activity
20242026
collaborators

8 papers

cs.CE2026

Context-Aware Mapping of 2D Drawing Annotations to 3D CAD Features Using LLM-Assisted Reasoning for Manufacturing Automation

Muhammad Tayyab Khan, Lequn Chen, Wenhe Feng +1

Manufacturing automation in process planning, inspection planning, and digital-thread integration depends on a unified specification that binds the geometric features of a 3D CAD m…

cs.CV2026

A Multi-Stage Hybrid Framework for Automated Interpretation of Multi-View Engineering Drawings Using Vision Language Model

Muhammad Tayyab Khan, Zane Yong, Lequn Chen +3

Engineering drawings are fundamental to manufacturing communication, serving as the primary medium for conveying design intent, tolerances, and production details. However, interpr…

cs.CV2025

From Drawings to Decisions: A Hybrid Vision-Language Framework for Parsing 2D Engineering Drawings into Structured Manufacturing Knowledge

Muhammad Tayyab Khan, Lequn Chen, Zane Yong +3

Efficient and accurate extraction of key information from 2D engineering drawings is essential for advancing digital manufacturing workflows. Such information includes geometric di…

cs.CV2025

Automated Parsing of Engineering Drawings for Structured Information Extraction Using a Fine-tuned Document Understanding Transformer

Muhammad Tayyab Khan, Zane Yong, Lequn Chen +3

Accurate extraction of key information from 2D engineering drawings is crucial for high-precision manufacturing. Manual extraction is slow and labor-intensive, while traditional Op…

cs.IR2025

Large Language Model Powered Decision Support for a Metal Additive Manufacturing Knowledge Graph

Muhammad Tayyab Khan, Lequn Chen, Wenhe Feng +1

Metal additive manufacturing (AM) involves complex interdependencies among processes, materials, feedstock, and post-processing steps. However, the underlying relationships and dom…

cs.CE2025

Audio-visual cross-modality knowledge transfer for machine learning-based in-situ monitoring in laser additive manufacturing

Jiarui Xie, Mutahar Safdar, Lequn Chen +2

Various machine learning (ML)-based in-situ monitoring systems have been developed to detect anomalies and defects in laser additive manufacturing (LAM) processes. While multimodal…