Automatic Shape Modification for Self-Supporting Structures in Additive Manufacturing

Additive Manufacturing (AM) technology is considered as one of the most promising manufacturing technologies in the areas of aerospace and defense industries. However, a lack of assurance of quality with AM parts is a key technological barrier that prevents manufacturers from adopting AM technologies, especially for high value added applications. Therefore, it is critically important to monitor the quality of products during the AM process. In this paper, current process monitoring especially the defects measurement for metal AM in Powder Bed Fusion (PBF) was firstly reviewed. And then, an optical in-situ inspection method based on multi-spectrum is proposed. The optical measuring system with infrared and white light imaging system is designed and optimized. Imaging data fusion algorithms is proposed to obtain the enhanced measuring results from infrared and white light imaging system. Simulation studies were undertaken to verify the validity of the proposed monitoring system. The research work is helpful for the optimization of process parameters so as to control the quality in the AM process.

Additive manufacturing (AM) which can be a suitable technology to personalize wearables is ideal for adjusting the range of part performance such as mechanical properties if high performance is not required. However, the AM process parameter can impact overall durability and reliability of the part. In this instance, user behavior can play an essential role in performance of wearables through the settings of AM process parameter. This review discusses parameters of AM processes influenced by user behavior with respect to performance required to fabricate AM wearables. Many studies on AM are performed regardless of the process parameters or are limited to certain parameters. Therefore, it is necessary to examine how the main parameters considered in the AM process affect performance of wearables. The overall aims of this review are to achieve a greater understanding of each AM process parameter affecting performance of AM wearables and to provide requisites for the desired performance including the practice of sustainable user behavior in AM fabrication. It is discussed that AM wearables with various performance are fabricated when the user sets the parameters. In particular, we emphasize that it is necessary to develop a qualified procedure and to build a database of each AM machine about part performance to minimize the effect of user behavior.

Additive manufacturing (AM), also known as “3D printing,” has transitioned from concepts and prototypes to part-for-part substitution—and now to the creation of part geometries that can only be made using AM. As a wide range of mobility OEMs begin to introduce AM parts into their products, the question between insourcing and outsourcing the manufacturing of AM parts has surfaced. Just like parts made using other technologies, AM parts can require significant post-processing operations. Therefore, as AM supply chains begin to develop, the sourcing of AM part building and their post-processing becomes an unsettled and important issue. Unsettled Aspects of Insourcing and Outsourcing Additive Manufacturing discusses the approaches and trade-offs of the different sourcing options for production hardware for multiple scenarios, including both metallic and polymer technologies and components.

Additive Manufacturing (AM) is an increasingly widespread family of technologies for the fabrication of objects based on successive depositions of mass and energy. A strong need for modeling and simulation tools for AM exists, in order to predict thermal histories, residual stresses, microstructure, and various other aspects of the resulting components. In this paper we explore the use of analytic solutions to model the thermal aspects of AM, in an effort to achieve high computational performance and enable “in the loop” use for feedback control of AM processes. It is shown that the utility of existing analytical solutions is limited due to their underlying assumption of a homogeneous semi-infinite domain. These solutions must therefore be enriched from their exact form in order to capture the relevant thermal physics associated with AM processes. Such enrichments include the handling of strong nonlinear variations in material properties, finite non-convex solution domains, behavior of heat sources very near boundaries, and mass accretion coupled to the thermal problem. The enriched analytic solution method (EASM) is shown to produce results equivalent to those of numerical methods which require six orders of magnitude greater computational effort.

Additive manufacturing (AM) techniques are ideal for producing customized products due to their high design flexibility. Despite the previous studies on specific additive manufactured customized products such as biomedical implants and prostheses, the simultaneous optimization of components, materials, AM processes, and dimensions remains a challenge. Multidisciplinary design optimization (MDO) is a research area of solving complex design problems involving multiple disciplines which usually interact with each other. The objective of this research is to formulate and solve an MDO problem in the development of additive manufactured products customized for various customers in different market segments. Three disciplines, i.e. the customer preference modeling, AM production costing, and structural mechanics are incorporated in the MDO problem. The optimal selections of components, materials, AM processes, and dimensional parameters are searched with the objectives to maximize the functionality utility, match individual customers' personal performance requirements, and minimize the total cost. A multi-objective genetic algorithm with the proposed chromosome encoding pattern is applied to solve the MDO problem. A case study of designing customized trans-tibial prostheses with additive manufactured components is presented to illustrate the proposed MDO method. Clusters of multi-dimensional Pareto-optimal design solutions are obtained from the MDO, showing trade-offs among the objectives. Appropriate design decision can be chosen from the clusters based on the manufacturer's market strategy. Highlights An optimization problem for additive manufactured customized products is solved. Three disciplines are incorporated in multidisciplinary design optimization (MDO). The selection of component, material, additive manufacturing process and dimensions are optimized. A multiobjective genetic algorithm is applied to solve the MDO problem. Pareto-optimal solutions with different utilities and costs are obtained.

Additive manufacturing (AM) of short-fiber reinforced composites are actively being considered for construction of low-cost, weight reducing alternatives to non-structural metal components. In addition AM parts are being used for rapid manufacturing of composite tooling. AM is notorious for generating parts with higher porosity than more traditional manufacturing technologies. Due to the aerospace industry’s low risk tolerance, novel material systems require comprehensive characterization of their associated manufacturing defects and the impact of defects on performance in order to receive certification. AM short fiber composites still require this analysis. Although traditional composite manufacturing methods, such as an autoclave or VARTM can produce porosity, the origin and transport of porosity has been thoroughly studied and acceptable limits for part qualification have been established to minimize the effect on performance. In AM components studies establishing the origination of porosity origin and processing defect related minimization strategies are lacking. A preliminary study on the impacts of porosity caused by print parameters such print speed, layer height, first layer height, and step-over distance has been undertaken. Direct Ink Writing (DIW) was selected for this study using an epoxy-based ink filled with clay and chopped carbon fiber. To understand performance, fracture and shear specimens were fabricated with different test parameters based on the assumed levels of porosity from screening tests. Samples were then mechanically tested using a novel in-plane shear test method and a single edge notch tension test. This study explores the processing parameter’s contribution to porosity and establishes general trends to understand the influence of porosity on performance for additively manufactured chopped fiber composites.

Additive manufacturing (AM) has been growing in interest due to its non-traditional approach to producing objects by which components are fabricated directly from computer models by selectively depositing and consolidating or curing raw materials in successive layers. Currently, most polymer AM processes are limited by a narrow field of available and compatible materials. Typical thermoplastics common to the injection molding industry are created from petroleum, a limited and nonrenewable resource and have been widely adopted for use in AM. Recently plastics that are more sustainable, such as polylactic acid (PLA), have started to gain traction as a competitor to these traditional plastics. For this study, cellulose acetate butyrate (CAB), cellulose acetate propionate (CAP), hydroxypropyl cellulose (HPC), nylon 11, and polycaprolactone (PCL) were formulated and tested for AM. In addition, ABS and PLA were tested as control materials. Results showed the technical feasibility of some bioplastics for AM.

Additive manufacturing (AM) provides design flexibility and allows rapid fabrications of parts with complex geometries. The presence of internal defects, however, can lead to deficit performance of the fabricated part. X-ray Computed Tomography (XCT) is a non-destructive inspection technique often used for AM parts. Although defects within AM specimens can be identified and segmented by manually thresholding the XCT images, the process can be tedious and inefficient, and the segmentation results can be ambiguous. The variation in the shapes and appearances of defects also poses difficulty in accurately segmenting defects. This paper describes an automatic defect segmentation method using U-Net based deep convolutional neural network (CNN) architectures. Several models of U-Net variants are trained and validated on an AM XCT image dataset containing pores and cracks, achieving a best mean intersection over union (IOU) value of 0.993. Performance of various U-Net models is compared and analyzed. Specific to AM porosity segmentation with XCT images, several techniques in data augmentation and model development are introduced. This work demonstrates that, using XCT images, U-Net can be effectively applied for automatic segmentation of AM porosity with high accuracy. The method can potentially help improve quality control of AM parts in an industry setting.

Additive Manufacturing (AM) for metals, is based on melting or sintering and then solidification of metal powder particles to fabricate 3D objects directly out of Computer Aided Design (CAD) data. Due to its strong capabilities to fabricate components with complex geometries, AM largely relaxes the manufacturing constraints opening the design space to optimize components for performance maximization and extensive weight minimization. These features have made AM a strong manufacturing candidate for several applications in the aerospace and defense industries. However, it is found that during the AM process several powder particles are left un-melted leading to high porosity and poor component surface finish. These surface pores act as potential crack initiation sites during cyclic loading and thus significantly compromise the component’s tensile and fatigue performances, leading to poor mechanical properties compared to traditional cast or wrought metals. In this paper, we study the effect of Ultrasonic Impact Treatment (UIT) on the stress-controlled fatigue performance of additively manufactured Ti–6Al–4V alloy. Here, severe plastic deformations are developed on the surface of the metal by multiple sliding impacts generated by the UIT system. Specimens of Ti–6Al–4V alloy were manufactured by Direct Metal Laser Sintering (DMLS) and were subject to the UIT. The treatment was applied to statically preloaded specimens at 30 N and a controlled scanning speed of 1000 mm/min. Structure investigations were performed by testing as-built specimens as well as treated specimens by UIT in low-cycle fatigue under tensile cyclic loading of 400 MPa using 810 Materials Testing System (MTS). Fatigue life, surface defects and roughness were examined to observe the effects of the UIT on the metal’s surface. It is found that the UIT treatment has improved the fatigue life of the treated specimens by 192% compared to the as-built specimens at the same dynamic stress level. Also, microscopic observations indicate a better surface finish of the treated specimens.

Additive manufacturing (AM) methods become more and more important due to improvements in the printing technology and availability of printers. As additive manufacturing is still a very expensive technology, it is predominantly used for complex parts in aerospace or medical applications. Especially in such demanding applications the properties of the parts are very important. In case of M50NiL, a carburizing heat resistant steel typically used for mainshaft bearings in aero engines, the development of an AM process and the properties of AM processed parts are shown. By comparison with specimens manufactured from conventional M50NiL it is demonstrated, that properties determined by optical microscopy, hardness testing, tensile testing, and rolling contact fatigue testing of M50NiL AM and M50NiL conventional manufactured are comparable.

Additive Manufacturing (AM) based Printed Electronics (PE) is an emerging technique where electronic components and interconnects are printed directly on substrates using a layered technique. The direct printing of the electronic components allows large scale and ultra-thin components to be printed on a wide variety of substrates including glass, silicon and plastic. These attributes make AM based Printed Electronics an invaluable manufacturing technique in the area of electronic sensors and sensor networks where thin, flexible and rugged form factors are very important. However, currently this technology is a labor intensive and manual process with the machine operator using his experience and judgment to slice the CAD file of the part to create 2D layers at different levels. This manual process increases the overall production time as well as the cost of the product and also results in inconsistent quality of parts. A major challenge faced by existing AM based Printed Electronics users for automating this process is the lack of a standard input file format that can be used by different PE machines for producing the components in layers. To leverage the capabilities of both AM and PE processes, a new file format based on the Constructive Solid Geometry (CSG) technique is proposed in this research paper. This file format data will not only include CAD data in the form of CSG primitives and Boolean representation but will also include manufacturing information related to the AM based PE process. The manufacturing information embedded within this new format will include data about the location of the different electronic components such as interconnects, resistors, capacitors, inductors, transistors, memory and substrate, and the materials required for the different components part. Different circuit board components will be represented as primitives or a combination of primitives obtained using CSG technique. In addition to the new file format, a slicing algorithm will also be developed which can be used to create the layers automatically using user inputs. The proposed file format and the slicing algorithm will be explained with the help of a case study.

Additive manufacturing (AM) especially metal additive manufacturing (MAM) is expected to disrupt many industries. Besides being very flexible and allowing bespoke parts with little to no setup time, AM technology is able to fabricate parts with geometries which were previously impossible to create. This allows for dramatically better designs by making the product lighter or more efficient. However, despite these numerous and significant benefits, the uptake of functional additive manufactured parts is slow. A major barrier to expedited uptake of this technology is process control. It is not certain what the most important process parameters or the ideal process windows are and how this changes for different process/material combinations. As of yet, there is not a set process to certify an AM part or process. This makes quality assurance prohibitively longwinded and expensive. Furthermore, to ensure safety under such uncertain conditions, a high safety factor and therefore thicker parts must be used. As a result, uncertainty is also tied to increased material consumption and therefore higher environmental impact. We need to better understand the nature of variability in AM in order to alleviate some of these problems. This manuscript presents several examples of the influence of variability in manufacturing and its potential impact on environmental performance.

Additive manufacturing (AM), also known as three-dimensional (3D) printing, is a disruptive technology that is revolutionizing many industries. It is gaining considerable attention, particularly in the medical field as it renders the possibilities of building new devices or modifying existing devices to match a patient's anatomy and to produce anatomically exact models, supporting health professionals with diagnostics and surgery preparation. In addition, the free-form building capability of AM allows the designer to have a complete control over the internal architecture of the device, along with tailored mechanical properties, such as compression strength, stiffness, and many surface features. As the processes of AM become well-understood, there is more control over the consistency and quality of the printed parts, positioning this technology for medical applications. With more and more medically approved 3D-printed devices entering the market, the purpose of this paper is to give an overview of the regulatory pathway to the Food and Drug Administration approval of a medical device, along with common AM processes used in the medical industry. To conclude, medical devices that are enabled by AM technology and associated companies will be highlighted.

Additive manufacturing (AM) processes fabricate parts in a layer-by-layer manner by which materials are added and processed repeatedly. This article introduces the general concepts and approaches to design for AM (DFAM) and outlines important implications for part characteristics, design opportunities, manufacturing practices, supply chains, and even business models. It presents contrasting perspectives on DFAM, followed by a discussion on more general and overarching opportunistic design methods and on design for constraints, similar to conventional DFM. It concludes with a presentation of a design approach to the AM process chain, acknowledging that AM-fabricated parts typically undergo several postprocessing steps and that it is important to design taking into account these steps.

Additive manufacturing (AM) affords those who wield it correctly the benefits of shape, material, hierarchical, and functional complexity. However, many engineers and designers lack the training and experience necessary to take full advantage of these benefits. They require training, tools, and methods to assist them in gaining the enhanced design freedom made possible by additive manufacturing. This work, which is an extension of the authors’ previous work, explores if design heuristics for AM, presented in a card-based format, are an effective mechanism for helping designers achieve the design freedoms enabled by AM. The effectiveness of these design heuristic cards is demonstrated in an experiment with 27 product design students, by showing that there is an increase in the number of unique capabilities of AM being utilized, an increase in the AM novelty, and an increase in the AM flexibility of the generated concepts, when given access to the cards. Additionally, similar to the previous work, an increase in the number of interpreted heuristics and AM modifications present in the participants’ designs when they are provided with the heuristic cards is shown. Comparisons are also made between 8-heuristic and 29-heuristic experiments, but no conclusive statements regarding these comparisons can be drawn. Further user studies are planned to confirm the efficacy of this format at enhancing the design freedoms achieved in group and team design scenarios.