Optimal configurations of Minimally Intelligent additive manufacturing machines for Makerspace production environments

James Gopsill*, Mark Goudswaard, Chris Snider, Lorenzo Giunta, Ben Hicks

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

Abstract

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.

Original languageEnglish
JournalArtificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM
Volume38
DOIs
Publication statusPublished - 17 Jan 2024

Bibliographical note

Funding Information:
The work has been undertaken as part of the Engineering and Physical Sciences Research Council (EPSRC) grants – EP/R032696/1, EP/V05113X/1, and EP/W024152/1.

Publisher Copyright:
© The Author(s), 2024. Published by Cambridge University Press.

Keywords

  • additive manufacturing
  • agent-based modelings
  • FabLabs
  • Hackerspaces
  • Hackspaces
  • job-shops
  • Makerspaces
  • material extrusion
  • minimal intelligence
  • workshops

Fingerprint

Dive into the research topics of 'Optimal configurations of Minimally Intelligent additive manufacturing machines for Makerspace production environments'. Together they form a unique fingerprint.

Cite this