Design of a 3D printed exoskeleton for human-centred industrial operations

Abstract

Musculoskeletal disorders (MSDs) remain a significant issue in industrial environments, particularly in roles that require repetitive overhead work, manual tool handling, and prolonged static postures. This project explores the development of a passive upper-body exoskeleton designed to reduce physical strain on industrial workers through mechanical assistance. The primary objective was to create a low-cost, modular, and structurally sound exoskeleton system that could be manufactured using additive manufacturing techniques. An initial analysis of industrial tasks and a survey of workers provided the foundation for the design requirements of this project. A structured design methodology was applied, including Quality Function Deployment (QFD), Function Means Analysis (FMA), and concept generation supported by Synectics and a Morphological chart. Six conceptual designs were evaluated, with the best solution developed into a refined design and prototype. This design features a spring-loaded drum mechanism integrated into a modular backplate, transferring assistive torque to the user’s upper limb via a cable routed system. Material selection was guided by Ashby performance indices, with components fabricated primarily through fused deposition modelling (FDM). A functional prototype was assembled using a mix of 3D printed parts and standard mechanical components. Testing included a static hold endurance evaluation, which showed a 3.8× improvement in task duration compared to unaided use. The prototype preserved the user’s full range of motion and remained comfortable during extended wear. Despite constraints in spring sourcing and material substitution, the project successfully demonstrates the feasibility of a 3D printed passive exoskeleton for industrial support tasks. The work lays the foundation for future enhancements including smart passive sensor integration, modular backplate accessories, and further biomechanical testing using electromyography (EMG) data.