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The data generated during additive manufacturing (AM) practice can be used to train machine learning (ML) tools to reduce defects, optimize mechanical properties, or increase efficiency. In addition to the size of the repository, emerging research shows that other characteristics of the data also impact suitability of the data for AM-ML application. What should be done in cases for which the data in too small, too homogeneous, or otherwise insufficient? Data augmentation techniques present a solution, offering automated methods for increasing the quality of data. However, many of these techniques were developed for machine vision tasks, and hence their suitability for AM data has not been verified. In this study, several data augmentation techniques are applied to synthetic design repositories to characterize if and to what degree they enhance their performance as ML training sets. We discuss the comparative advantage of these data augmentation techniques across several canonical AM-ML tasks. 

Additive manufacturing (AM) processes present designers with creative freedoms beyond the capabilities of traditional manufacturing processes. However, to successfully leverage AM, designers must balance their creativity against the limitations inherent in these processes to ensure the feasibility of their designs. This feasible adoption of AM can be achieved if designers learn about and apply opportunistic and restrictive design for AM (DfAM) techniques at appropriate stages of the design process. Researchers have demonstrated the effect of the order of presentation of information on the learning and retrieval of said information; however, there is a need to explore this effect within DfAM education. In this paper, we explore this gap through an experimental study involving 195 undergraduate engineering students. Specifically, we compare two variations in DfAM education: (1) opportunistic DfAM followed by restrictive DfAM, and (2) restrictive DfAM followed by opportunistic DfAM, against only opportunistic DFAM and only restrictive DfAM training. These variations are compared through (1) differences in participants’ DfAM self-efficacy, (2) their self-reported DfAM use, and (3) the creativity of their design outcomes. From the results, we see that only students trained in opportunistic DfAM, with or without restrictive DfAM, present a significant increase in their opportunistic DfAM self-efficacy. However, all students trained in DfAM – opportunistic, restrictive, or both – demonstrated an increase in their restrictive DfAM self-efficacy. Further, we see that teaching restrictive DfAM first followed by opportunistic DfAM results in the generation of ideas with greater creativity – a novel research finding. These results highlight the need for educators to accountfor the effects of the order of presenting content to students, especially when educating students about DfAM. 

The capabilities of additive manufacturing (AM) open up designers’ solution space and enable them to build designs previously impossible through traditional manufacturing. To leverage AM, designers must not only generate creative ideas, but also propagate these ideas without discarding them in the early design stages. This emphasis on selecting creative ideas is particularly important in design for AM (DfAM), as ideas perceived as infeasible through the traditional design for manufacturing lens could now be feasible with AM. Several studies have discussed the role of DfAM in encouraging creative idea generation; however, there is a need to understand concept selection in DfAM. In this paper, we investigated the effect of two variations in DfAM education: 1) restrictive DfAM and 2) dual DfAM (opportunistic and restrictive) on students’ concept selection process. Specifically, we compared the creativity of the concepts generated by the students to the creativity of the concepts selected by them. Further, we performed qualitative analyses to explore the rationale provided by the students in making these design decisions. From the results, we see that teams from both educational groups select ideas of greater usefulness; however, only teams from the restrictive DfAM group select ideas of higher uniqueness and overall creativity. Further, we see that introducing students to opportunistic DfAM increases their emphasis on the complexity of designs when evaluating and selecting them. These results highlight the need for DfAM education to encourage AM designers to not just generate but also select creative ideas. 

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. 

The integration of additive manufacturing (AM) processes in many industries has led to the need for AM education and training, particularly on design for AM (DfAM). To meet this growing need, several academic institutions have implemented educational interventions, especially project- and problem-based, for AM education; however, limited research has explored how the choice of the problem statement influences the design outcomes of a task-based AM/DfAM intervention. This research explores this gap in the literature through an experimental study with 222 undergraduate engineering students. Specifically, the study compared the effects of restrictive and dual (restrictive and opportunistic) DfAM education, when introduced through either a simple or complex design task. The effects of the intervention were measured through (1) changes in student DfAM self-efficacy, (2) student self-reported emphasis on DfAM, and (3) the creativity of student AM designs. The results show that the complexity of the design task has a significant effect on the participants’ self-efficacy with, and self-reported emphasis on, certain DfAM concepts. The results also show that the complex design task results in participants generating ideas with greater median uniqueness compared to the simple design task. These findings highlight the importance of the chosen problem statement on the outcomes of a DfAM educational intervention, and future work is also discussed.