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This work addresses the challenges of acquiring additive manufacturing data, given the complexities and design possibilities of such structures. Researchers in additive manufacturing struggle with scarcity and unsuitability of 2D datasets which pose further difficulties. To overcome these concerns, this research presents an application, AddLat2D, for generating 2D lattice structure datasets tailored to user specifications. Building upon a previous version of the application (Baldwin et al., 2023, 2022), this work highlights our development and usage of AddLat2D to generate datasets that have custom image size and pixel intensity values. 

Additive manufacturing is advantageous for producing lightweight components while addressing complex design requirements. This capability has been bolstered by the introduction of unit lattice cells and the gradation of those cells. In cases where loading varies throughout a part, it may be beneficial to use multiple, distinct lattice cell types, resulting in multi-lattice structures. In such structures, abrupt transitions between unit cell topologies may cause stress concentrations, making the boundary between unit cell types a primary failure point. Thus, these regions require careful design to ensure the overall functionality of the part. Although computational design approaches have been proposed, smooth transition regions are still difficult to achieve, especially between lattices of drastically different topologies. This work demonstrates and assesses a method for using variational autoencoders to automate the creation of transitional lattice cells, examining the factors that contribute to smooth transitions. Through computational experimentation, it was found that the smoothness of transition regions was strongly predicted by how closely the endpoints were in the latent space, whereas the number of transition intervals was not a sole predictor. 

Abstract The intersection between engineering design, manufacturing, and artificial intelligence offers countless opportunities for breakthrough improvements in how we develop new technology. However, achieving this synergy between the physical and the computational worlds involves overcoming a core challenge: few specialists educated today are trained in both engineering design and artificial intelligence. This fact, combined with the recency of both fields’ adoption and the antiquated state of many institutional data management systems, results in an industrial landscape that is relatively devoid of high-quality data and individuals who can rapidly use that data for machine learning and artificial intelligence development. In order to advance the fields of engineering design and manufacturing to the next level of preparedness for the development of effective artificially intelligent, data-driven analytical and generative tools, a new design for X principle must be established: design for artificial intelligence (DfAI). In this paper, a conceptual framework for DfAI is presented and discussed in the context of the contemporary field and the personas which drive it. 

Compared to conventional fabrication, additive manufacturing enables production of far more complex geometries with less tooling and increased automation. However, despite the common perception of AM’s “free” geometric complexity, this freedom comes with a literal cost: more complex geometries may be challenging to design, potentially manifesting as increased engineering labor cost. Being able to accurately predict design cost is essential to reliably forecasting large-scale design for additive manufacturing projects, especially for those using expensive processes like laser powder bed fusion of metals. However, no studies have quantitatively explored designers’ ability to complete this forecasting. In this study, we address this gap by analyzing the uncertainty of expert design cost estimation. First, we establish a methodology to translate computer-aided design data into descriptive vectors capturing design for additive manufacturing activity parameters. We then present a series of case study designs, with varied functionality and geometric complexity, to experts and measure their estimations of design labor for each case. Summary statistics of the cost estimates and a linear mixed effects model predicting labor responses from participant and design attributes was used to estimate the significance of factors on the responses. A task-based, CAD model complexity calculation is then used to infer an estimate of the magnitude and variability of normalized labor cost to understand more generalizable attributes of the observed labor estimates. These two analyses are discussed in the context of advantages and disadvantages of relying on human cost estimation for additive manufacturing forecasts as well as future work that can prioritize and mitigate such challenges.

 

The demand for additive manufacturing (AM) continues to grow as more industries look to integrate the technology into their product development. However, there is a deficit of designers skilled to innovate with this technology due to challenges in supporting designers with tools and education for their development in design for AM (DfAM). There is a need to introduce intuitive tools and knowledge to enable future designers to DfAM. Immersive virtual reality (VR) shows promise to serve as an intuitive tool for DfAM to aid designers during design evaluation. The goal of this research is to, therefore, identify the effects of immersion in design evaluation and study how evaluating designs for DfAM between mediums that vary in immersion, affects the results of the DfAM evaluation and the mental effort experienced from evaluating the designs. Our findings suggest that designers can use immersive and non-immersive mediums for DfAM evaluation without experiencing significant differences in the outcomes of the evaluation and the cognitive load experienced from conducting the evaluation. The findings from this work thus have implications for how industries can customize product and designer-talent development using modular design evaluation systems that leverage capabilities in immersive and non-immersive DfAM evaluation.

 

Additive manufacturing is advantageous for producing lightweight components while maintaining function and form. This ability has been bolstered by the introduction of unit lattice cells and the gradation of those cells. In cases where loading varies throughout a part, it may be necessary to use multiple lattice cell types, also known as multi-lattice structures. In such structures, abrupt transitions between geometries may cause stress concentrations, making the boundary a primary failure point; thus, transition regions should be created between each lattice cell type. Although computational approaches have been proposed, smooth transition regions are still difficult to intuit and design, especially between lattices of drastically different geometries. This work demonstrates and assesses a method for using variational autoencoders to automate the creation of transitional lattice cells. In particular, the work focuses on identifying the relationships that exist within the latent space produced by the variational autoencoder. Through computational experimentation, it was found that the smoothness of transition regions was higher when the endpoints were located closer together in the latent space. 

Although there is a substantial growth in the Additive Manufacturing (AM) market commensurate with the demand for products produced by AM methods, there is a shortage of skilled designers in the workforce that can apply AM effectively to meet this demand. This is due to the innate complications with cost and infrastructure for high-barrier-to-entry AM processes such as powder bed fusion when attempting to educate designers about these processes through in-person learning. To meet the demands for a skilled AM workforce while also accounting for the limited access to the range of AM processes, it is important to explore other mediums of AM education such as computer-aided instruction (CAI) which can increase access to hands-on learning experiences. Therefore, the purpose of this paper is to analyze the use of CAI in AM process education and focus on its effects on knowledge gain and cognitive load. Our findings show that when designers are educated about material extrusion and powder bed fusion through CAI, the knowledge gain for powder bed fusion is significantly different than knowledge gain for material extrusion, with no significant difference in cognitive load between these two AM processes. These findings imply that there is potential in virtual mediums to improve a designer’s process-centric knowledge for the full range of AM processes including those that are usually inaccessible. We take these findings to begin developing recommendations and guidelines for the use of virtual mediums in AM education and future research that investigates implications for virtual AM education.

 

Given the growing presence of additive manufacturing (AM) processes in engineering design and manufacturing, there has emerged an increased interest in introducing AM and design for AM (DfAM) educational interventions in engineering education. Several researchers have proposed AM and DfAM educational interventions; however, some argue that these efforts might not be sufficient to develop higher-level skills among engineers (e.g., identifying design opportunities that leverage AM capabilities). Prior work has shown that longer, distributed educational interventions are more effective in encouraging learning and information retention; however, these interventions could also be time-consuming and expensive to implement. Therefore, there is a need to test the effectiveness of longer, distributed DfAM educational interventions compared to shorter, lecture-style interventions. Our aim in this research is to explore this research gap through an experimental study. Specifically, we compared two variations of a DfAM educational intervention: (1) a module-style intervention spread over two sessions with the introduction of DfAM evaluation metrics, and (2) a lecture-style intervention completed in a single session with no evaluation metrics introduced. From our results, we see that students who received the module-style intervention reported a greater increase in their DfAM self-efficacy. Additionally, students who received the module-style intervention reported having given a greater emphasis on part consolidation and feature size. Finally, we observe that the structure of the educational intervention did not influence the creativity of ideas generated by the participants. These findings highlight the utility of module-style DfAM educational interventions towards increasing DfAM self-efficacy, but not necessarily design creativity. Moreover, these findings highlight the need to formulate educational interventions that are effective and efficient.

 

Additive manufacturing (AM) processes present designers with unique capabilities while imposing several process limitations. Designers must leverage the capabilities of AM — through opportunistic design for AM (DfAM) — and accommodate AM limitations — through restrictive DfAM — to successfully employ AM in engineering design. These opportunistic and restrictive DfAM techniques starkly contrast the traditional, limitation-based design for manufacturing techniques — the current standard for design for manufacturing (DfM). Therefore, designers must transition from a restrictive DfM mindset towards a ‘dual’ design mindset — using opportunistic and restrictive DfAM concepts. Designers’ prior experience, especially with a partial set of DfM and DfAM techniques could inhibit their ability to transition towards a dual DfAM approach. On the other hand, experienced designers’ auxiliary skills (e.g., with computer-aided design) could help them successfully use DfAM in their solutions. Researchers have investigated the influence of prior experience on designers’ use of DfAM tools in design; however, a majority of this work focuses on early-stage ideation. Little research has studied the influence of prior experience on designers’ DfAM use in the later design stages, especially in formal DfAM educational interventions, and we aim to explore this research gap. From our results, we see that experienced designers report higher baseline self-efficacy with restrictive DfAM but not with opportunistic DfAM. We also see that experienced designers demonstrate a greater use of certain DfAM concepts (e.g., part and assembly complexity) in their designs. These findings suggest that introducing designers to opportunistic DfAM early could help develop a dual design mindset; however, having more engineering experience might be necessary for them to implement this knowledge into their designs.