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Article
Computer Science, Interdisciplinary Applications
Wentai Zhang et al.
Summary: We propose a data-driven framework for automating the vectorization and machine interpretation of 2D engineering part drawings. This framework combines image-based computer vision methods with deep learning to accurately interpret and classify components in engineering drawings. The results show that our framework outperforms recent image-based and graph-based segmentation methods.
COMPUTERS IN INDUSTRY
(2023)
Article
Computer Science, Artificial Intelligence
Yuanbin Wang et al.
Summary: This paper proposes a high-efficient view planning method based on deep reinforcement learning to solve the problem of complex product surface defect detection. The proposed method effectively reduces the viewpoints while ensuring coverage, and its effectiveness and efficiency are verified through experiments.
ADVANCED ENGINEERING INFORMATICS
(2023)
Article
Automation & Control Systems
Yuki Kadokawa et al.
Summary: Cyclic policy distillation (CPD) is a sample-efficient method that learns control policies in multiple simulated environments. It accelerates learning by dividing the range of randomized parameters into small sub-domains and assigning a local policy to each sub-domain. CPD then transitions cyclically between sub-domains and distills all learned local policies into a global policy for sim-to-real transfers.
ROBOTICS AND AUTONOMOUS SYSTEMS
(2023)
Article
Engineering, Industrial
Xu Liu et al.
Summary: This research presents a new data-driven method for constructing machining regions by learning from historical data, eliminating the need for domain-specific expert-defined rules or strategies. Experimental results confirm the practicality of the proposed method in automatic machining region construction and suggest its potential for extension to parts in other domains.
JOURNAL OF MANUFACTURING SYSTEMS
(2022)
Article
Engineering, Industrial
Yu Zhang et al.
Summary: This paper presents an intelligent feature recognition method for STEP-NC-compliant manufacturing using artificial bee colony (ABC) algorithm and back propagation (BP) neural network. The method extracts geometric and topological information from STEP AP203 neutral file, constructs the minimum subgraphs of a part based on the concavity and convexity judgement algorithm, and proposes an improved BP neural network combined with ABC algorithm for STEP-NC-compliant manufacturing feature recognition. Case study concludes that the proposed method is effective and feasible.
JOURNAL OF MANUFACTURING SYSTEMS
(2022)
Article
Computer Science, Interdisciplinary Applications
Peizhi Shi et al.
Summary: In the field of intelligent manufacturing, recognizing interacting features on a CAD model is a critical yet challenging task. Some learning methods struggle with highly interacting features and require a large number of 3D models for training. The proposed method RDetNet can effectively recognize highly interacting features with small training samples.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2022)
Article
Computer Science, Software Engineering
Andrew R. Colligan et al.
Summary: This paper introduces a novel hierarchical B-Rep graph shape representation and applies it to feature identification tasks, achieving improved performance.
COMPUTER-AIDED DESIGN
(2022)
Article
Computer Science, Artificial Intelligence
Dongmin Lee et al.
Summary: This paper explores the potential of deep reinforcement learning (DRL) for adaptive task allocation in dynamic robotic construction environments using a digital twin-driven learning method. The results of testing on a virtual robotic construction project showed that the DRL model's task allocation approach reduced construction time by 36% compared to a rule-based imperative model.
ADVANCED ENGINEERING INFORMATICS
(2022)
Article
Computer Science, Interdisciplinary Applications
Qinglei Ji et al.
Summary: This paper proposes a new design for quadruped robots using soft actuators driven by tendons as the four legs. The advantages of the proposed soft quadruped robot are inherent safety, less weight, and simpler mechanism, but the challenge lies in deriving the accurate mathematical model for control design. The paper makes contributions in modeling the flexible components, optimizing gaits using soft actor-critic methods, and implementing the learned gaits in a real robot.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2022)
Article
Computer Science, Interdisciplinary Applications
Yi Zhang et al.
Summary: This paper proposes a multi-agent manufacturing system based on deep reinforcement learning to address the production planning and control problems caused by personalized orders. The system, with the cooperation and competition among multiple equipment agents and the decision-making module of AI scheduler, efficiently performs task allocation and continuously improves decision-making performance through training and updating.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2022)
Article
Computer Science, Artificial Intelligence
Shengluo Yang et al.
Summary: This study proposes a solution to the distributed permutation flowshop scheduling problem using deep reinforcement learning. By designing suitable reward function and scheduling actions, an intelligent scheduling agent is trained, which significantly improves the solution quality and computation efficiency.
ADVANCED ENGINEERING INFORMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Ruixin Wang et al.
Summary: This paper proposes a deep feature enhanced reinforcement learning method for fault diagnosis of rolling bearing. The method improves the neural network with the Elu activation function and utilizes an attention model to enhance the feature and acquire global information accurately.
ADVANCED ENGINEERING INFORMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Zisheng Wang et al.
Summary: This paper proposes an improved deep reinforcement learning method for multi-label classification. The method achieves higher accuracy in fault recognition tasks under complex working conditions. By designing an action history vector, the method establishes relationships between predicted labels of input samples and utilizes the curriculum learning mechanism to distinguish labels from easy to complex.
ADVANCED ENGINEERING INFORMATICS
(2022)
Article
Computer Science, Interdisciplinary Applications
Liuyue Xie et al.
Summary: This study proposes a computational framework to automatically determine the appropriate manufacturing method for custom mechanical parts, achieving an accuracy of 90.78% in classifying engineering drawings. The framework uses data-driven approaches and a series of pre-processing steps to process raster images, followed by graph neural networks to accurately determine the manufacturing methods.
COMPUTERS IN INDUSTRY
(2022)
Article
Computer Science, Interdisciplinary Applications
Tongming Xu et al.
Summary: This study investigates a systematic AFR method for machining parts based on process semantics, which converts the part MBD model to a structuralized feature model (SFM). It provides a new definition of machining features and proposes detailed data extraction and preprocessing methods. The study also presents a new classification methodology for identifying feature geometries and discusses the recognition methods for high-level composite features. The results of feature recognition can be directly applied for downstream machining process planning.
COMPUTERS IN INDUSTRY
(2022)
Article
Engineering, Industrial
Hang Zhang et al.
Summary: This article presents a deep learning-based approach for estimating manufacturing costs, with a focus on the precision information of parts. The approach defines an attribute graph to represent the CAD model of a part and constructs a ConvGNN framework called Cost Estimation Network (CEN) that combines spectral-based and spatial-based convolutional layers. The trained CEN can accurately estimate manufacturing costs, and a modified Grad-CAM process is developed to explain the rationale behind cost decisions. Experimental studies using CNC machined rotary parts validate the feasibility and effectiveness of the approach.
JOURNAL OF MANUFACTURING SYSTEMS
(2022)
Article
Computer Science, Interdisciplinary Applications
Hang Zhang et al.
Summary: This paper proposes a novel multi-task network named ASIN based on point cloud data for machining feature recognition. By combining the tasks of segmentation, identification, and bottom face identification, ASIN can simultaneously achieve feature segmentation, identification, and bottom face identification. Experimental results demonstrate that the proposed method effectively segments machining features and performs well in recognizing intersecting machining features.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2022)
Article
Computer Science, Interdisciplinary Applications
Jifei Deng et al.
Summary: This paper proposes a novel approach that combines ensemble learning with reinforcement learning methods for strip rolling control. The multi-actor PPO method is introduced to improve the control effectiveness. Simulation results demonstrate that the proposed method outperforms conventional control methods and state-of-the-art reinforcement learning methods in terms of process capability and smoothness.
COMPUTERS IN INDUSTRY
(2022)
Article
Computer Science, Interdisciplinary Applications
Liang Guo et al.
Summary: The paper introduces a new hybrid 3D feature recognition method for recognizing machining features, which addresses the limitations of existing automatic feature recognition methods. By using reverse modeling for feature classification, proposing the WAAM data structure and recognition rules, and conducting instance tests, the feasibility and efficiency of the method are validated.
INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING
(2021)
Article
Computer Science, Artificial Intelligence
Yulan Guo et al.
Summary: Recently, deep learning on point clouds has been gaining more attention, but it is still in its early stages due to the unique challenges faced in processing point clouds with deep neural networks.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Zonghan Wu et al.
Summary: This article provides a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields. It discusses the taxonomy of GNNs, their applications, and summarizes open-source codes, benchmark data sets, and model evaluation. The article also proposes potential research directions in this rapidly growing field.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Automation & Control Systems
Peizhi Shi et al.
Summary: In this study, a novel deep learning approach named SsdNet is proposed to tackle the machining feature localization and recognition problem, achieving state-of-the-art performance in feature recognition and localization. The method modifies the network architecture and output of SSD, and utilizes advanced techniques to enhance recognition performance.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Zisheng Wang et al.
Summary: The study proposes a method that combines deep learning with reinforcement learning to enhance fault recognition accuracy, allowing the model to better learn hidden knowledge in raw data. Through the improved actor-critic algorithm and multi-channel data processing method, the proposed network structure can more accurately identify compound faults in machine tool systems under heavy background noise.
ADVANCED ENGINEERING INFORMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Quan Liu et al.
Summary: This paper introduces a deep reinforcement learning approach for real-time collision-free motion planning of an industrial robot, aiming to ensure operator safety in human-robot collaboration in manufacturing. By optimizing the reward function and combining the DDPG algorithm, the proposed IRDDPG algorithm allows the robot to learn an expected collision avoidance policy effectively in a simulation environment.
ADVANCED ENGINEERING INFORMATICS
(2021)
Article
Computer Science, Information Systems
Shuhui Ding et al.
Summary: Automatic Feature Recognition (AFR) is considered a key technique for integrating Computer Aided Design (CAD) and Computer Aided Process Planning (CAPP). This paper proposes a novel Model Based Definition (MBD) method based on 3D CAD model AFR and similarity evaluation, using a Multi-Dimensional Attributed Adjacency Matrix (MDAAM) that fully considers various process information of the B-rep model.
Proceedings Paper
Computer Science, Artificial Intelligence
Youshan Zhang et al.
Summary: The article mainly discusses how to solve the problem of domain shift by selecting the most relevant features through reinforcement learning, and proposes adversarial distribution alignment learning to improve prediction results. Extensive experiments show that the proposed method outperforms existing state-of-the-art methods.
2021 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2021)
(2021)
Article
Computer Science, Interdisciplinary Applications
Ahmad Faiz Zubair et al.
ENGINEERING WITH COMPUTERS
(2020)
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JOURNAL OF INTELLIGENT MANUFACTURING
(2020)
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Guang Yang et al.
PATTERN RECOGNITION
(2020)
Article
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Yang Shi et al.
CIRP JOURNAL OF MANUFACTURING SCIENCE AND TECHNOLOGY
(2020)
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Soonjo Kwon et al.
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(2019)
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ACM TRANSACTIONS ON GRAPHICS
(2019)
Proceedings Paper
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Guohao Li et al.
2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2019)
(2019)
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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2017)
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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2016)
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(2015)
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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2014)
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INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING
(2010)
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COMPUTERS IN INDUSTRY
(2008)