Article
Automation & Control Systems
Qing Chen, Zhanzhan Liu, Xin Ma, Youqing Wang
Summary: This article proposes a novel fault detection and process monitoring method called artificial neural correlation analysis (ANCA). By combining artificial neural networks (ANN) and canonical correlation analysis (CCA), this method is able to effectively handle the nonlinear characteristics commonly found in complex industrial processes.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Engineering, Electrical & Electronic
Cong Li, Yina Yao, Rui Yang, Chengwu Li, Hui Zhang
Summary: This study verifies the feasibility of a quantitative fire detection method based on visual flame in confined and inaccessible environments. By extracting the features of flame images and conducting dimensionality reduction, a principal component that has a linear relationship with instantaneous pressure decrease is obtained.
SIGNAL IMAGE AND VIDEO PROCESSING
(2022)
Article
Automation & Control Systems
Xianchao Xiu, Zhonghua Miao, Ying Yang, Wanquan Liu
Summary: This article proposes an efficient nonlinear process monitoring method by integrating DAENNs, CCA, and SCO. The method is demonstrated on the TE process and the diesel generator process, achieving an increased fault detection rate of 8.00% for the fault IDV(11) compared to classical CCA.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Automation & Control Systems
Bing Song, Hongbo Shi, Shuai Tan, Yang Tao
Summary: The article introduces a novel data-driven method called multisubspace orthogonal canonical correlation analysis, which can real-time judge whether faults affect product quality. By dividing the process variable space into four subspaces, conducting orthogonal CCA for feature extraction and monitoring statistics, the method is developed and tested successfully.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Engineering, Mechanical
Ryo Sakakibara, Ichiro Yoshida, Sho Nagai, Yuki Kondo, Kenichi Yamashita
Summary: The study introduces a newly developed algorithm to evaluate the running-in wear process of plateau surfaces, addressing the calculation problems and human judgment required in previous methods. This proposed method offers easy, precise, and direct evaluation of plateau surfaces and is expected to aid in determining optimal machining methods and conditions.
TRIBOLOGY INTERNATIONAL
(2021)
Article
Engineering, Environmental
Ping Wu, Xujie Zhang, Jiajun He, Siwei Lou, Jinfeng Gao
Summary: The paper presents a novel locality preserving randomized canonical correlation analysis (LPRCCA) method for real-time nonlinear process monitoring which maps original data to a randomized low-dimensional feature space and integrates local geometric structure information to improve data mining performance, reducing computational cost and showing significant advantages over kernel-based methods.
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
(2021)
Article
Materials Science, Ceramics
Yang Li, Yanhou Liu, Jinling Wang, Yi Wang, Yebing Tian
Summary: This paper proposes a time-spatial domain spectrum analysis method based on the monitored grinding force signal and grinding surface texture curve, which can accurately predict the ceramic surface roughness. This method is of great value for online measurement of grinding quality in intelligent manufacturing.
CERAMICS INTERNATIONAL
(2022)
Article
Engineering, Multidisciplinary
Sho Nagai, Ichiro Yoshida, Kaito Oshiro, Ryo Sakakibara
Summary: ISO 21920-2 defines an analysis method and roughness parameters for evaluating the roughness of plateau structure surfaces. However, this method is unable to calculate parameters for surfaces with insufficient plateau structure. To address this issue, we propose a new method called RLF method that can automatically and significantly faster calculate roughness parameters for these surfaces, potentially improving productivity at production sites.
Article
Engineering, Electrical & Electronic
Xianchao Xiu, Ying Yang, Lingchen Kong, Wanquan Liu
Summary: A novel process monitoring approach using SJSCCA was proposed to improve monitoring performance. By incorporating structured joint sparse canonical correlation analysis and two-stage alternating direction method of multipliers, useless variables are discarded and fast solutions are achieved.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2021)
Article
Engineering, Chemical
Bing Song, Tao Guo, Hongbo Shi, Yang Tao, Shuai Tan
Summary: With the development of sensor technology and industrial processes, process monitoring has become crucial for ensuring product quality and improving economic efficiency. This study proposes a model called Neighborhood Embedding Canonical Correlation Analysis (NECCA) that combines canonical correlation analysis (CCA) with a neighborhood structure feature extraction algorithm. By incorporating neighborhood information into the traditional CCA model, the NECCA model achieves a more comprehensive feature representation. The rationality and effectiveness of the proposed model are demonstrated through a typical test case.
JOURNAL OF THE TAIWAN INSTITUTE OF CHEMICAL ENGINEERS
(2023)
Article
Chemistry, Multidisciplinary
Le Du, Wenhao Jin, Yang Wang, Qingchao Jiang
Summary: This paper proposes a data-driven time-slice latent variable correlation analysis-based model predictive fault detection framework to ensure accurate fault detection in dynamic batch processes. The proposed framework unfolds the batch process data into time slices, maps the process data to latent variables and residual subspaces, and generates prediction-based residuals to identify the characteristics of detected faults.
Article
Automation & Control Systems
Hongchao Cheng, Jing Wu, Daoping Huang, Yiqi Liu, Qilin Wang
Summary: A novel method called Rab-CCA is proposed for monitoring wastewater treatment processes, which includes a robust decomposition method and an adaptive statistical control limit to improve the performance of standard process monitoring methods, reducing missed alarms and false alarms simultaneously.
Article
Automation & Control Systems
Zhiwen Chen, Chang Liu, Steven X. Ding, Tao Peng, Chunhua Yang, Weihua Gui, Yuri A. W. Shardt
Summary: A new method for monitoring and fault detection of multimode processes is proposed in the article, integrating K-means into just-in-time learning to build local models and addressing limitations of traditional canonical correlation analysis methods in handling processes with multiple operating points. Its effectiveness is demonstrated in an industrial benchmark process, showing better fault detection rate compared to conventional methods.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2021)
Article
Chemistry, Physical
Tomasz Bartkowiak, Karol Grochalski, Bartosz Gapinski, Michal Wieczorowski
Summary: This article explores different multiscale methods for measuring and discriminating topographies of processed steel rings, comparing ISO standard parameters, geometric length and area analyses, and multiscale curvature tensor approach. It was found that bandpass filtration and basic height parameters Sa and Sq could confidently discriminate against various factors at all considered bandwidths.
Article
Chemistry, Physical
Ilaria Caravella, Daniele Cortis, Luca Di Angelo, Donato Orlandi
Summary: This paper presents an experimental analysis of the effects of SLM process parameters on the surface quality of CuCrZr specimens. The results show that different combinations of parameters can significantly affect the surface quality.
Article
Computer Science, Interdisciplinary Applications
Shenglin Wang, Jingqiong Zhang, Peng Wang, James Law, Radu Calinescu, Lyudmila Mihaylova
Summary: In Industry 5.0, Digital Twins provide flexibility and efficiency for smart manufacturing. Deep learning techniques are used to enhance the Digital Twin framework, enabling the detection and classification of human operators and robots during the manufacturing process. The framework shows promising results in accurately detecting and classifying actions of human operators and robots in various scenarios.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2024)
Article
Computer Science, Interdisciplinary Applications
Yi Liu, Junpeng Qiu, Jincheng Wang, Junhe Lian, Zeran Hou, Junying Min
Summary: In this study, a double-sided robotic roller forming process was developed to form ultrahigh strength steels to thin-walled profiles. Synchronized laser heating and iterative path compensation method were used to reduce forming forces and achieve high-precision forming.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2024)
Article
Computer Science, Interdisciplinary Applications
Zequn Zhang, Yuchen Ji, Dunbing Tang, Jie Chen, Changchun Liu
Summary: This paper proposes a digital twin system for human-robot collaboration (HRC) that overcomes the limitations of current methods and improves the overall performance. The system includes a human mesh recovery algorithm and uncertainty estimation to enhance the system's capabilities. Experimental results demonstrate the superiority of the proposed methods over baseline methods. The feasibility and effectiveness of the HRC system are validated through a case study involving component assembly.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2024)
Article
Computer Science, Interdisciplinary Applications
Junmin Park, Taehoon Kim, Chengyan Gu, Yun Kang, Joono Cheong
Summary: This paper proposes a highly reliable and accurate collision estimator for robot manipulators in human-robot collaborative environments using the Bayesian approach. By assuming robot collisions as dynamic Markov processes, the estimator can integrate prior beliefs and measurements to produce current beliefs in a recursive form. The method achieves compelling performance in collision estimation with high accuracy and no false alarms.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2024)
Article
Computer Science, Interdisciplinary Applications
Meng Wang, Kaixuan Chen, Panfeng Wang, Yimin Song, Tao Sun
Summary: In this study, a novel teleoperation machining mode and control strategy were proposed to improve efficiency and accuracy in small batch production of large casting parts. By using variable motion mapping and elastic compensation, constant cutting force was achieved, and the workpiece was protected by employing forbidden virtual fixtures and movement constraints on the slave robot.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2024)
Article
Computer Science, Interdisciplinary Applications
Zhaoyu Li, Dong He, Xiangyu Li, Xiaoke Deng, Pengcheng Hu, Jiancheng Hao, Yue Hou, Hongyu Yu, Kai Tang
Summary: This paper presents a novel algorithm for planning a five-axis inspection path for arbitrary freeform surfaces. By converting the inspection path planning problem into a set-covering problem, the algorithm generates a near-minimum set of inspection paths that satisfy necessary constraints. Both computer simulation and physical inspection experiments confirm the effectiveness and advantages of the proposed method.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2024)
Article
Computer Science, Interdisciplinary Applications
Hang Zhang, Wenhu Wang, Shusheng Zhang, Yajun Zhang, Jingtao Zhou, Zhen Wang, Bo Huang, Rui Huang
Summary: This paper introduces a novel framework based on deep reinforcement learning for generating machining process routes for designated parts. The framework utilizes graph representations of parts and employs convolutional graph neural networks for effective processing. Experimental results demonstrate the ability of the proposed method to generate efficient machining process routes and overcome limitations of traditional methods.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2024)
Article
Computer Science, Interdisciplinary Applications
Regina Kyung-Jin Lee, Hao Zheng, Yuqian Lu
Summary: Future manufacturing will witness a shift towards collaboration and compassion in human-robot relationships. To enable seamless knowledge transfer, a unified knowledge representation system that can be shared by humans and robots is essential. The Human-Robot Shared Assembly Taxonomy (HR-SAT) proposed in this study allows comprehensive assembly tasks to be represented as a knowledge graph that is understandable by both humans and robots. HR-SAT incorporates rich assembly information and has diverse applications in process planning, quality checking, and human-robot collaboration.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2024)
Article
Computer Science, Interdisciplinary Applications
Jianhui He, Lefeng Gu, Guilin Yang, Yiyang Feng, Silu Chen, Zaojun Fang
Summary: This paper presents a new modular kinematic error model for collaborative robots and proposes a portable self-calibration device to improve their positioning accuracy.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2024)
Article
Computer Science, Interdisciplinary Applications
Hongwei Sun, Jixiang Yang, Han Ding
Summary: This paper proposes an asymmetrical FIR filter-based tool path smoothing algorithm to fully utilize the joint drive capability of robot manipulators. The algorithm considers the pose-dependent dynamics and constraints of the robot and improves motion efficiency by over 10% compared to traditional methods.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2024)
Article
Computer Science, Interdisciplinary Applications
Dongsheng Ge, Huan Zhao, Yiwei Wang, Dianxi Li, Xiangfei Li, Han Ding
Summary: This paper focuses on learning a stable force control policy from human demonstration during contact transients. Based on the analysis of human demonstration data, a novel human-inspired force control strategy called compliant dynamical system (CDS) is proposed. The effectiveness of the proposed method is validated through simulation and real-world experiments.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2024)
Article
Computer Science, Interdisciplinary Applications
Xuepeng Huang, Zhenzhong Wang, Lucheng Li, Qi Luo
Summary: This study models the stiffness of a robot and modifies the tool influence function (TIF) with the Preston equation in order to achieve uniform surface quality in robotic bonnet polishing (RBP) of optical components. Experimental results validate the accuracy of the modified model.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2024)
Article
Computer Science, Interdisciplinary Applications
Mario D. Fiore, Felix Allmendinger, Ciro Natale
Summary: This paper presents a constraint-based programming framework for task specification and motion optimization. The framework can handle constraints on robot joint and Cartesian coordinates, as well as time dependency. It also compares with existing methods and provides numerical support through illustrative examples and case studies.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2024)
Article
Computer Science, Interdisciplinary Applications
Yongxue Chen, Yaoan Lu, Ye Ding
Summary: This paper presents an optimization method for directly generating a six-degree-of-freedom toolpath for robotic flank milling. By optimizing the smoothness of the toolpath and the stiffness of the robot, the efficiency, accuracy, and finish of the machining are improved.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2024)
Article
Computer Science, Interdisciplinary Applications
Chungang Zhuang, Haoyu Wang, Han Ding
Summary: This article proposes an end-to-end pipeline for synchronously regressing potential object poses from an unsegmented point cloud. It extracts point pair features and uses a voting architecture for instance feature extraction, along with a 3D heatmap for clustering votes and generating center seeds. An attention voting module is also employed to adaptively fuse point-wise features into instance-wise features. The network demonstrates robustness and improved performance in pose estimation.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2024)