A Deployment Case Study in Robotic Apparel Automation: Digital Twin Integration, Interoperability, and Workforce Enablement
2026-06-15 • Robotics
Robotics
AI summaryⓘ
The authors studied how to make robots better at sewing denim clothes, which is hard because fabric is soft and tricky to handle. They created a system that turns design files into robot instructions, making programming faster and easier. They also used a virtual model of the sewing setup to test and improve the process before using it in the real factory. Their system includes safety checks during sewing and tools to help workers learn how to use the robots. Testing in real factories showed these steps help make robot sewing more practical for making clothes.
flexible automationdigital threaddigital twinrobot trajectorycollaborative robotinteroperabilityruntime monitoringgarment shapingapparel manufacturing
Authors
Gokul Narayanan, Abhiroop Ajith, Jonathan Zornow, Carlos Calle, Auralis Herrero Lugo, Jose Luis Susa Rincon, Chengtao Wen, Eugen Solowjow
Abstract
Despite steady advances in flexible automation in sectors such as electronics and automotive manufacturing, apparel automation remains challenging because fabrics are deformable and difficult to manipulate with robots. This paper presents a deployment-oriented case study of a robotic sewing system for denim manufacturing, emphasizing the system-level integration required for practical adoption. At the engineering level, a digital thread module parses DXF production drawings into process parameters and executable robot trajectories, reducing manual programming effort and enabling rapid re-targeting across sewing operations. In parallel, a digital twin of the workcell is used during pre-deployment to validate reach and clearance, refine layout and sequencing, evaluate operator access, and assess cycle-time compatibility with upstream and downstream tasks, thereby reducing commissioning risk. At deployment, the system integrates a collaborative robot with conventional sewing equipment, welding, suction fixtures, and machine-level controllers through an interoperability layer. Runtime monitoring and verification, including seam monitoring, collision checking, and trajectory-level validation, improve robustness under environmental variability, while operator-facing training and guidance tools support setup, troubleshooting, and technology adoption. Two staged factory deployments on denim shorts, covering 2D pocket operations and 3D garment-shaping seams, show that digital-twin-based validation, digital-thread-driven task generation, interoperability, runtime verification, and operator training are important for scaling robotic apparel automation.