Additive manufacturing (AM) has transformed job shop production and catalysed the growth of Makerspaces, FabLabs, Hackspaces, and Repair Cafés. AM has enabled the handling and manufacturing of a wide variety of components, and its accessibility has enabled more individuals to make. While smaller than their production-scale counterparts, the objectives of minimizing technician overhead, capital expenditure, and job response time remain the same. The typical First-Come First-Serve (FCFS) operating model, while functional, is not necessarily the most efficient and makes responding to a-typical or urgent demand profiles difficult. This article reports a study that investigated how AM machines configured with Minimally Intelligent agents can support production in these environments. An agent-based model that simulated 5, 10, 15, and 20 AM machines operating a 9 am−5 pm pattern and experiencing a diverse non-repeating demand profile was developed. Machines were configured with minimal intelligence – FCFS, First-Response First-Serve (FRFS), Longest Print Time (LPT), Shortest Print Time (SPT), and Random Selection logics – that governed the selection of jobs from the job pool. A full factorial simulation totaling 15,629 configurations was run until convergence to a ranked list of production performance – min Job Time-in-System. Performance changed as much as 200%. Performant configurations featured a variety of logics, while the least performant were dominated by FCFS and LPT. All FCFS (a proxy for today’s operations) was one of the least performant configurations. The results provide an optimal set of logics and performance bands that can be used to justify capital expenditure and AM operations in Makerspaces.

An academic makerspace, home to tools and people dedicated to facilitating and inspiring a making culture, is characterized by openness, creativity, learning, design, and community. This nontraditional learning environment has found an immense increase in popularity and investment in the last decade. Further, makerspaces have been shown to be highly gendered, privileging men's and masculine understandings of making. The spike in popularity warrants deeper analysis, examining the value of these spaces for women and if learning is occurring in these spaces, specifically at higher education institutions. We implemented a phenomenologically based interviewing process to capture the making experiences of 20 women students, recruited through purposive and snowball sampling. By eliciting the narratives of women students, we captured how making, designing, and creating evolved through gendered experiences in the university makerspace. Each interview was transcribed and resulted in around 868 pages of single-spaced text transcriptions. The data were analyzed through multiple cycles of open and axial coding for common themes and patterns, where makerspaces create a culture of learning, facilitate students’ design journey, and form a laboratory for creativity. These themes forwarded the creation of a learning model that showcases how design and learning interact in the makerspace. This work demonstrates that women students are engaging learning and inspiration; developing confidence and resilience; and learning how to work with others and collaborate.