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Transferable multi-objective factory layout planning using simulation-based deep reinforcement learning J. Manuf. Syst. (IF 12.1) Pub Date : 2024-04-20 Matthias Klar, Philipp Schworm, Xiangqian Wu, Peter Simon, Moritz Glatt, Bahram Ravani, Jan C. Aurich
Factory layout planning aims at finding an optimized layout configuration under consideration of varying influences such as the material flow characteristics. Manual layout planning can be characterized as a complex decision-making process due to a large number of possible placement options. Automated planning approaches aim at reducing the manual planning effort by generating optimized layout variants
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Machine learning applications on IoT data in manufacturing operations and their interpretability implications: A systematic literature review J. Manuf. Syst. (IF 12.1) Pub Date : 2024-04-18 Anna Presciuttini, Alessandra Cantini, Federica Costa, Alberto Portioli-Staudacher
Industry 4.0 has transformed manufacturing with real-time plant data collection across operations and effective analysis is crucial to unlock the full potential of Internet-of-Things (IoT) sensor data, integrating IoT with Artificial Intelligence (AI) techniques, such as Machine Learning (ML) and Deep Learning (DL). They can provide powerful predictions but anticipating issues is not enough. Manufacturing
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Experimental analysis of augmented reality interfaces for robot programming by demonstration in manufacturing J. Manuf. Syst. (IF 12.1) Pub Date : 2024-04-18 Chih-Hsing Chu, Chen-Yu Weng
Augmented Reality (AR) technology has been effectively utilized to support various manual operations in the manufacturing industry. An important application is serving as a user interface for human-robot collaboration. This paper presents an experimental study on the feasibility of robot programming by demonstration (PbD) through AR interfaces in the context of manufacturing. Our focus is on comparing
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A systematic methodology for changeable and reconfigurable manufacturing systems development J. Manuf. Syst. (IF 12.1) Pub Date : 2024-04-15 Rasmus Andersen, Alessia Napoleone, Ann-Louise Andersen, Thomas Ditlev Brunoe, Kjeld Nielsen
Pursuing manufacturing competitiveness in the dynamic industrial landscape necessitates implementing changeable and reconfigurable manufacturing systems (RMS) capable of rapid adaptation to varying functionalities and capacities. However, current manufacturing system development methods often overlook product-driven changes during the system's life cycle, hindering companies from effectively responding
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Personalized feature extraction for manufacturing process signature characterization and anomaly detection J. Manuf. Syst. (IF 12.1) Pub Date : 2024-04-15 Naichen Shi, Shenghan Guo, Raed Al Kontar
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AF-FTTSnet: An end-to-end two-stream convolutional neural network for online quality monitoring of robotic welding J. Manuf. Syst. (IF 12.1) Pub Date : 2024-04-13 Yuxiang Hong, Xingxing He, Jing Xu, Ruiling Yuan, Kai Lin, Baohua Chang, Dong Du
Online welding quality monitoring (WQM) is crucial for intelligent welding, and deep learning approaches considering spatiotemporal features for WQM tasks show great potential. However, one of the important challenges for existing approaches is to balance the spatiotemporal representation learning capability and computational efficiency, which makes it challenging to adapt welding processes with complex
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Unleashing mixed-reality capability in Deep Reinforcement Learning-based robot motion generation towards safe human–robot collaboration J. Manuf. Syst. (IF 12.1) Pub Date : 2024-04-12 Chengxi Li, Pai Zheng, Peng Zhou, Yue Yin, Carman K.M. Lee, Lihui Wang
The integration of human–robot collaboration yields substantial benefits, particularly in terms of enhancing flexibility and efficiency within a range of mass-personalized manufacturing tasks, for example, small-batch customized product inspection and assembly/disassembly. Meanwhile, as human–robot collaboration lands broader in manufacturing, the unstructured scene and operator uncertainties are increasingly
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A hybrid deep learning approach to integrate predictive maintenance and production planning for multi-state systems J. Manuf. Syst. (IF 12.1) Pub Date : 2024-04-11 Hassan Dehghan Shoorkand, Mustapha Nourelfath, Adnène Hajji
This paper develops a data-driven approach to dynamically integrate tactical production and predictive maintenance planning for a multi-state system composed of several series-parallel machines. The objective is to determine an integrated lot-sizing and preventive maintenance strategy that will minimize the sum of maintenance and production costs, while satisfying the demand for all products over the
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Towards a green electromobility transition: A systematic review of the state of the art on electric vehicle battery systems disassembly J. Manuf. Syst. (IF 12.1) Pub Date : 2024-04-11 Dominik Hertel, Gerald Bräunig, Matthias Thürer
To achieve the sustainable development goals, there is a need to realize a green electromobility transition. Yet, this alone is not enough. There is also the question on how to manage end-of-life battery systems to reduce environmental pollution caused by improper disposal. A major challenge is battery disassembly, which precedes any reuse or recycling. If this disassembly is not cost-effective, cost-efficient
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Tool wear and remaining useful life estimation in precision machining using interacting multiple model J. Manuf. Syst. (IF 12.1) Pub Date : 2024-04-10 Qian Yang, Debasish Mishra, Utsav Awasthi, George M. Bollas, Krishna R. Pattipati
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A chance-constraint programming approach for a disassembly line balancing problem under uncertainty J. Manuf. Syst. (IF 12.1) Pub Date : 2024-04-10 Xuesong Zhang, Guangdong Tian, Amir M. Fathollahi-Fard, Duc Truong Pham, Zhiwu Li, Yongfeng Pu, Tongzhu Zhang
Against the backdrop of escalating global imperatives for resource conservation and environmental sustainability, the strategies of recycling and remanufacturing have risen to prominence as crucial solutions, particularly in tackling the challenges presented by end-of-life (EOL) products. At the heart of these operations lies the intricate process of product disassembly, where the optimal allocation
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A novel collaborative agent reinforcement learning framework based on an attention mechanism and disjunctive graph embedding for flexible job shop scheduling problem J. Manuf. Syst. (IF 12.1) Pub Date : 2024-04-08 Wenquan Zhang, Fei Zhao, Yong Li, Chao Du, Xiaobing Feng, Xuesong Mei
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A deep reinforcement learning-driven multi-objective optimization and its applications on aero-engine maintenance strategy J. Manuf. Syst. (IF 12.1) Pub Date : 2024-04-06 Zeqi Wei, Zhibin Zhao, Zheng Zhou, Jiaxin Ren, Yajun Tang, Ruqiang Yan
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Behavior-environment interaction aware manufacturing service collaboration optimization J. Manuf. Syst. (IF 12.1) Pub Date : 2024-04-06 Bo Liu, Yongping Zhang, Guojun Sheng, Ying Cheng, Fei Tao
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Manufacturing service capability prediction with Graph Neural Networks J. Manuf. Syst. (IF 12.1) Pub Date : 2024-04-05 Yunqing Li, Xiaorui Liu, Binil Starly
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An improved memetic algorithm for multi-objective resource-constrained flexible job shop inverse scheduling problem: An application for machining workshop J. Manuf. Syst. (IF 12.1) Pub Date : 2024-03-27 Shupeng Wei, Hongtao Tang, Xixing Li, Deming Lei, Xi Vincent Wang
Resource-constrained flexible job shop scheduling problems are commonly encountered in some manufacturing industries, and have been widely studied in recent years. However, traditional resource constrained flexible job shop scheduling problem rarely consider the uncertainties in actual manufacturing systems, which may make the original schedule become suboptimal or even unfeasible. Therefore, a resource
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An improved genetic programming hyper-heuristic for the dynamic flexible job shop scheduling problem with reconfigurable manufacturing cells J. Manuf. Syst. (IF 12.1) Pub Date : 2024-03-26 Haoxin Guo, Jianhua Liu, Yue Wang, Cunbo Zhuang
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A review: Insight into smart and sustainable ultra-precision machining augmented by intelligent IoT J. Manuf. Syst. (IF 12.1) Pub Date : 2024-03-22 Zhicheng Xu, Tong Zhu, Fan Louis Luo, Baolong Zhang, Hiuying Poon, Wai Sze Yip, Suet To
Ultra-precision machining (UPM), which is capable of fabricating micro-components with less than 0.2 µm forming accuracy and 10 nm surface accuracy, is becoming increasingly important due to its indispensable and widespread application in various high-tech fields such as optics, electrics, and semiconductor. However, the low energy and machining efficiency of UPM is getting more prominent. Current
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Multi-objective optimization of machining parameters based on an improved Hopfield neural network for STEP-NC manufacturing J. Manuf. Syst. (IF 12.1) Pub Date : 2024-03-21 Yu Zhang, Guojun Du, Hongqiang Li, Yuanxin Yang, Hongfu Zhang, Xun Xu, Yadong Gong
Aiming at solving the existing problems of machining parameters optimization for STEP-NC manufacturing, a method for multi-objective optimization of machining parameters based on an improved Hopfield neural network (IHNN) for STEP-NC manufacturing is proposed. In this method, a multi-objective optimization mathematical model of machining parameters compliant with STEP-NC is firstly established taking
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Time series prediction for production quality in a machining system using spatial-temporal multi-task graph learning J. Manuf. Syst. (IF 12.1) Pub Date : 2024-03-19 Pei Wang, Qianle Zhang, Hai Qu, Xun Xu, Sheng Yang
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Flexible job shop scheduling with stochastic machine breakdowns by an improved tuna swarm optimization algorithm J. Manuf. Syst. (IF 12.1) Pub Date : 2024-03-19 Chengshuai Fan, Wentao Wang, Jun Tian
In job-shop production environments, machine breakdowns are a significant factor in reducing productivity. Existing approaches seldom consider algorithm improvement and rescheduling scheme design in an integrated manner, and lack stability considerations. This paper addresses the flexible job shop scheduling problem with random machine breakdowns, aiming to produce a stable rescheduling scheme that
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From Nash Q-learning to nash-MADDPG: Advancements in multiagent control for multiproduct flexible manufacturing systems J. Manuf. Syst. (IF 12.1) Pub Date : 2024-03-15 Muhammad Waseem, Qing Chang
The emergence of flexible manufacturing systems (FMS) capable of processing multiple product types is a result of the growing demand for product customization and personalization. Such multiproduct systems are characterized by a higher level of uncertainty and variability when compared to traditional manufacturing systems. This paper proposes a Nash integrated multiagent deep deterministic policy gradient
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Data-driven carbon emission accounting for manufacturing systems based on meta-carbon-emission block J. Manuf. Syst. (IF 12.1) Pub Date : 2024-03-15 Weiwei Ge, Huajun Cao, Hongcheng Li, Qiongzhi Zhang, Xuanhao Wen, Chaoyong Zhang, Paul Mativenga
In the context of carbon neutrality and sustainable manufacturing, there is an urgent need for carbon emission accounting in the manufacturing industry, especially in high-energy, low-efficiency, and high-carbon emission manufacturing systems. However, the dynamics and coupling of carbon emissions caused by the multiple characteristics of manufacturing systems increase the challenges of carbon emission
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A novel adaptive deep transfer learning method towards thermal error modeling of electric spindles under variable conditions J. Manuf. Syst. (IF 12.1) Pub Date : 2024-03-14 Shuai Ma, Jiewu Leng, Zhuyun Chen, Bo Li, Ding Zhang, Weihua Li, Qiang Liu
Thermal error modeling (TEM) plays a vital role in maintaining the machining accuracy of electric spindles. Recently, deep learning (DL) techniques have obtained promising achievements in this area. However, DL techniques have certain limitations. The data acquired from variable working conditions present large distribution discrepancies, the DL-based model established on one working condition fails
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Profile extraction and defect detection for stereolithography curing process based on multi-regularized tensor decomposition J. Manuf. Syst. (IF 12.1) Pub Date : 2024-03-10 Yinwei Zhang, Tao Zhang, Jian Liu, Wenjun Kang, Rongguang Liang, Barrett G. Potter
Optical lenses cured from the stereolithography process are still at their primitive stage, where the detection of process faults and product defects is of great importance. Such needed capability is enabled by an process monitoring system with advanced camera sensors that collect high-dimensional images of the outer geometric profile and/or inner defects of the cured lenses. The state-of-the-art methods
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Ensemble learning-based stability improvement method for feature selection towards performance prediction J. Manuf. Syst. (IF 12.1) Pub Date : 2024-03-08 Feng Xiang, Yulong Zhao, Meng Zhang, Ying Zuo, Xiaofu Zou, Fei Tao
The uncertainty and complexity of real data collected in the industrial production process increase the difficulty in data-based knowledge discovering. Feature selection is an important step to remove redundant and irrelevant data, and thus it is essential to construct an efficient feature selection method. In this paper, an ensemble learning-driven stable feature selection method is proposed to improve
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Research on establishment of digital-twin system for intelligent control of cutting tools sintering process driven by data-model combination J. Manuf. Syst. (IF 12.1) Pub Date : 2024-03-08 Chao Niu, Rongyi Li, Mingqiu Dai, Xianli Liu, Bo Zhou, Peining Wei, Xudong Zhao, Erliang Liu
In the manufacturing process of tungsten cemented carbide cutting tools, the changes in temperature, pressure and vacuum during the sintering process of the cutting tools can have a significant impact on the quality of the cutting tools. This effect is particularly significant in the vacuum sintering stage, so this paper focuses on cutting tools sintering in the vacuum stage. The cutting tools sintering
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Empowering digital twins with large language models for global temporal feature learning J. Manuf. Syst. (IF 12.1) Pub Date : 2024-03-08 Yicheng Sun, Qi Zhang, Jinsong Bao, Yuqian Lu, Shimin Liu
Digital Twin (DT), as an efficient technology for virtual-physical interaction, has demonstrated significant application potential in various industries. Intelligent agent-driven digital twin systems excel in analysis, decision-making, and control, making them highly suitable for manufacturing resource scheduling, diagnostic decision-making, and other requirements. However, current intelligent agents
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X-SEM: A modeling and simulation-based system engineering methodology J. Manuf. Syst. (IF 12.1) Pub Date : 2024-03-07 Pengfei Gu, Zhen Chen, Lin Zhang, Yuteng Zhang, Kunyu Xie, Chun Zhao, Fei Ye, Yiran Tao
Model-based systems engineering (MBSE) is an effective approach that facilitates complex systems’ collaborative research and development. The prevalent MBSE methodologies rely heavily on the System Modeling Language (SysML). However, SysML, characterized as a semi-formal language, presents challenges in facilitating the modeling and simulation of the physical characteristics of complex systems. Consequently
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Adaptive edge finishing process on distorted features through robot-assisted computer vision J. Manuf. Syst. (IF 12.1) Pub Date : 2024-03-06 Mikel González, Adrián Rodríguez, Unai López-Saratxaga, Octavio Pereira, Luis Norberto López de Lacalle
Robotic deburring has emerged as a transformative solution in finishing processes, offering independence from operator influence to enhance product quality and reduce production costs. To do this, contour tracking is key to ensuring cutting tool contact and obtaining chamfers within narrow tolerances, especially on high added-value parts such as aeronautical turbomachinery. The persistent challenge
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A novel data augmentation framework for remaining useful life estimation with dense convolutional regression network J. Manuf. Syst. (IF 12.1) Pub Date : 2024-03-05 Jie Shang, Danyang Xu, Haobo Qiu, Liang Gao, Chen Jiang, Pengxing Yi
Deep learning-based methods play an increasingly significant role in prognostic and health management, enabling accurate and rapid estimation of the remaining useful life (RUL) without relying on prior knowledge. In general, sufficient labeled samples are always needed to ensure the successful application of these methods, but the labeled samples are often difficult to obtain in practical engineering
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Spatial-temporal traceability for cyber-physical industry 4.0 systems J. Manuf. Syst. (IF 12.1) Pub Date : 2024-03-01 Zhiheng Zhao, Mengdi Zhang, Wei Wu, George Q. Huang, Lihui Wang
The COVID-19 outbreak has posed significant challenges to end-to-end global supply chain visibility and transparency, with city lockdowns, factory shutdowns, flight cancellations, cross-border closures, and other uncertainties, disruptions, and disturbances. To address these challenges, reliable and accurate spatial-temporal information of physical objects and processes is essential to understand the
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Unlocking the power of industrial artificial intelligence towards Industry 5.0: Insights, pathways, and challenges J. Manuf. Syst. (IF 12.1) Pub Date : 2024-02-29 Jiewu Leng, Xiaofeng Zhu, Zhiqiang Huang, Xingyu Li, Pai Zheng, Xueliang Zhou, Dimitris Mourtzis, Baicun Wang, Qinglin Qi, Haidong Shao, Jiafu Wan, Xin Chen, Lihui Wang, Qiang Liu
With the continuous development of human-centric, resilient, and sustainable manufacturing towards Industry 5.0, Artificial Intelligence (AI) has gradually unveiled new opportunities for additional functionalities, new features, and tendencies in the industrial landscape. On the other hand, the technology-driven Industry 4.0 paradigm is still in full swing. However, there exist many unreasonable designs
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The progress and trend of digital twin research over the last 20 years: A bibliometrics-based visualization analysis J. Manuf. Syst. (IF 12.1) Pub Date : 2024-02-29 Zeyu Sun, Runtong Zhang, Xiaomin Zhu
Digital Twin (DT) is an increasingly popular technology in both academia and industry due to its potential to facilitate the realization of advanced concepts such as digitization, intelligence, and service. This paper uses bibliometrics and visual methods to analyze the progress and trends of DT research. Data was collected from the Web of Science Core Collection, comprising 10,840 papers published
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Digital control system of tension during the formation of flexible-oriented three-dimensional composite preforms J. Manuf. Syst. (IF 12.1) Pub Date : 2024-02-26 Siyuan Li, Zhongde Shan, Dong Du, Baohua Chang, Li Wang
Tension control is a main event during the formation of a flexible-oriented three-dimensional composite preform, which determines the forming quality of preform. However, control error and fluctuation of existing carbon fiber control system cannot meet the needs of weaving flexible-oriented three-dimensional preforms, which is prone to cause poor consistency of the edge of preform. In this research
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Hybrid quantum particle swarm optimization and variable neighborhood search for flexible job-shop scheduling problem J. Manuf. Syst. (IF 12.1) Pub Date : 2024-02-26 Yuanxing Xu, Mengjian Zhang, Ming Yang, Deguang Wang
The rise and integration of Industry 4.0 has led to a growing focus on the flexible job-shop scheduling problem (FJSP). As an extension of the classic job-shop scheduling problem, FJSP is recognized as an NP-hard problem. Swarm intelligence algorithms provide a robust and adaptable approach for addressing the FJSP, generating approximate solutions near the optima within significantly less computing
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Deep learning-based augmented reality work instruction assistance system for complex manual assembly J. Manuf. Syst. (IF 12.1) Pub Date : 2024-02-24 Wang Li, Aibo Xu, Ming Wei, Wei Zuo, Runsheng Li
The manual assembly process of complex products is lengthy, the assembly requirements are difficult to recall, and the assembly quality requirements are high. The separation of the operation guidance information from the real physical object in the traditional paper manual easily distracts the operator, increasing their cognitive burden. To address this issue, we integrate augmented reality (AR) and
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“Follower” to “Collaborator”: A robot proactive collaborative controller based on human multimodal information for 3D handling/assembly scenarios J. Manuf. Syst. (IF 12.1) Pub Date : 2024-02-23 Hubo Chu, Tie Zhang, Yanbiao Zou, Hanlei Sun
At present, human-robot collaboration systems usually focus on robot followership and only achieve one-way system feedback. To make robots proactively collaborate with humans in unstructured and unknown scenarios and achieve the transition from "Follower" to "Collaborator", a robot proactive collaborative controller based on human multimodal information is proposed, which integrates a motion planning
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Integrated operation planning and process adjustment for optimum cost with attention to manufacturing quality and waste J. Manuf. Syst. (IF 12.1) Pub Date : 2024-02-18 Yue Wang, Jie Liu, Lirong Zhou, Liang Cong, John W. Sutherland
Operation planning and process adjustment are two important aspects of manufacturing operations that can have a significant impact on the performance of a manufacturing system. A framework is proposed for integrating these two planning activities to improve product quality as well as cost and waste minimization. Traditional models for quality, cost, and waste management have two shortcomings: i) they
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Ensuring additive manufacturing quality and cyber–physical security via side-channel measurements and transmissions J. Manuf. Syst. (IF 12.1) Pub Date : 2024-02-18 Nathan Raeker-Jordan, Jihoon Chung, Zhenyu (James) Kong, Christopher Williams
In order to securely monitor and validate additive manufacturing (AM) processes,the authors present a novel data transmission approach using a disconnected side-channel monitoring system. This approach is centered on the concept of embedding process-quality information into the print toolpath such that it may be detected by an in-situ side-channel monitoring system. This system can then parse these
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Pipeline condition monitoring towards digital twin system: A case study J. Manuf. Syst. (IF 12.1) Pub Date : 2024-02-16 Teng Wang, Ke Feng, Jiatong Ling, Min Liao, Chunsheng Yang, Robert Neubeck, Zheng Liu
Condition monitoring is essential for the industrial pipelines in manufacturing to ensure the consistent delivery of high quality products with efficient cost. Traditional pipeline conditional monitoring is driven by the entity in its physical space, with little connection to its virtual space. With the development of the digital twin, it is possible to implement the seamless convergence of physical
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A novel exponential model for tool remaining useful life prediction J. Manuf. Syst. (IF 12.1) Pub Date : 2024-02-15 Mingjian Sun, Kai Guo, Desheng Zhang, Bin Yang, Jie Sun, Duo Li, Tao Huang
Implementing proactive maintenance strategies based on condition prediction for cutting tools can reduce expensive, unscheduled maintenance events. This work proposes an novel exponential model to predict the Remaining Useful Life (RUL) of cutting tools. Firstly, a new monitoring indicator named second-order derivative of health index (SDHI) is constructed, and on this basis, a 3 interval-based first
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Manufacturing crisis and twin-oriented manufacturing J. Manuf. Syst. (IF 12.1) Pub Date : 2024-02-13 Wenlei Xiao, Tianze Qiu, Qiang Liu, Gang Zhao, Hongwen Xing, Rupeng Li
In the last decade, new concepts related to intelligent manufacturing have been proposed frequently, such as 3D printing, big data, machine learning, cloud manufacturing, cyber–physical system, digital twin, etc. Some concepts have gradually convergent to reality (such as 3D printing, machine learning), while others are still in dispute (such as digital twin). The future vision of intelligent manufacturing
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An online inference method for condition identification of workpieces with complex residual stress distributions J. Manuf. Syst. (IF 12.1) Pub Date : 2024-02-10 Dehua Li, Yingguang Li, Changqing Liu, Xu Liu, Lihui Wang
The residual stress field of structural components significantly influences their comprehensive performance and service life. Due to the lack of effective representation means and inference methods, existing methods are confined to inspecting local residual stress rather than the entire residual stress field, rendering the inference of complex residual stress fields quite difficult. In response to
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Joint scheduling optimisation method for the machining and heat-treatment of hydraulic cylinders based on improved multi-objective migrating birds optimisation J. Manuf. Syst. (IF 12.1) Pub Date : 2024-02-08 Xixing Li, Qingqing Zhao, Hongtao Tang, Siqin Yang, Deming Lei, XiVincent Wang
For the hydraulic cylinder parts manufacturing shop scheduling problem (HCPMS), which integrates a parallel batch processor hybrid flow shop scheduling problem with the flexible job shop scheduling problem, this paper establishes a multi-objective scheduling model with makespan, total energy consumption, and total machine workload as the optimisation objectives, and proposes an improved multi-objective
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AEKD: Unsupervised auto-encoder knowledge distillation for industrial anomaly detection J. Manuf. Syst. (IF 12.1) Pub Date : 2024-02-08 Qiangwei Wu, Hui Li, Chenyu Tian, Long Wen, Xinyu Li
Unsupervised Anomaly Detection (UAD) has achieved promising results in industrial Surface Defect Detection. Knowledge-Distillation (KD) based UAD became a hotspot due to its simple structure and convincing detection results. However, the generalization issue of the similarity between Student (S) and Teacher (T) models in KD hinders the accuracy. KD based UAD is based on the feature differences between
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Machine-fixture-pallet resources constrained flexible job shop scheduling considering loading and unloading times under pallet automation system J. Manuf. Syst. (IF 12.1) Pub Date : 2024-02-06 Yulu Zhou, Shichang Du, Molin Liu, Xiaoxiao Shen
Pallet automation system (PAS) has gained more and more attention from many manufacturing enterprises with the development of flexible automation, where machine, fixture, and pallet are three critical resources. Few scholars study flexible job shop scheduling considering fixture and pallet resources. Meanwhile, they ignore the loading and unloading times, potentially leading to an extended makespan
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On the maintenance of processing stability and consistency in laser-directed energy deposition via machine learning J. Manuf. Syst. (IF 12.1) Pub Date : 2024-02-03 Mengjie Wang, Nikolai Kashaev
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Solving multi-objective hybrid flowshop lot-streaming scheduling with consistent and limited sub-lots via a knowledge-based memetic algorithm J. Manuf. Syst. (IF 12.1) Pub Date : 2024-01-31 Yingying Zhu, Qiuhua Tang, Lixin Cheng, Lianpeng Zhao, Gan Jiang, Yiling Lu
All workpieces of a job are usually treated as a whole in general hybrid flowshop scheduling problem, resulting in lower production efficiency and on-time delivery rate. If a job lot can be split into smaller sub-lots and machines in each stage can process them in parallel, these performance indicators can be easily improved. Hence, this work addresses the hybrid flowshop lot-streaming scheduling problem
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Robust unsupervised-learning based crack detection for stamped metal products J. Manuf. Syst. (IF 12.1) Pub Date : 2024-01-30 Penghua Zhang, Hojun Ryu, Yinan Miao, Seungpyo Jo, Gyuhae Park
Crack detection plays an important role in the industrial inspection of stamped metal products. While supervised learning methods are commonly used in the quality assessment process, they often require a substantial amount of labeled data, which can be challenging to obtain in a well-tuned production line. Unsupervised learning has demonstrated exceptional performance in anomaly detection. This study
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In-process and post-process strategies for part quality assessment in metal powder bed fusion: A review J. Manuf. Syst. (IF 12.1) Pub Date : 2024-01-30 Cherq Chua, Yanting Liu, Richard J. Williams, Chee Kai Chua, Swee Leong Sing
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An ontology-based, general-purpose and Industry 4.0-ready architecture for supporting the smart operator (Part II – Virtual Reality case) J. Manuf. Syst. (IF 12.1) Pub Date : 2024-01-23 Antonio Cimino, Francesco Longo, Giovanni Mirabelli, Vittorio Solina, Saverino Verteramo
The ongoing transformation of the manufacturing sector towards Industry 4.0 is driven by the increasing importance of new technologies, such as Virtual Reality (VR) and Digital Twins (DTs). VR enables immersive and interactive experiences, while DTs facilitate real-time monitoring and control of manufacturing systems. However, their widespread adoption faces barriers including the lack of ready-to-use
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A Graph-based framework for assembly sequence planning of a cable harness J. Manuf. Syst. (IF 12.1) Pub Date : 2024-01-22 Hang Zhou, Qi Lu, Jinwu Qian
This paper focuses on assembly sequence planning (ASP) for cable harness, which is an essential but not yet well-addressed problem for automated cable assembly. A systematic approach is proposed to obtain all feasible assembly sequences taking account of the effects of the topological structure as well as various types of assembly tasks. The cable representation is first extended to consider the assembled
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Data-model linkage prediction of tool remaining useful life based on deep feature fusion and Wiener process J. Manuf. Syst. (IF 12.1) Pub Date : 2024-01-20 Xuebing Li, Xianli Liu, Caixu Yue, Lihui Wang, Steven Y. Liang
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Dynamic job-shop scheduling using graph reinforcement learning with auxiliary strategy J. Manuf. Syst. (IF 12.1) Pub Date : 2024-01-20 Zhenyu Liu, Haoyang Mao, Guodong Sa, Hui Liu, Jianrong Tan
The unpredictable variety of dynamic events in manufacturing systems poses a great challenge for tackling the job-shop scheduling problem (JSP), while most prior arts fail to strike a good balance between solution efficiency and dynamic adaptation. To this end, this paper outlines a graph reinforcement learning framework for solving dynamic JSP (DJSP) with stochastic processing time and machine breakdowns
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Modeling and Scheduling a Constrained Flowshop in Distributed Manufacturing Environments J. Manuf. Syst. (IF 12.1) Pub Date : 2024-01-09 Bing-Tao Wang, Quan-Ke Pan, Liang Gao, Zhong-Hua Miao, Hong-Yan Sang
This paper addresses a constrained distributed flowshop scheduling problem (CDFSP) that widely exists in modern manufacturing industry but has not been investigated before. Different from the distributed flowshop scheduling problem (DFSP) in the literature, CDFSP considers that all jobs are grouped for processing efficiently, and part of the groups subject to production requirements have a common deadline
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Real-time action localization of manual assembly operations using deep learning and augmented inference state machines J. Manuf. Syst. (IF 12.1) Pub Date : 2024-01-08 Vignesh Selvaraj, Md Al-Amin, Xuyong Yu, Wenjin Tao, Sangkee Min
The real-time monitoring of assembly operations in manufacturing industries can be used for manufacturing process optimization, which is crucial to manufacturers. It helps to improve productivity by automatically identifying the bottleneck and enhancing product quality by detecting errors and providing feedback to rectify them in real-time. However, developing a robust and reliable assembly monitoring
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A new description model for enabling more general manufacturing systems representation in digital twin J. Manuf. Syst. (IF 12.1) Pub Date : 2024-01-05 Jiaxiang Xie, Haifan Jiang, Shengfeng Qin, Jian Zhang, Guofu Ding
The demand for high efficiency and flexible production has led to more complex and flexible production organizations in discrete manufacturing systems. How to rapidly construct/reconfigure a workshop digital twin in response to a configurable complex physical production organization becomes a challenging problem. To address this challenge, based on the existing SevenElement description model, we propose
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Automated guided vehicle dispatching and routing integration via digital twin with deep reinforcement learning J. Manuf. Syst. (IF 12.1) Pub Date : 2024-01-06 Lixiang Zhang, Chen Yang, Yan Yan, Ze Cai, Yaoguang Hu
The manufacturing industry has witnessed a significant shift towards high flexibility and adaptability, driven by personalized demands. However, automated guided vehicle (AGV) dispatching optimization is still challenging when considering AGV routing with the spatial-temporal and kinematics constraints in intelligent production logistics systems, limiting the evolving industry applications. Against