Tools for analysing additive manufacturability often employ complex models that lack transparency; this impedes user understanding and has detrimental effects on the implementation of results. An expert system tool that transparently learns features for successful printing has been created. The tool uses accessible data from STL models and printer configurations to create explainable parameters and identify risks. Testing has shown good agreement to print behaviour and easy adaptability. The tool reduces the learning curves designers face in understanding design for additive manufacturing.

Metal material extrusion is a family of metal additive manufacturing that includes atomic diffusion additive manufacturing (ADAM) and bound metal deposition (BMD). In the literature, there are just a few cost models for ADAM and no one for BMD. The paper presents an analytic cost model for BMD. It considers the entire process: pre-processing, printing and post-processing. The total manufacturing cost is split into material, machine, labour, energy and consumables items. The cost model validation on a 3D-printed part determined an accuracy of 98%.

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 additive manufacturing of parts made from close-to-production materials poses a great challenge. One example are highly viscous silicones, as used in injection moulding. For small production quantities, the manufacturing of injection moulds is uneconomical. This paper presents tensile specimens printed with an in-house developed dispensing system, which are analysed for air cavities (micro-CT scans) and mechanical properties. Based on the results, advice for the design and slicing parameters of parts using high-viscosity silicones in AM by means of material extrusion are developed.

In recent years, interest in additive manufacturing has increased. To overcome challenges such as the correct use of the technology, guidelines are needed to help the user in the fabrication process. However, such guidance is not currently available for all applications. This paper dives into design methods in AM and their transfer to an application example in the field of medical technology. The aim of this paper is to analyse the transferability of a design method for vessel models to small vessel models. To this end, an initial printing study is carried out on simplified hollow structures.

The production of reusable gecko-inspired dry adhesives has traditionally been done with complex nanofabrication methods such as lithography and PDMS casting. This article presents a way of producing and testing dry adhesive samples using consumer-grade AM machines and equipment typically available in a Makerspace. The samples produced exhibit adhesive properties depending on the preload and surface structure, and we conclude that consumer-grade AM is suitable for rapid prototyping and testing of dry adhesion. However, it is limited by the scale and accuracy compared to traditional methods.

Proficiency in design for additive manufacturing (DfAM) requires training and a lot of trial and error. To support the development of DfAM skills, we redesigned 47 design artifacts from case studies and derived tacit knowledge from successful and unsuccessful redesigns. All knowledge about these artifacts was then collected in a design catalog. In a workshop with a total of 48 graduates and students, 45 participants deemed the design catalog supportive. After evaluating their designs, we concluded that the use of a knowledge-based design catalog can develop and improve individual DfAM skills.

This paper presents a method for the lightweight design of robotic links subject to dynamic loads and requirements on the overall system stiffness. It includes (1) a decomposition scheme to enable separate component optimization and (2) an approach based on topology optimization for optimal load path design of screw connections. The approach reduces computing cost and mass of designs with screw connections.

Additive Manufacturing (AM) enhances component functionality in engineering. This study explores AM benefits for hydraulic pumps, by reviewing literature on fluid power and existing AM successes in pumps, pipes, and manifolds. While hydraulic pump research is scarce, the study redesigns a pump, mirroring successes in other hydraulic areas. Predicted outcomes include a 45-85% pressure drop reduction, 35% weight reduction, and fewer parts compared to traditional pumps, achieved with minor design changes. Larger-scale redesigns promise even greater improvements.

Additive Manufacturing has transformed modern manufacturing with its well-known advantages. However, shrinkage remains a critical challenge, causing dimensional inaccuracies that should be properly compensated to assure geometric fidelity. This study aims to assess the reliability of a Reverse Engineering (RE) technique for dimensional compensation. A gauge-based measurement approach has been used to validate the RE method. Results confirm that the RE method is promising, while highlighting the intrinsic errors of the RE technique, and suggesting ways to evaluate and prevent them.

This study explores Machine Learning (ML) integration for household refrigerator efficiency. The ML approach allows to optimize defrost cycles, offering energy savings without complexity or cost escalation. The paper initially presents a State-of-the-Art of ML potential to improve functionality and efficiency of refrigerators. Since frost is the cause of significant energy losses, a ML-based Virtual Sensor was developed to predict frost formation on the evaporator also in low -level refrigerators. The results show the environmental significance of ML in enhancing appliance efficiency.

Despite the recognized importance of datasets in data-driven design approaches, their extensive study remains limited. We review the current landscape of design datasets and highlight the ongoing need for larger and more comprehensive datasets. Three categories of challenges in dataset development are identified. Analyses show critical dataset gaps in design process where future studies can be directed. Synthetic and end-to-end datasets are suggested as two less explored avenues. The recent application of Generative Pretrained Transformers (GPT) shows their potential in addressing these needs.

This study examines the use of graph centrality to identify critical components in assembly models, a method typically dominated by computationally intense analyses. By applying centrality measures to simulated assembly graphs, components were ranked to assess their criticality. These rankings were compared against Monte Carlo sensitivity analysis results. Preliminary findings indicate a promising correlation, suggesting graph centrality as a valuable tool in assembly analysis, enhancing efficiency and insight in critical component identification.

The role of Artificial Intelligence (AI) in design is clearly growing. One of the tenets of the paper is that stimulation could be among the design processes mostly benefitting from the introduction of AI. Available contributions have been reviewed to understand the current support AI can give in design inspiration and ideation. We also reflected on what AI should and ahould not do in the future: a framework is proposed. Based on the reviewed contributions, in no case, AI is seen as a substitute of designers. Most contributions originate from the IT domain and have a demonstrative purpose.

Early consideration of failure modes in the feature development process is essential to identify and trace risks across the physical and embedded AI components of intelligent systems, to enhance the robustness of the feature delivery as well as trust in the AI. This paper introduces an extension of the AIAG/VDA function failure modes taxonomy, to facilitate the integrated analysis of complex intelligent systems with embedded AI. A case study of an autonomous driving feature is discussed as validation of the proposed taxonomy.

Despite the rapid advancement of generative Large Language Models (LLMs), there is still limited understanding of their potential impacts on engineering design (ED). This study fills this gap by collecting the tasks LLMs can perform within ED, using a Natural Language Processing analysis of 15,355 ED research papers. The results lead to a framework of LLM tasks in design, classifying them for different functions of LLMs and ED phases. Our findings illuminate the opportunities and risks of using LLMs for design, offering a foundation for future research and application in this domain.

Images provide concise representations of design artifacts and emerge as the primary mode of communication among innovators, engineers, and designers. The advanced of Artificial Intelligence tools which integrates image and textual information can significantly support the Engineering Design process. In this paper we create 5 different datasets combining both images and text of patents and we develop a set of text-based metrics to assess the quality of text for multimodal applications. Finally, we discuss the challenges arising in the development of multimodal patent datasets.

Shorter product lifecycles are also leading to even shorter planning times for the development of production systems. In most companies, the restructuring is carried out within a few weeks during the annual holidays. Digital tools such as simulations or the digital twin are used to avoid delaying the restructuring during this time. However, the introduction of a 3D model of the factory is often the first point of failure for many companies. This article proposes a six-step process model that enables the transition from 2D to 3D design. The process model was evaluated in a research project.

Sketch2Prototype is an AI-based framework that transforms a hand-drawn sketch into a diverse set of 2D images and 3D prototypes through sketch-to-text, text-to-image, and image-to-3D stages. This framework, shown across various sketches, rapidly generates text, image, and 3D modalities for enhanced early-stage design exploration. We show that using text as an intermediate modality outperforms direct sketch-to-3D baselines for generating diverse and manufacturable 3D models. We find limitations in current image-to-3D techniques, while noting the value of the text modality for user-feedback.

This study provides insights into the capabilities and performance of generative AI, specifically ChatGPT, in engineering design. ChatGPT participated in a 48-hour hackathon by instructing two participants who acted out its instructions, successfully designing and prototyping a NERF dart launcher that finished second among six teams. The paper highlights the potential and limitations of generative AI as a tool for ideation, decision-making, and optimization in engineering tasks, demonstrating the practical applicability of generating viable design solutions under real-world constraints.