A Deployment Case Study in Robotic Apparel Automation: Digital Twin Integration, Interoperability, and Workforce Enablement
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Using Machine Learning Methods for Automation of Size Grid Building and Management
Fashion apparel companies require planning for the next season, a year in advance for supply chain management. This study focuses on size selection decision making for Levi Strauss. Currently, the region and planning gro…
Decision MakingManagementApparel-invariant Feature Learning for Apparel-changed Person Re-identification
With the rise of deep learning methods, person Re-Identification (ReID) performance has been improved tremendously in many public datasets. However, most public ReID datasets are collected in a short time window in which…
Person Re-IdentificationRepresentation LearningDistributional Instance Segmentation: Modeling Uncertainty and High Confidence Predictions with Latent-MaskRCNN
Object recognition and instance segmentation are fundamental skills in any robotic or autonomous system. Existing state-of-the-art methods are often unable to capture meaningful uncertainty in challenging or ambiguous sc…
Instance SegmentationObject RecognitionSegmentationSemantic SegmentationAutomated Seam Folding and Sewing Machine on Pleated Pants for Apparel Manufacturing
The applied research is the design and development of an automated folding and sewing machine for pleated pants. It represents a significant advancement in addressing the challenges associated with manual sewing processe…
Towards High-Quality 3D Motion Transfer with Realistic Apparel Animation
Animating stylized characters to match a reference motion sequence is a highly demanded task in film and gaming industries. Existing methods mostly focus on rigid deformations of characters' body, neglecting local deform…