2 citations · 3 across the 2 of their papers we have counts for
6 papers
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…
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…
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…
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…
Fine-Tuning Vision-Language Model for Automated Engineering Drawing Information Extraction
Muhammad Tayyab Khan, Lequn Chen, Ye Han Ng +3
Geometric Dimensioning and Tolerancing (GD&T) plays a critical role in manufacturing by defining acceptable variations in part features to ensure component quality and functionalit…
Leveraging Vision-Language Models for Manufacturing Feature Recognition in CAD Designs
Muhammad Tayyab Khan, Lequn Chen, Ye Han Ng +3
Automatic feature recognition (AFR) is essential for transforming design knowledge into actionable manufacturing information. Traditional AFR methods, which rely on predefined geom…