Knowledge engineering for additive manufacturing-driven customization

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.

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.

Metal 3D AM (Additive Manufacturing) has been becoming a more common production method for larger variety of parts. In this review the current situation and future development trends of the 3D metal AM are presented, concentrating on the SLM (Selective Laser Melting) technology. A holistic approach to the AM as a digital manufacturing method is presented and different manufacturing aspects of the AM production are identified. The most promising aspects for the future development are the automatization of the AM design tasks and automatization of the production. With the development of these aspects the production and cost efficiency of the metal AM can be increased to a more competitive level compared with other manufacturing methods.

Design for Additive Manufacturing: Trends, opportunities, considerations, and constraints

The unique design freedom offered by additive manufacturing (AM) technologies enables engineers to develop more innovative products with relatively lower costs within a shorter period of processing time in comparison with conventional manufacturing methods. On the other hand, the unique capabilities of AM have created a platform for researchers to combine several engineering methods with the new manufacturing technique to grow industrial applications as well as resolve the existing issues with AM processes. Understanding the research values that AM offers academic environments, this paper performs a systematic survey on AM-related research topics in the fields of mechanical engineering and materials science that have attracted much attention from research teams over the last few years. These topics, namely process modelling in AM, innovative research in AM, generative design by AM, material characterisation in AM processes, and finally, design for additive manufacturing (DfAM), are notably investigated through this study.

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.

Of Narrow Windows and Acrobats: COVID-Responsiveness and Knock-Ons via Additive Manufacturing Capabilities

Chapter 1 - Introduction to AM

Purpose – The purpose of this paper is to present Design Rules for additive manufacturing and a method for their development. Design/methodology/approach – First, a process-independent method for the development of Design Rules was worked out. Therefore, geometrical standard elements and attributes that characterize the elements’ shapes have been defined. Next, the standard elements have been manufactured with different attribute values with Laser Sintering, Laser Melting and Fused Deposition Modeling, and their geometrical quality was examined. From the results, Design Rules for additive manufacturing were derived and summarized in a catalogue. Findings – Due to the process independent method, Design Rules were developed that apply for the different considered additive manufacturing technologies equally. These Design Rules are completely function-independent and easily transferable to individual part designs. Research limitations/implications – The developed Design Rules can only apply for the considered boundary conditions. To extend the Design Rules’ validity, their applicability should be proven for other boundary conditions. Practical implications – The developed Design Rules practically support the design of technical parts. Additionally they can be used for training and teaching in the field of “design for additive manufacturing”. Originality/value – The developed Design Rules constitute a first step toward general Design Rules for Additive Manufacturing. Thus, they might form a suitable basis for further scientific approaches, and the Design Rules can be used to set up teaching documentations for lessons and seminars.

Multi-view feature modeling for design-for-additive manufacturing

The cyclic manufacturing processes of Additive Manufacturing (AM) create three-dimensional objects layer by layer, offer a large freedom in design, and suffer from very different restrictions compared to conventional manufacturing processes. Nowadays, AM is a mature manufacturing technology capable of being applied in industrial series production. However, the AM technology enters the industrial series production only slowly. A main reason is the fact that engineers in industry have little to no experience in Design for Additive Manufacturing (DfAM) and an integrated experience transfer in engineering education is not yet widespread. To implement the AM technology, a course for graduate students was developed and established, mainly in the master studies and in the field of mechanical engineering. The goal of the course is that the students gain experience in the possibilities and the restrictions of DfAM. The course is based on the Experience Transfer Model (ETM) and is divided in two parts, a lecture and a team project. In the lecture, the students gain explicit knowledge for DfAM. In the practical team project, the students develop and produce AM prototypes based on a variety of AM processes and gain implicit knowledge. The concept of the team project is based on Problem-Based Learning (PBL). The whole team project follows a product development process, including presentations of milestone. The physical outcomes showed that the students are able to design AM optimized parts without falling back into old design patterns. In a survey at the end of the lecture series, the students gave the authors feedback. The large majority of the feedback was positive.

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, characterized by its inherent layer by layer fabrication methodology has been coined by many as the latest revolution in the manufacturing industry. Due to its diversification of Materials, processes, system technology and applications, Additive Manufacturing has been synonymized with terminology such as Rapid prototyping, 3D printing, free-form fabrication, Additive Layer Manufacturing, etc. A huge media and public interest in the technology has led to an innovative attempt of exploring the technology for applications beyond the scope of the traditional engineering industry. Nevertheless, it is believed that a critical factor for the long-term success of Additive Manufacturing would be its ability to fulfill the requirements defined by the traditional manufacturing industry. A parallel development in market trends and product requirements has also lead to a wider scope of opportunities for Additive Manufacturing. The presented paper discusses some of the key challenges which are critical to ensure that Additive Manufacturing is truly accepted as a mainstream production technology in the industry. These challenges would highlight on various aspects of production such as product requirements, process management, data management, intellectual property, work flow management, quality assurance, resource planning, etc. In Addition, changing market trends such as product life cycle, mass customization, sustainability, environmental impact and localized production will form the foundation for the follow up discussion on the current limitations and the corresponding research opportunities. A discussion on ongoing research to address these challenges would include topics like process monitoring, design complexity, process standardization, multi-material and hybrid fabrication, new material development, etc.

Additive manufacturing (AM) can produce designs in a manner that greatly differs from the methods used in the older, more familiar technologies of traditional manufacturing (TM). As an example, AM's layer-by-layer approach to manufacturing designs can lead to the production of intricate geometries and make use of multiple materials, made possible without added manufacturing cost and time due to AM's “free complexity.” Despite this contrasting method for manufacturing designs, designers often forgo the new design considerations for AM (AM design heuristics). Instead, they rely on their familiarity with the design considerations for TM (TM design heuristics) regardless of the intended manufacturing process. For designs that are intended to be manufactured using AM, this usage of TM design considerations is wasteful as it leads to unnecessary material usage, increased manufacturing time, and can result in designs that are poorly manufactured. To remedy this problem, there is a need to intervene early in the design process to help address any concerns regarding the use of AM design heuristics. This work aims to address this opportunity through a preliminary exploration of the design heuristics that students naturally leverage when creating designs in the context of TM and AM. In this study, 117 students in an upper-level engineering design course were given an open-ended design challenge and later tasked with self-evaluating their designs for their manufacturability with TM and AM. This evaluation of the students' designs was later repeated by relevant experts, who would identify the common design heuristics that students are most likely to use in their designs. Future studies will build on these findings by cementing early-stage design support tools that emphasize the significant heuristics found herein. For example, this work found that the design heuristic “incorporating complexity” was the most significant indicator of designs most suited for AM and should therefore be highly encouraged/emphasized when guiding designers in the use of AM. In doing so, it will be possible for early-stage design support tools to maximally improve designs that are intended to be manufactured for AM.

Total Life Cycle Sustainability Analysis of Additively Manufactured Products