Introduction to Design for Additive Manufacturing

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) 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) 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.

Research in additive manufacturing (AM) has increased the use of AM in many industries, resulting in a commensurate need for a workforce skilled in AM. In order to meet this need, educational institutions have undertaken different initiatives to integrate design for additive manufacturing (DfAM) into the engineering curriculum. However, limited research has explored the impact of these educational interventions in bringing about changes in the technical goodness of students' design outcomes, particularly through the integration of DfAM concepts in an engineering classroom environment. This study explores this gap using an experimental study with 193 participants recruited from a junior-level course on mechanical engineering design. The participants were split into three educational intervention groups: (1) no DfAM, (2) restrictive DfAM, and (3) restrictive and opportunistic (dual) DfAM. The effects of the educational intervention on the participants' use of DfAM were measured through changes in (1) participants' DfAM self-efficacy, (2) technical goodness of their AM design outcomes, and (3) participants' use of DfAM-related concepts when describing and evaluating their AM designs. The results showed that while all three educational interventions result in similar changes in the participants' opportunistic DfAM self-efficacy, participants who receive only restrictive DfAM inputs show the greatest increase in their restrictive DfAM self-efficacy. Further, we see that despite these differences, all three groups show a similar decrease in the technical goodness of their AM designs, after attending the lectures. A content analysis of the participants' design descriptions and evaluations revealed a simplification of their design geometries, which provides a possible explanation for the decrease in their technical goodness, despite the encouragement to utilize the design freedom of AM to improve functionality or optimize the weight of the structure. These results emphasize the need for more in-depth DfAM education to encourage the use of both opportunistic and restrictive DfAM during student design challenges. The results also highlight the possible influence of how the design problem is stated on the use of DfAM in solving it.

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) 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) 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.

Design for additive manufacturing (DFAM) seeks to develop designs that take advantage of the unique capabilities of additive manufacturing (AM) processes to maximize benefits. In this paper, several issues at the frontier of DFAM research are highlighted. First, the need is described to include as-manufactured mechanical and other physical properties, such as topology and shape optimization and generative design, during computational design. AM processes rarely produce parts with homogeneous composition and isotropic properties, so design methods and tools should not assume them. Second, the topic of designing for the AM process chain is important since AM-fabricated parts typically need postprocessing operations, such as support removal, finish machining, heat treatments, etc. DFAM methods need to incorporate the entire process chain, not just the AM process, which is explored with an example from metal powder bed fusion. Third, potentially, the largest benefit of AM can be realized by radically rethinking product architectures. Examples of an electric motorcycle and an assistive exosuit illustrate the ideas and potential benefits. Finally, some thoughts on design for 4D printing are offered, specifically for 3D printed morphing and deployable systems that utilize the shape memory effect.

Additive Manufacturing (AM) is one of the manufacturing processes with the highest potentials in the current transformation of the industry. To make use of this potential and to achieve consistent product quality at decreasing costs not only the 3D printers themselves but also the whole process chain has to be automated. Due to the high degree of digitalization and the use of 3D Computer Aided Design (CAD) models within the entire process chain, it is possible to use these information for automation via intelligent data analysis. In this paper, the potential of using 3D Machine Learning (ML) approaches for automation and optimization of sub-processes of the process chain is analyzed. Therefore, we consider the information flow of the 3D models in the process chain of an AM service provider. The potential of using state-of-the-art algorithms from the field of 3D ML for automation of sub-processes like manufacturability analysis, production cost calculation or 3D-component recognition is analyzed and feasibility is examined. For the sub-processes of manufacturability analysis and 3D-component recognition prototype solutions have been implemented and evaluated. For the production cost calculation, only preliminary analyses were carried out, on the basis of which the possible applications of 3D ML algorithms can be estimated. With our analyses, we demonstrate that it is possible to further automate the process chain of AM service providers through the use of 3D ML algorithms.

The unique tool-less and layer-upon-layer capabilities of additive manufacturing (AM) have largely lessened manufacturing constraints and significantly broadened design freedom. AM offers new opportunities for developing customized products by achieving significant improvements in product performances and lower overall costs. The AM-enabled customization opportunities inevitably challenge traditionally design theories and methodologies (DTMs) that lack design knowledge for AM products. Here, to counteract this limitation, this study presents a novel methodology for constructing knowledge supporting DTMs for AM-enabled customization: customized design for AM (CDfAM). The CDfAM supports two main phases of design for additive manufacturing (DfAM): (1) opportunistic DfAM in a conceptual design phase and (2) restrictive DfAM in an embodiment design phase. In terms of the knowledge construction for the opportunistic DfAM, it is essential to construct knowledge that supports to explore a customized design space enabled by AM’s design freedom from the conceptual design phase. To address the challenge, this study provides a novel method for capturing knowledge of customized AM product behaviors. First, this study proposes an affordance-based DfAM framework by integrating the concept of affordance into three link chain models. Second, an affordance structure is proposed to develop a formal method for representing the knowledge of the customized AM product behaviors utilizing finite state automata. In terms of the knowledge construction for the restrictive DfAM, this study presents a formal method for representing and reasoning customizable modular knowledge of design rules for AM. First, a novel structured modular hierarchy and associated formalisms of AM design rule elements are presented. Second, machine learning is employed to automatically learn design rules from AM data, which leverages the proposed design rule formalisms. A knowledge graph is designed and built to demonstrate the proposed methodology. Case studies with AM products such as a transtibial prosthesis leg and AM data such as ex-situ AM measurement data provide illustrations in this study. The case studies show that the proposed methodology explicitly constructs structured and correlated AM design knowledge of customized AM product behaviors, product geometries, and additive manufacturability constraints. It is also shown that qualified design knowledge in the knowledge graph enhances automation of DfAM decision-making and data-driven knowledge constructions. The proposed CDfAM methodology enables incorporating both customized AM product functionalities and additive manufacturability constraints into product designs. This study provides a foundation for AM knowledge construction and data- and knowledge-driven automation of DfAM decision-making. Validated design rules and representations are expected to contribute to developing international standards on AM design rules.

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.

Purpose This paper aims to provide a multi-objective optimization problem in design for manufacturing (DFM) approach for fused deposition modeling (FDM). This method considers the manufacturing criteria and constraints during the design by selecting the best manufacturing parameters to guide the designer and manufacturer in fabrication with FDM. Design/methodology/approach Topological optimization and bi-objective optimization problems are suggested to complete the DFM approach for design for additive manufacturing (DFAM) to define a product. Topological optimization allows the shape improvement of the product through a material distribution for weight gain based on the desired mechanical behavior. The bi-objective optimization problem plays an important role to evaluate the manufacturability by quantification and optimization of the manufacturing criteria and constraint simultaneously. Actually, it optimizes the production time, required material regarding surface quality and mechanical properties of the product because of two significant parameters as layer thickness and part orientation. Findings A comprehensive analysis of the existing DFAM approaches illustrates that these approaches are not developed sufficiently in terms of manufacturability evaluation in quantification and optimization levels. There is no approach that investigates the AM criteria and constraints simultaneously. It is necessary to provide a decision-making tool for the designers and manufacturers to lead to better design and manufacturing regarding the different AM characteristics. Practical implications To assess the efficiency of this approach, a wheel spindle is considered as a case study which shows how this method is capable to find the best design and manufacturing solutions. Originality/value A multi-criteria decision-making approach as the main contribution is developed to analyze FDM technology and its attributes, criteria and drawbacks. It completes the DFAM approach for FDM through a bi-objective optimization problem which deals with finding the best manufacturing parameters by optimizing production time and material mass because of the product mechanical properties and surface roughness.

Design for manufacture and assembly (DfMA) in the architectural, engineering, and construction (AEC) industry is attracting the attention of designers, practitioners, and construction project stakeholders. Digital fabrication (Dfab) and design for additive manufacturing (DfAM) practices are found in current need of further research and development. The DfMA’s conceptual function is to maximize the process efficiency of Dfab and AM building projects. This work reviewed 171 relevant research articles over the past few decades. The concepts and the fundamentals of DfMA in building and construction were explored. In addition, DfMA procedures for Dfab, DfAM, and AM assembly processes were discussed. Lastly, the current machine learning research on DfMA in construction was also highlighted. As Dfab and DFAM are innovated, practical DFMA techniques begin to develop to a great extent. Large research gaps in the DfMA for Dfab and DfAM can be filled in terms of integrating them with product structural performance, management, studied cases, building information modeling (BIM), and machine learning to increase operational efficiency and sustainable practices.

Design for manufacturing to design for Additive Manufacturing: Analysis of implications for design optimality and product sustainability