Enhancing Structural Performance of Short Fiber Reinforced Objects through Customized Tool-Path

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

The goal of this work is to predict the effect of part geometry and process parameters on the instantaneous spatiotemporal distribution of temperature, also called the thermal field or temperature history, in metal parts as they are being built layer-by-layer using additive manufacturing (AM) processes. In pursuit of this goal, the objective of this work is to develop and verify a graph theory-based approach for predicting the temperature distribution in metal AM parts. This objective is consequential to overcome the current poor process consistency and part quality in AM. One of the main reasons for poor part quality in metal AM processes is ascribed to the nature of temperature distribution in the part. For instance, steep thermal gradients created in the part during printing leads to defects, such as warping and thermal stress-induced cracking. Existing nonproprietary approaches to predict the temperature distribution in AM parts predominantly use mesh-based finite element analyses that are computationally tortuous—the simulation of a few layers typically requires several hours, if not days. Hence, to alleviate these challenges in metal AM processes, there is a need for efficient computational models to predict the temperature distribution, and thereby guide part design and selection of process parameters instead of expensive empirical testing. Compared with finite element analyses techniques, the proposed mesh-free graph theory-based approach facilitates prediction of the temperature distribution within a few minutes on a desktop computer. To explore these assertions, we conducted the following two studies: (1) comparing the heat diffusion trends predicted using the graph theory approach with finite element analysis, and analytical heat transfer calculations based on Green’s functions for an elementary cuboid geometry which is subjected to an impulse heat input in a certain part of its volume and (2) simulating the laser powder bed fusion metal AM of three-part geometries with (a) Goldak’s moving heat source finite element method, (b) the proposed graph theory approach, and (c) further comparing the thermal trends predicted from the last two approaches with a commercial solution. From the first study, we report that the thermal trends approximated by the graph theory approach are found to be accurate within 5% of the Green’s functions-based analytical solution (in terms of the symmetric mean absolute percentage error). Results from the second study show that the thermal trends predicted for the AM parts using graph theory approach agree with finite element analyses, and the computational time for predicting the temperature distribution was significantly reduced with graph theory. For instance, for one of the AM part geometries studied, the temperature trends were predicted in less than 18 min within 10% error using the graph theory approach compared with over 180 min with finite element analyses. Although this paper is restricted to theoretical development and verification of the graph theory approach, our forthcoming research will focus on experimental validation through in-process thermal measurements.

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

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

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

The design methodologies and part shape algorithms for additive manufacturing (AM) are rapidly growing fields, proven to be of critical importance for the uptake of additive manufacturing of parts with enhanced performance in all major industrial sectors. The current trend for part design is a computationally driven approach where the parts are algorithmically morphed to meet the functional requirements with optimized performance in terms of material distribution. However, the manufacturability restrictions of AM processes are not considered at the primary design phases but at a later post-morphed stage of the part’s design. This paper proposes an AM design method to ensure: (1) optimized material distribution based on the load case and (2) the part’s manufacturability. The buildability restrictions from the direct energy deposition (DED) AM technology were used as input to the AM shaping algorithm to grant high AM manufacturability. The first step of this work was to define the term of AM manufacturability, its effect on AM production, and to propose a framework to estimate the quantified value of AM manufacturability for the given part design. Moreover, an AM design method is proposed, based on the developed internal stresses of the build volume for the load case. Stress tensors are used for the determination of the build orientation and as input for the part morphing. A top-down mesoscale geometric optimization is used to realize the AM part design. The DED Design for Additive Manufacturing (DfAM) rules are used to delimitate the morphing of the part, representing at the same time the freeform mindset of the AM technology. The morphed shape of the part is optimized in terms of topology and AM manufacturability. The topology optimization and AM manufacturability indicator (TMI) is introduced to screen the percentage of design elements that serve topology optimization and the ones that serve AM manufacturability. In the end, a case study for proof of concept is realized.

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

Material characterization and precise finite element analysis of fiber reinforced thermoplastic composites for 4D printing

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

Cracks and pores are two common defects in metallic additive manufacturing (AM) parts. In this paper, deep learning-based image analysis is performed for defect (cracks and pores) classification/detection based on SEM images of metallic AM parts. Three different levels of complexities, namely, defect classification, defect detection and defect image segmentation, are successfully achieved using a simple CNN model, the YOLOv4 model and the Detectron2 object detection library, respectively. The tuned CNN model can classify any single defect as either a crack or pore at almost 100% accuracy. The other two models can identify more than 90% of the cracks and pores in the testing images. In addition to the application of static image analysis, defect detection is also successfully applied on a video which mimics the AM process control images. The trained Detectron2 model can identify almost all the pores and cracks that exist in the original video. This study lays a foundation for future in situ process monitoring of the 3D printing process.

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

Granta: Materials Intelligence (Granta: MI) is a commercial database software distributed by Ansys, Inc. that is utilized by the Nuclear Security Enterprise (NSE) to organize and store relevant materials data. Lack of standard and well-documented database schema is the primary obstacle to an NSE materials data management solution, so the objective of this project is to create and document such a schema. In FY21, an approach for designing, documenting, and managing a standard database schema was described based on the creation of schema elements (collections of attributes used to describe particular aspects of the data) to be used as building blocks for creating various database tables without duplication. In FY22, these methods were applied through a multi-site collaboration to create and document the schema elements necessary to build a thermogravimetric analysis (TGA) testing table. In FY23 the schema was expanded to include elements for a differential scanning calorimetry (DSC) table, along with schema for supporting metadata tables including Instruments, Projects, Documents, and Testing Series. In FY24 the following progress was made, again through multi-site collaboration: • The existing schema elements were modified to accommodate thermomechanical analysis (TMA) data, and a table, Test Data: TMA, was created for managing TMA data. • The elements necessary for the following additive manufacturing (AM) data tables (directed at data specific to selective laser sintering AM technology) were created: • AM Builds • AM Processes • AM Part Designs • Built AM Parts • AM Feedstock Materials • AM Feedstock Material Batches • The elements necessary for creating a Calibrated Material Models table were created, and the Calibrated Material Models table was created. In FY25 the existing schema will be deployed on the production enterprise Granta instance on the enterprise secure network. Schema elements will be appended, and new elements created as necessary, to allow the creation of tables specifically to support materials testing, AM process development, and design and analysis for modernization programs.

Additive manufacturing, an innovative process that assembles materials layer by layer from 3D model data, is recognized as a transformative technology across diverse industries. Researchers have extensively investigated the impact of various printing parameters of 3D printing machines, such as printing speed, nozzle temperature, and infill, on the mechanical properties of printed objects. Specifically, this study focuses on applying Finite Element Analysis (FEA) in G code modification in Fused Deposition Modeling (FDM) 3D Printing. FDM involves extruding a thermoplastic filament in layers over a build plate to create a three-dimensional object. In the realm of load-bearing structures, the Finite Element Analysis (FEA) process is initiated on the target object, employing the primary load to identify areas with high-stress concentrations. Subsequently, optimization techniques are used to strategically assign printing parameter combinations to improve mechanical properties in potentially vulnerable regions. The ultimate objective is to tailor the G code, a set of instructions for the printer, to strengthen particular areas and improve the printed object’s overall structural integrity. To evaluate the suggested methodology’s efficacy, the study conducts a comprehensive analysis of printed objects, both with and without the optimized G code. Simultaneously, mechanical testing, such as tensile testing, demonstrates quantitative data on structural performance. This comprehensive analysis aims to identify the impact of G code alteration on the finished product. Preliminary experimental results using simple tensile specimens indicate notable improvements in structural performance. Importantly, these improvements are achieved without any discernible mass increase, optimizing material usage and reducing the cost of additive manufacturing. The modified G code targets to strengthen critical areas using updated printing parameters without a net increase in the overall material consumption of the object. This finding holds significant implications for industries reliant on additive manufacturing for load-bearing components, offering a promising avenue for improved efficiency and durability. Integrating advanced techniques, such as G code modification and finite element analysis (FEA), as the additive manufacturing landscape evolves presents a pathway toward optimizing mechanical properties. By contributing valuable insights and laying the groundwork for further exploration and refinement of these methodologies, this study paves the way for enhanced structural performance in various additive manufacturing applications. Ultimately, it encourages innovation and progress in the field, propelling the industry toward new heights of efficiency and reliability.

The objective of this work is to develop and apply a spectral graph theoretic approach for differentiating between (classifying) additive manufactured (AM) parts contingent on the severity of their dimensional variation from laser-scanned coordinate measurements (3D point cloud). The novelty of the approach is in invoking spectral graph Laplacian eigenvalues as an extracted feature from the laser-scanned 3D point cloud data in conjunction with various machine learning techniques. The outcome is a new method that classifies the dimensional variation of an AM part by sampling less than 5% of the 2 million 3D point cloud data acquired (per part). This is a practically important result, because it reduces the measurement burden for postprocess quality assurance in AM—parts can be laser-scanned and their dimensional variation quickly assessed on the shop floor. To realize the research objective, the procedure is as follows. Test parts are made using the fused filament fabrication (FFF) polymer AM process. The FFF process conditions are varied per a phased design of experiments plan to produce parts with distinctive dimensional variations. Subsequently, each test part is laser scanned and 3D point cloud data are acquired. To classify the dimensional variation among parts, Laplacian eigenvalues are extracted from the 3D point cloud data and used as features within different machine learning approaches. Six machine learning approaches are juxtaposed: sparse representation, k-nearest neighbors, neural network, naïve Bayes, support vector machine, and decision tree. Of these, the sparse representation technique provides the highest classification accuracy (F-score > 97%).

Opportunities are offered by multiple Additive Manufacturing (AM) processes nowadays. Design rules are evolving to lead to lighter and stiffer parts with really more complex shapes than those obtained by conventional processes. Worldwide, new methodologies/tools of assistance for the design are developed such as Design for Additive Manufacturing (DfAM). Additive manufacturing can allow the development of new metamaterials and health-matter evaluation based on energy flow evaluation. In this paper, the objective is to generate a new methodology with DfAM based on mesoscale knowledge. It is generated with open lab bench and simple object characterization. A methodology is presented to formalize and quantify information at multilayer dimension. A database is also generated following Design of Experiments (DoE) to obtain metamodels. They are developed for specific features representative of AM geometric class such as overhanging, holes or walls for instance. Mereotopological primitives with their AM definitions are used to define features in term of space and time variables. This theory enables the formalization of knowledge at the mesoscopic scale taken into consideration layer by layer build-up. It is then possible to use it to integrate data and information to the different feature juxtapositions using recognition algorithm. Information for each feature can then be included and explicitly used to help the designer during detailed design phase. A global 4-steps DfAM methodology maximizing the potential of AM is presented and validated through a part from the space industry use case. It includes the definition of skeleton/skin entities, pattern decomposition, information associated based on material evaluation and decision for AM part.