172 citations · 300 across the 14 of their papers we have counts for
14 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…
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…
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…
In-situ process monitoring and adaptive quality enhancement in laser additive manufacturing: a critical review
Lequn Chen, Guijun Bi, Xiling Yao +6
Laser Additive Manufacturing (LAM) presents unparalleled opportunities for fabricating complex, high-performance structures and components with unique material properties. Despite…
Multimodal sensor fusion for real-time location-dependent defect detection in laser-directed energy deposition
Lequn Chen, Xiling Yao, Wenhe Feng +2
Real-time defect detection is crucial in laser-directed energy deposition (L-DED) additive manufacturing (AM). Traditional in-situ monitoring approach utilizes a single sensor (i.e…
Multisensor fusion-based digital twin in additive manufacturing for in-situ quality monitoring and defect correction
Lequn Chen, Xiling Yao, Kui Liu +2
Early detection and correction of defects are critical in additive manufacturing (AM) to avoid build failures. In this paper, we present a multisensor fusion-based digital twin for…
In-situ crack and keyhole pore detection in laser directed energy deposition through acoustic signal and deep learning
Lequn Chen, Xiling Yao, Chaolin Tan +6
Cracks and keyhole pores are detrimental defects in alloys produced by laser directed energy deposition (LDED). Laser-material interaction sound may hold information about underlyi…