2 citations · 6 across the 10 of their papers we have counts for
10 papers
Design-to-Plan: A Large Language Model-Based Multi-Agent Framework for Manufacturing Process Planning from 3D CAD Models and 2D Engineering Drawings
Muhammad Tayyab Khan, Lequn Chen, Wenhe Feng +1
Manufacturing process planning transforms heterogeneous design information into coherent manufacturing decisions. However, existing approaches focus on isolated subtasks, such as f…
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…
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…
Foundation Model Driven Robotics: A Comprehensive Review
Muhammad Tayyab Khan, Ammar Waheed
The rapid emergence of foundation models, particularly Large Language Models (LLMs) and Vision-Language Models (VLMs), has introduced a transformative paradigm in robotics. These m…
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…
Automatic Feature Recognition and Dimensional Attributes Extraction From CAD Models for Hybrid Additive-Subtractive Manufacturing
Muhammad Tayyab Khan, Wenhe Feng, Lequn Chen +3
The integration of Computer-Aided Design (CAD), Computer-Aided Process Planning (CAPP), and Computer-Aided Manufacturing (CAM) plays a crucial role in modern manufacturing, facilit…