enabling re-manufacturing of aerospace and biomedical components

This paper describes the state of the art of additive manufacturing of Titanium alloys especially in the high-value-materials industries such as aerospace and biomedical. In such industries, the high value components like aerospace gas engine turbine blades or the coronary angioplasty stents in biomedical industry call especially for the re-manufacturing vision that is only recently enabled by additive manufacturing methodologies. This paper starts by describing the two most common additive manufacturing (AM) methods as characterized by their input material forms: powder and wire. Using examples, it then provides an assessment of the status of these methods as regards to their maturity as production ready processes. Following this, the principal issues that must be addressed before AM can achieve this status are outlined. Perhaps the most critical one among these is development of a rapid qualification method for AM produced components. In this regard, the current method for qualifying different classes of components is outlined. Some suggestions for reducing the time required for qualification are made. Significant benefits of AM are described including part count reduction and the use of design synthesis to achieve significant component weight reduction. Two low risk examples of this are described herein. The Direct Energy Deposition (DED) model is highlighted especially in its role to enable the Re-Manufacturing vision for high-value structural components such as in aerospace and biomedical industries. This paper concludes that there are numerous opportunities for incorporation of components made by AM once some of the issues described are effectively addressed.

Battery anode design: From 1D nanostructure to 3D nanoarchitecture – Enabling next-generation energy storage technology

Recently, many studies have reported the efficacy of 1D silicon nanostructures as anode materials (mostly nanowires) due to their large surface to volume ratio which could be an advantage in terms of charging kinetics. Anode design is a crucial factor for the further development of lithium-ion battery (LIB) technology, especially in terms of achieving the high charging rate performance, as typically needed in electric vehicles (EV). 3D nanostructures have been identified as the key to dramatically enhanced charging rate of LIB, in addition to the change of materials (from carbon-based anode to silicon-based). However, existing fabrication methodologies for 3D nanostructures are inherently expensive – using advanced lithography techniques or other nanopatterning methodologies, as well as using additive manufacturing approaches. Electrospinning (as an integrated additive manufacturing methodology) may be used to produce low-cost 3D-printed fiber structures. In the present study, we demonstrate the groundwork in this field of research in the past 3–4 years, which would allow the extension of this special additive manufacturing approach to reach nanoscale 3D-printed fiber structures more economically and with scalability for manufacturing. This would pave the way to achieve the order of magnitude increase in battery charging rate as needed to realize the vision of the electrification of mobility/transportation.

Validation of the Mechanical Behavior of an Aeronautical Fixing Turret Produced by a Design for Additive Manufacturing (DfAM).

The design of parts in such critical sectors as the manufacturing of aeronautical parts is awaiting a paradigm shift due to the introduction of additive manufacturing technologies. The manufacture of parts designed by means of the design-oriented additive manufacturing methodology (DfAM) has acquired great relevance in recent years. One of the major gaps in the application of these technologies is the lack of studies on the mechanical behavior of parts manufactured using this methodology. This paper focuses on the manufacture of a turret for the clamping of parts for the aeronautical industry. The design of the lightened turret by means of geometry optimization, the manufacture of the turret in polylactic acid (PLA) and 5XXX series aluminum alloy by means of Wire Arc Additive Manufacturing (WAAM) technology and the analysis by means of finite element analysis (FEA) with its validation by means of a tensile test are presented. The behavior of the part manufactured with both materials is compared. The conclusion allows to establish which are the limitations of the part manufactured in PLA for its orientation to the final application, whose advantages are its lower weight and cost. This paper is novel as it presents a holistic view that covers the process in an integrated way from the design and manufacture to the behaviour of the component in use.

IdeAM Running Quiz: A Digital Learning Game to Enhance Additive Manufacturing Opportunities Discovery

This paper provides a description of IdeAM Running Quiz, a learning game aiming at enhancing Additive Manufacturing (AM) opportunities learning. Firstly, a review of the literature on the Design and Additive Manufacturing methodologies and the educational effectiveness of learning games is presented. Then, the steps involved in the design of IdeAM Running Quiz game are introduced. IdeAM Running Quiz is a digital learning game inspired by arcade games. It is an infinite race where the play-er will have to choose the right path that will allow him to advance. It features exam-ples of objects that can be made with AM as well as AM opportunities. For this research, we have conducted an experiment aiming at evaluating the effectiveness of IdeAM Running Quiz on AM opportunities learning, and satisfaction provided by playing it. This experiment has shown that IdeAM Running Quiz helps player to understand the additive manufacturing opportunities. The experiment has also shown that participants like to play IdeAM Running Quiz and would recommend it for someone who want to discover the AM opportunities.