The Future of In-Space Manufacturing: A Systematic Review

Abstract

This systematic review examines the current state and future prospects of in-space manufacturing technologies. As humanity expands its presence beyond Earth, the ability to manufacture components and structures in space becomes increasingly critical for sustainable space exploration and settlement. This paper comprehensively analyzes recent advancements in additive manufacturing, materials processing, and robotic assembly systems designed for the space environment. We evaluate the technical challenges posed by microgravity, vacuum conditions, and radiation exposure, while identifying promising solutions and ongoing research initiatives. The review covers orbital manufacturing facilities, lunar in-situ resource utilization (ISRU), and asteroid mining technologies. We discuss the economic implications, potential applications for deep space missions, and the role of in-space manufacturing in establishing permanent human presence beyond Earth. Key findings highlight the transformative potential of space-based manufacturing for reducing launch costs, enabling large-scale space infrastructure, and supporting long-duration missions to Mars and beyond.

Publication
75th International Astronautical Congress (IAC), Milan, Italy, 2024

Conference

75th International Astronautical Congress (IAC) 2024

The International Astronautical Congress is the world’s premier space event, bringing together leaders from space agencies, industry, academia, and government.

Key Topics

  • Additive Manufacturing in Microgravity
  • Lunar In-Situ Resource Utilization (ISRU)
  • Asteroid Mining Technologies
  • Robotic Assembly Systems for Space
  • Materials Processing in Space Environment
  • Economic Analysis of Space Manufacturing
  • Deep Space Mission Applications
  • Sustainable Space Infrastructure

Status

Published - Presented at IAC 2024

Arman Asgharpoor Golroudbari
Arman Asgharpoor Golroudbari
Machine Learning Engineer

Machine Learning Engineer with 5+ years of experience in LLMs, transformer architectures, computer vision systems, and autonomous robotics.