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Article
Engineering, Manufacturing
PengYu Wang et al.
Summary: Manufacturing feature recognition is crucial in intelligent manufacturing as it extracts valuable geometric information from solid models, reducing reliance on machines and automation. Features bridge the gap between CAD, CAPP, CAE, and CAM, enabling seamless design-to-manufacture connection. However, existing methods have limitations such as low learning efficiency and accuracy. To address this, a hybrid learning framework called DeepFeature, based on Graph neural network, is proposed for high accuracy and robustness in handling interacting features.
JOURNAL OF MANUFACTURING PROCESSES
(2023)
Article
Engineering, Industrial
Xiaoliang Yan et al.
Summary: Industry 4.0 requires highly autonomous manufacturing process planning. This paper proposes an integrated approach using Autoencoder-based deep generative models and a Siamese Neural Network (SNN) to enable automated manufacturability analysis and machining process selection. The proposed AE-SNN achieves high accuracy in process selection and manufacturability analysis.
JOURNAL OF MANUFACTURING SYSTEMS
(2023)
Article
Engineering, Industrial
Tullio Antonio Maria Tolio et al.
Summary: Platform-based manufacturing is a revolutionary approach that is changing the way production is done. Companies don't know who makes their parts and part manufacturers don't necessarily own the machines. This approach has become a reality due to innovations in manufacturing science and information and communication technologies.
CIRP ANNALS-MANUFACTURING TECHNOLOGY
(2023)
Article
Engineering, Industrial
Jaemin Cho et al.
Summary: This paper proposes a precision object segmentation method to accurately detect objects moving on a conveyor belt. By designing a new backbone network and using a multi-level pooling layer, lightweight and stable real-time detection and precise segmentation are achieved. The experimental results confirm the superior performance of this method.
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, 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)
Review
Engineering, Industrial
Hafiz Mughees Ahmad et al.
Summary: This paper presents a comprehensive survey of deep learning-based object detection methods for industrial applications. It discusses their applications in industrial settings and presents challenges and future trends in the field.
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
Xiaoliang Yan et al.
Manufacturing Letters
(2022)
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
Engineering, Manufacturing
Dheeraj Peddireddy et al.
Summary: The manufacturing industry still relies on human labor and knowledge for Machining Process Identification (MPI), crucial for sourcing qualified suppliers and achieving efficient automated industrial logistic systems. This paper presents a novel two-step MPI system based on 3D Convolutional Neural Networks and transfer learning, demonstrating high accuracy in identifying manufacturability and manufacturing processes.
JOURNAL OF MANUFACTURING PROCESSES
(2021)
Article
Computer Science, Software Engineering
Zhibo Zhang et al.
COMPUTER-AIDED DESIGN
(2018)
Article
Computer Science, Software Engineering
Morad Behandish et al.
COMPUTER-AIDED DESIGN
(2018)
Article
Computer Science, Software Engineering
Y Woo et al.
COMPUTER-AIDED DESIGN
(2002)
Article
Engineering, Industrial
HS Nagaraj et al.
JOURNAL OF MANUFACTURING SYSTEMS
(2001)