Increasing adoption of additive manufacturing (AM) makes software support for design for additive manufacturing (DfAM) more relevant. This paper presents a novel, knowledge-driven design support tool for AM that leverages a central knowledge base to provide extensible and powerful DfAM support early in the development process. The approach was implemented using Python for the knowledge base and as a plugin for Siemens NX. It offers automated design checks, optimizations, and further information through an integrated Wiki. Evaluation confirms the feasibility and benefits of the approach.

Unlocking additive manufacturing's (AM) potential requires designer expertise. Design for additive manufacturing (DfAM) addresses this need but faces barriers, such as uncertainty in scope of integration, design support selection, result validation or time investment for incorporating design support. This paper proposes a framework aligning SCRUM (agile framework) to aid designers in overcoming those barriers. The goal is to pave the way for a better exchange between academia and industry and fostering iterative development of DfAM support tailored to designer needs.

The remote management of equipment is part of the functionalities granted by the design principles of Industry 4.0. However, some critical operations are managed by operators, machine setup and initialization serve as a significant illustration. Since the initialization is a repetitive task, industrial robots with a smart vision system can undertake these duties, enhancing the autonomy and flexibility of the manufacturing process. The smart vision system is considered essential for the implementation of several characteristics of Industry 4.0. This paper introduces a novel solution for controlling manufacturing machines using an embedded camera on the robot. This implementation requires the development of an interactive interface, designed in accordance with the supervision system known as Manufacturing Execution System. The framework is implemented inside a manufacturing cell, demonstrating a quick response time and an improvement between the cameras.

Designers are experiencing greater mental demands given the complexity of design tools, necessitating the study of cognitive load in design. Researchers have identified task- and designer-related factors that affect cognitive load; however, these studies primarily use self-reported measures that could be inaccurate and incomplete. Little research has tested the accuracy and completeness of self-reported measures and we aim to explore this gap. Towards this aim, we seek to answer the question: How does cognitive load vary based on the different design representations used, and do these differences depend on the measure of cognitive load? From our results, we see that the design representations vary in the range of cognitive load experienced by designers when using them. Moreover, this role of the range of cognitive load variance was observed given our use of pupil diameter. These findings call for the use of a multi-modal approach for measuring cognitive load with the combined use of subjective (e.g., self-report) and objective measures (e.g., physiological measures), as well as the use of both retrospective (e.g., self-report) and concurrent measures (e.g., physiological measures).

Additive Manufacturing (AM) is a unique manufacturing technology that is being rapidly accepted in various industries, leading to increased demand for experts who know to work with AM and how to design AM products. This led to a broader adaptation of AM in an educational context with various research on how to teach AM. However, most approaches are focused on teaching advanced AM application and Design for AM (DfAM), including both restrictive and opportunistic approaches, with little attention to specialised educational tools to show and teach the basic principle, possibilities and characteristics of AM. This paper presents the development of an Educational Kit for AM to address the gap and help teachers to explain the basics of AM, with a current focus on the material extrusion process. The Educational Kit is made of 17 models and accompanied cards explaining the essential characteristics of AM through short textual explanations, graphics, examples and manufacturing data. The Educational Kit for AM is intended to be used in introductory lessons on AM, so the novices in AM can quickly grasp the characteristics of AM and the basic terms used in AM before advancing to other AM and DfAM topics.

Additive manufacturing (AF) is characterised by a high degree of individuality and flexibility with regard to design and product layout. This enables the integration of different functions in a component. Due to these properties, AM has established itself in medical technology for the production of implants. Depending on the application, parameters such as resilience, biocompatibility and manufacturing restrictions play a varying role. So far, however, only limited research has been done on the design, manufacturing and application of hybrid implants (use of several materials). Although initial design and manufacturing guides exist, the problem of removing the hybrid implant from the shaping negative is hardly addressed.

The aim is to analyse and evaluate an existing procedure for the design of hybrid implants depending on individual requirements and to further develop it regarding the removability from the shaping negative. In this context, the extent to which the adhesive properties between the elements can be influenced by design changes is to be investigated.

The number of digital solutions based on machine learning has increased in recent years. In many industrial sectors, they try to enhance automation in manual or repetitive tasks or provide decision support for complex problems. Data plays an essential role in the selection and implementation of ML algorithms, as it determines the quality of the training and the results. As data drive ML models, selecting the correct data with the suitable ML algorithm for a given use case is crucial but challenging. This paper reviews the application of machine learning in the embodiment design phase addressing the challenge. The work focuses on ML applications in conventional product development and non-conventional additive manufacturing processes. Based on the literature review, the required knowledge to implement the ML algorithms has been derived and presented in a systematic approach. This work highlights the importance of an initial analysis of the existing knowledge in the engineering and additive manufacturing processes in order to implement the proper ML algorithms.

Due to the high freedom of design, additive manufacturing (AM) is increasingly substituting conventional manufacturing technology in several sectors. However, the knowledge and the awareness for the suitable design of additively manufactured components or assemblies ensuring manufacturability and fully realizing its potential is still lacking. In recent years, approaches and tools have emerged that allow the incorporation of existing knowledge of Design for Additive Manufacturing (DfAM) into the design process. Nevertheless, these applications mostly do not consider the formalisation of both restrictive and opportunistic DfAM guidelines for their integration in design tools.

Therefore, the following article presents a framework for the knowledge-driven adaptation of existing designs in the context of DfAM within an expert system. The novelty of the presented approach lies in the interdisciplinarity between the formalization of design guidelines and their integration and consideration within computeraided design for the semi-automated adaptation of functional non-assembly mechanisms. The application of the presented framework to a case study manufactured via Fused Layer Modeling (FLM) illustrates the applicability and benefits.

The as-built geometry and material properties of parts manufactured using Additive Manufacturing (AM) can differ significantly from the as-designed model and base material properties. These differences can be more pronounced in thin strut-like features (e.g., in a lattice structure), making it essential to incorporate them when designing for AM and predicting their structural behaviour. Therefore, the aim of this study is to develop a numerical model with realistic characteristics based on a thin strut-based test artefact and to use it accurately for estimating its compressive strength. Experiments on test samples produced by selective laser sintering in PA 1101, are used to calculate geometrical deviations, Young's modulus, and yield strength, which are used to calibrate the numerical model. The experimental and numerical results show that the numerical model incorporating geometrical and material deviations can accurately predict the peak load and the force-displacement behaviour. The main contributions of this paper include the design of the test artefact, the average geometrical deviation of the struts, the measured material data, and the developed numerical model.

Additive manufacturing methods present prospects for designed mechanical deformation via the integration of controlled anisotropic lattice structure forms. Their assimilation into a Design for Additive Manufacturing (DfAM) process would create a novel framework for the design of compliant mechanisms (CM). The method uses lattice structures to replace rigid multi-part mechanisms, with integrated and controlled flexibility into a single, compact, and precise component. In recent years, a lot of research has gone into making algorithms that enable users to generate CMs for their designs. But by relying on algorithms to design solutions, are they neglecting to fully understand how these mechanisms work. This work undertakes the design and development of a novel DfAM Framework, that utilises controlled lattice structure deformations to create a standardised method of CM design. The authors have developed a method for this, whilst allowing users to tailor CMs to their design, by using a wide selection of pretested structures. Indicating suitable structures for their design using an integrated novel taxonomy. The framework is tested and developed using a series of case studies.

Optimization-driven design offers advantages over traditional experience-based mechanical design. As an example, topology optimization can be a powerful tool to generate body shapes for Additive Manufacturing (AM). This is helpful, when (1) load paths are non-intuitive due to complex design domains or boundary conditions, or (2) the design process is to be automated to minimize effort associated with experience-based design. However, practically relevant boundary conditions are often difficult to put into a formal mathematical language to, for example, either feed it into a topology optimization algorithm, or provide precise quantitative criteria for CAE-supported manual design. This paper presents a survey of three industry use cases and identifies three types of requirements: the first can be directly cast into parts of an optimization problem statement (∼ 40%), the second is considered indirectly by adapting the optimization problem without explicit reference to the requirement (∼ 20%), and the third is only assessed after the design is finalized (∼ 40%). For categories 2 and 3 we propose directions of improvement to support formulating complex design tasks as unambiguous design problems.

The part build orientation is a manufacturing variable that must be considered when designing a product to maximise AM opportunities. There are several approaches to selecting the best print direction in the scientific literature by considering different criteria. However, most of the studies are focused on specific AM technologies. It is missing a general method that evaluates a widespread number of criteria. Furthermore, such approaches expect designers establish weights for technical criteria that are too specific, especially during the preliminary design steps. Designers are familiar with criteria like cost-effectiveness, productiveness, quality and mechanical strength.

The paper presents a multi-criteria decision-making approach to optimise the build part orientation in additive manufacturing. The method considers five decision-making criteria (cost-effectiveness, rapidity, productiveness, quality and mechanical strength) and seventeen specific technical criteria. TOPSIS is the method used to optimise the build part orientation. A case study of three components exemplifies the five steps of the procedure.

Water scarcity and resource depletion can be expected during the climate crisis. Therefore, thermally loaded processes in particular, must be made more efficient in the future. Heat exchangers will play a key role in this optimization process. More efficient designs allow a greater heat flow to be removed from processes while mass flows remain constant. In this context, the heat-transferring wall of heat exchangers is a focus of current research on the design of heat exchangers. The aim is to increase the heat-transferring surface of the wall as much as possible and to keep the design space as compact as possible. Therefore, this study investigates the suitability of the differential-growth method for generating complex heat-transferring walls for heat exchangers using CFD-analysis. Firstly, a framework for generating the wall structures and a computational model for predicting the design influence of such structures for the thermal and fluid-dynamic behavior of the heat exchanger are presented. Thereby, the potential of such wall structures is analyzed in this study. Furthermore, the study identified weaknesses of such walls designed with the differential-growth method, which should be the focus of future investigations.

Research on additive manufacturing has highlighted methods and guidelines to optimise the design process and improving finished product quality. There is still room for improvement in making AM as reliable as more traditional processes when considering industrial use. In terms of manufacturing, managing print parameters properly can improve reproducibility and repeatability of a part, in addition to its fidelity to the basic geometric model. However, a topological optimised geometry requires more than good parameterisation. Efforts are therefore being made to formalise knowledge so that it is explicit and accessible to designers. This paper proposes an approach based on the spatio-temporal evolution of a geometry during printing to quantify data at the meso scale. Previous studies have been conducted on the description of features in time, space and space-time, and on the influence of their arrangement within a part. Building on this work, a parameterised test specimen was designed to measure the quantitative impact of these arrangements on the final product. The method is then presented and illustrated through a case study to help the designer with quantitative predictive values of geometric parameters.

Laser Powder Bed Fusion (LPBF) brings the possibility to manufacture innovative near-net-shape part designs. But unfortunately, some designed surfaces suffer from rough surface finish due to characteristics of the LPBF process. This paper explores trends in managing surface roughness and through a space industry case study, a proposed process that uses Additive Manufacturing Design Artefacts (AMDAs) is used to investigate the relationship between design, surface roughness and fatigue. The process enables the identification of design uncertainties, however, iterations of AMDA's can be required.

The potentials of additive manufacturing for objectives such as lightweight construction are not yet fully exploited. In this paper, the possibilities of integrative function and system modelling for this challenge will be discussed. In a design study, a triathlon trailer is designed considering the constraints of AM. A suitable system boundary is to be detected using the one-part device method. The findings of the study will help to understand in which form methods such as functional modelling can be applied or adapted for the application of additive manufacturing.

Design for AM (DfAM) requires the definition of Design Actions (DAs) to optimize AM manufacturing processes. However, AM understanding is still very blurred. Often designers are challenged by selecting the right design parameters. A method to list and collect DfAM DAs is currently missing. The paper aims at providing a framework to collect DfAM DAs according to a developed ontology to create databases (DBs). DBs were tested with two real case studies and geometric features to improve identified. Future developments aim at widening the database to provide all-around support for AM processes.

Additive manufacturing (AM) has gained prominence over the last 15 years and become a viable manufacturing option. Since there is great industrial interest to implement serial production of products using AM, the education of engineers in design for additive manufacturing (DfAM) is important today. DfAM extends on design for manufacturing to provide knowledge about the new design opportunities enabled by AM. A set of design heuristics for additive manufacturing (DHAM) to assist designers with conceptual-level DfAM has previously been proposed. In this paper, these DHAM are evaluated through workshops with 3 engineering companies and 19 engineers, in which the participants re-design existing company products for AM using the DHAM as an aid, and then give feedback. The DHAM are well received by the workshop participants, and they find that the DHAM are good for teaching others about the capabilities of AM, provide a checklist of items to consider to help avoid oversights, and encourage the generation of new ideas. Criticisms include the number of examples provided and the lack of information about how to implement the ideas concretely. It is also found that the DHAM fulfil 16 of 18 criteria for early design phase DfAM methods, identified in academic literature.

The concept of Design for Additive Manufacturing (DfAM) is gaining popularity along with AM, despite its scopes are not well established. In particular, in the last few years, DfAM methods have been intuitively subdivided into opportunistic and restrictive. This distinction is gaining traction despite a lack of formalization. In this context, the paper investigates experts' understanding of DfAM. In particular, the authors have targeted educators, as the perception of DfAM scopes in the future will likely depend on teachers' view. A bespoke survey has been launched, which has been answer by 100 worldwide-distributed respondents. The gathered data has undergone several analyses, markedly answers to open questions asking for individual definitions of DfAM, and evaluations of the pertinence of meanings and acceptations from the literature. The results show that the main DfAM aspects focused on by first standardization attempts have been targeted, especially products, processes, opportunities and constraints. Beyond opportunistic and restrictive nuances, DfAM different understandings are characterized by different extents of cognitive endeavor, convergence vs. divergence in the design process, theoretical vs. hands on approaches.

Additively manufactured final products and components are not always tailored to the additive manufacturing (AM) process, but they need to be in order to exploit the many advantages and potentials that AM provides. Therefore, an appropriate AM design should be targeted, which reduces the necessary iterations in the developing process of AM products. Although there is a large number of existing literature on the Design for Additive Manufacturing (DfAM), designers usually lack criteria in order to assess AM-conformity in conceptual design. In this paper, we provide a basis for the assessment of solution principles regarding their conformity for additive manufacturing.

First, existing literature on DfAM and AM products is reviewed comprehensively to derive criteria for the AM-conformity of solution principles. Subsequently, the correlations between these criteria are identified including the interdependencies to be considered when assessing AM-conformity. A basis for assessment is created, which offers designers early support in the development of AM-conformal designs.