Pub Date : 2024-10-15DOI: 10.1016/j.jmsy.2024.10.008
L. Hu , Y.B. Guo , I. Seskar , Y. Chen , N. Mandayam , W. “Grace” Guo , J. Yi
This paper proposes a new paradigm of extreme manufacturing from the temporal perspective in contrast to the current extreme manufacturing paradigm based on length scales (e.g., from nanometer to close-to-atom). The advent of 5 G and future 6 G (NextG) wireless communication provides unique capabilities of ultra-low end-to-end (E2E) latency (∼1 ms), high speed (up to 20 Gb/s), high reliability (>99.999 %), and high flexibility (wireless) to meet the stringent requirements of future manufacturing. The ultra-low E2E latency enables NextG Manufacturing - a new extreme manufacturing paradigm from the latency perspective. This positioning paper identifies the needs of NextG manufacturing, introduces the characteristics of NextG wireless communication networks, proposes a framework for NextG manufacturing, demonstrates use cases, summarizes current challenges, and provides an outlook for future research directions.
{"title":"NextG manufacturing − New extreme manufacturing paradigm from the temporal perspective","authors":"L. Hu , Y.B. Guo , I. Seskar , Y. Chen , N. Mandayam , W. “Grace” Guo , J. Yi","doi":"10.1016/j.jmsy.2024.10.008","DOIUrl":"10.1016/j.jmsy.2024.10.008","url":null,"abstract":"<div><div>This paper proposes a new paradigm of extreme manufacturing from the temporal perspective in contrast to the current extreme manufacturing paradigm based on length scales (e.g., from nanometer to close-to-atom). The advent of 5 G and future 6 G (NextG) wireless communication provides unique capabilities of ultra-low end-to-end (E2E) latency (∼1 ms), high speed (up to 20 Gb/s), high reliability (>99.999 %), and high flexibility (wireless) to meet the stringent requirements of future manufacturing. The ultra-low E2E latency enables NextG Manufacturing - a new extreme manufacturing paradigm from the latency perspective. This positioning paper identifies the needs of NextG manufacturing, introduces the characteristics of NextG wireless communication networks, proposes a framework for NextG manufacturing, demonstrates use cases, summarizes current challenges, and provides an outlook for future research directions.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"77 ","pages":"Pages 418-431"},"PeriodicalIF":12.2,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-13DOI: 10.1016/j.jmsy.2024.09.024
Joelle W.Y. Chia, Wim J.C. Verhagen, Jose M. Silva, Ivan S. Cole
The Airframe Digital Twin (ADT) framework was conceived over a decade ago as a revolutionary way to realise condition-based maintenance within the defence aviation field. Since then, this concept has witnessed significant progress not only in terms of its scope and areas of application, but also in the fidelity of the virtual models used to represent physical systems. This paper sheds light on the progress and evolution of the ADT framework and methodologies since 2011 through a systematic literature review. Based on this review, it is understood that the progress in ADT places the aerospace industry on a path towards achieving Structural Prognostics and Health Management (SPHM), nevertheless more work needs to be done. This paper proceeds on evaluating the remaining challenges in the development of the ADT for SPHM, particularly in the context of fatigue and corrosion as the main forms of structural degradation. Modelling of the environmental and operational conditions, multiphysics, and multiscale interactions are highlighted. A further review on the outlook for ADT in the civil aviation industry is presented through comparisons between current industrial regulations and the state-of-the-art in the scientific community, and focus areas for future works in developing the ADT for SPHM are identified.
{"title":"A review and outlook of airframe digital twins for structural prognostics and health management in the aviation industry","authors":"Joelle W.Y. Chia, Wim J.C. Verhagen, Jose M. Silva, Ivan S. Cole","doi":"10.1016/j.jmsy.2024.09.024","DOIUrl":"10.1016/j.jmsy.2024.09.024","url":null,"abstract":"<div><div>The Airframe Digital Twin (ADT) framework was conceived over a decade ago as a revolutionary way to realise condition-based maintenance within the defence aviation field. Since then, this concept has witnessed significant progress not only in terms of its scope and areas of application, but also in the fidelity of the virtual models used to represent physical systems. This paper sheds light on the progress and evolution of the ADT framework and methodologies since 2011 through a systematic literature review. Based on this review, it is understood that the progress in ADT places the aerospace industry on a path towards achieving Structural Prognostics and Health Management (SPHM), nevertheless more work needs to be done. This paper proceeds on evaluating the remaining challenges in the development of the ADT for SPHM, particularly in the context of fatigue and corrosion as the main forms of structural degradation. Modelling of the environmental and operational conditions, multiphysics, and multiscale interactions are highlighted. A further review on the outlook for ADT in the civil aviation industry is presented through comparisons between current industrial regulations and the state-of-the-art in the scientific community, and focus areas for future works in developing the ADT for SPHM are identified.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"77 ","pages":"Pages 398-417"},"PeriodicalIF":12.2,"publicationDate":"2024-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142433129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-11DOI: 10.1016/j.jmsy.2024.09.020
Péter Dobrovoczki , András Kovács , Hiroyuki Sakata , Daisuke Tsutsumi
During the (re-)design of manufacturing systems, geometrical limitations on the available floor space may seriously impact the applicable resource configurations, including the selection of machines, robots, as well as auxiliary equipment. In current practice, such cases are managed by arduous manual iterations over the selection of resources and their geometrical arrangement. To overcome this inefficiency of existing approaches, the paper introduces a generic, integrated configuration-and-layout problem where the configuration sub-problem can encode arbitrary application-specific constraints on the selection of items (e.g., CNC machines and robots), while the layout sub-problem ensures geometrical feasibility, via a 2D rectangle packing representation. The generic model is demonstrated on an industrial application that involves the design of a flexible manufacturing system: items corresponding to CNC machines and robots must be selected, assigned to multiple manufacturing cells, and placed in the workshop blueprint to ensure that a given mix of products can be manufactured in the desired volume. For solving the generic configuration-and-layout problem, a logic-based Benders decomposition method is proposed. The efficiency of the approach is ensured by adding lifted cuts, symmetry breaking, and redundant constraints inspired by 2D bin packing lower bounds to the core Benders framework. Thorough computational evaluation is performed on a large set of problem instances, whereas practical applicability is verified in a real industrial case study.
{"title":"Integrated system configuration and layout planning for flexible manufacturing systems","authors":"Péter Dobrovoczki , András Kovács , Hiroyuki Sakata , Daisuke Tsutsumi","doi":"10.1016/j.jmsy.2024.09.020","DOIUrl":"10.1016/j.jmsy.2024.09.020","url":null,"abstract":"<div><div>During the (re-)design of manufacturing systems, geometrical limitations on the available floor space may seriously impact the applicable resource configurations, including the selection of machines, robots, as well as auxiliary equipment. In current practice, such cases are managed by arduous manual iterations over the selection of resources and their geometrical arrangement. To overcome this inefficiency of existing approaches, the paper introduces a generic, integrated configuration-and-layout problem where the configuration sub-problem can encode arbitrary application-specific constraints on the selection of items (e.g., CNC machines and robots), while the layout sub-problem ensures geometrical feasibility, via a 2D rectangle packing representation. The generic model is demonstrated on an industrial application that involves the design of a flexible manufacturing system: items corresponding to CNC machines and robots must be selected, assigned to multiple manufacturing cells, and placed in the workshop blueprint to ensure that a given mix of products can be manufactured in the desired volume. For solving the generic configuration-and-layout problem, a logic-based Benders decomposition method is proposed. The efficiency of the approach is ensured by adding lifted cuts, symmetry breaking, and redundant constraints inspired by 2D bin packing lower bounds to the core Benders framework. Thorough computational evaluation is performed on a large set of problem instances, whereas practical applicability is verified in a real industrial case study.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"77 ","pages":"Pages 384-397"},"PeriodicalIF":12.2,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142425172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-09DOI: 10.1016/j.jmsy.2024.10.001
Alessandro Ruberti , Adalberto Polenghi , Marco Macchi
The increased requests for value-added services to integrate product performance push manufacturing companies to extend their service offerings to meet customers’ needs. In this context, maintenance planning can leverage new possibilities offered by digital technologies for data analytics services. The present research then proposes an approach for maintenance plan adaptation based on a data-driven method applied over a fleet of machines installed in different production sites. The method relies on collaborative prognostics to develop a clustering of machines’ behaviour aimed at providing the health ratings of the machines and the subsequent maintenance plan adaptation due to the deviation from the expected behaviour. The method is adopted from the perspective of an Original Equipment Manufacturer, as part of a transformation path towards an advanced provision of digitalization for maintenance service offerings. The method is validated in the context of two lines at selected customer’s premises. This demonstrates the viability and effectiveness of adapting the maintenance plans thanks to the data analytics in light of the current behaviour of the machines within the lines.
{"title":"Maintenance plan adaptation based on health ratings of servitised machines through a fleet-wide machine clustering method","authors":"Alessandro Ruberti , Adalberto Polenghi , Marco Macchi","doi":"10.1016/j.jmsy.2024.10.001","DOIUrl":"10.1016/j.jmsy.2024.10.001","url":null,"abstract":"<div><div>The increased requests for value-added services to integrate product performance push manufacturing companies to extend their service offerings to meet customers’ needs. In this context, maintenance planning can leverage new possibilities offered by digital technologies for data analytics services. The present research then proposes an approach for maintenance plan adaptation based on a data-driven method applied over a fleet of machines installed in different production sites. The method relies on collaborative prognostics to develop a clustering of machines’ behaviour aimed at providing the health ratings of the machines and the subsequent maintenance plan adaptation due to the deviation from the expected behaviour. The method is adopted from the perspective of an Original Equipment Manufacturer, as part of a transformation path towards an advanced provision of digitalization for maintenance service offerings. The method is validated in the context of two lines at selected customer’s premises. This demonstrates the viability and effectiveness of adapting the maintenance plans thanks to the data analytics in light of the current behaviour of the machines within the lines.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"77 ","pages":"Pages 368-383"},"PeriodicalIF":12.2,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142425171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-07DOI: 10.1016/j.jmsy.2024.09.011
Sihoon Moon , Sanghoon Lee , Kyung-Joon Park
In smart manufacturing systems (SMSs), flexible job-shop scheduling with transportation constraints (FJSPT) is essential to optimize solutions for maximizing productivity, considering production flexibility based on automated guided vehicles (AGVs). Recent developments in deep reinforcement learning (DRL)-based methods for FJSPT have encountered a scale generalization challenge. We propose the Heterogeneous Graph Scheduler (HGS), a novel DRL-based method that provides near-optimal solutions regardless of the scale of operations, machines, and vehicles. HGS modifies the disjunctive graph to model FJSPT as a heterogeneous graph of operations, machines, and vehicles, dynamically representing processes and transportation. It involves a structure-aware heterogeneous graph encoder to enhance scale generalization, using multi-head attention to aggregate messages locally and integrate them globally. A three-stage decoder for end-to-end decision-making outputs the scheduling solution by selecting nodes with the highest likelihood of minimizing makespan. Our evaluation with benchmark datasets shows HGS outperforms traditional dispatching rules, metaheuristics, and existing DRL-based methods, demonstrating superior makespan performance and scale generalization. Moreover, as the scale increases, HGS achieves the best solutions across all instances.
{"title":"Learning-enabled flexible job-shop scheduling for scalable smart manufacturing","authors":"Sihoon Moon , Sanghoon Lee , Kyung-Joon Park","doi":"10.1016/j.jmsy.2024.09.011","DOIUrl":"10.1016/j.jmsy.2024.09.011","url":null,"abstract":"<div><div>In smart manufacturing systems (SMSs), flexible job-shop scheduling with transportation constraints (FJSPT) is essential to optimize solutions for maximizing productivity, considering production flexibility based on automated guided vehicles (AGVs). Recent developments in deep reinforcement learning (DRL)-based methods for FJSPT have encountered a <em>scale generalization</em> challenge. We propose the Heterogeneous Graph Scheduler (HGS), a novel DRL-based method that provides near-optimal solutions regardless of the scale of operations, machines, and vehicles. HGS modifies the disjunctive graph to model FJSPT as a heterogeneous graph of operations, machines, and vehicles, dynamically representing processes and transportation. It involves a structure-aware heterogeneous graph encoder to enhance scale generalization, using multi-head attention to aggregate messages locally and integrate them globally. A three-stage decoder for end-to-end decision-making outputs the scheduling solution by selecting nodes with the highest likelihood of minimizing makespan. Our evaluation with benchmark datasets shows HGS outperforms traditional dispatching rules, metaheuristics, and existing DRL-based methods, demonstrating superior makespan performance and scale generalization. Moreover, as the scale increases, HGS achieves the best solutions across all instances.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"77 ","pages":"Pages 356-367"},"PeriodicalIF":12.2,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142425170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-30DOI: 10.1016/j.jmsy.2024.09.018
Jun-Hee Han , Sung-hoon Jeong , Gyusun Hwang , Ju-Yong Lee
The concept of cyber manufacturing has become a critical element in semiconductor fabrication environments, where automation and systemization are integral, for addressing the growing complexity of processes and facilitating predictive capabilities through data integration. This study deals with the dispatching problem to minimize makespan at a wet clean station in semiconductor fabrication using artificial intelligence-enabled manufacturing control techniques. The wet clean station is comprised of sequential chemical and rinsing baths for cleaning wafer lots and multiple robot arms for lot handling. In the station, wafer lots are sequentially immersed in several baths for cleaning to eliminate residual contaminants and stains that cause defects on wafer surfaces. The station can process various types of products, and the specific order of immersion differs depending on the product type. Unlike typical dispatching problems, the information required for dispatching, such as processing times and sequences inside the station, is not available. The only available data are historical logs that record when each lot enters and leaves the station. However, even when cleaning the same product type, the duration that lots spend in the station may vary based on the combination of product types being cleaned simultaneously and the settings of the station. Thus, using the time records, this study proposes a dispatching method based on machine learning models (multiple linear regression, deep neural network, and convolutional neural network). The proposed algorithms were evaluated and verified by comparing them with CPLEX solving a mixed integer programming and dispatching methods used in a semiconductor fab in Korea. Through this experiment, we observed that the proposed models can provide dispatching solutions that are practical and effective in a rapidly changing production setting. These models have the potential to enhance the capacity of a wet clean station and will contribute to artificial intelligence-based manufacturing system control.
{"title":"Machine learning-based dispatching for a wet clean station in semiconductor manufacturing","authors":"Jun-Hee Han , Sung-hoon Jeong , Gyusun Hwang , Ju-Yong Lee","doi":"10.1016/j.jmsy.2024.09.018","DOIUrl":"10.1016/j.jmsy.2024.09.018","url":null,"abstract":"<div><div>The concept of cyber manufacturing has become a critical element in semiconductor fabrication environments, where automation and systemization are integral, for addressing the growing complexity of processes and facilitating predictive capabilities through data integration. This study deals with the dispatching problem to minimize makespan at a wet clean station in semiconductor fabrication using artificial intelligence-enabled manufacturing control techniques. The wet clean station is comprised of sequential chemical and rinsing baths for cleaning wafer lots and multiple robot arms for lot handling. In the station, wafer lots are sequentially immersed in several baths for cleaning to eliminate residual contaminants and stains that cause defects on wafer surfaces. The station can process various types of products, and the specific order of immersion differs depending on the product type. Unlike typical dispatching problems, the information required for dispatching, such as processing times and sequences inside the station, is not available. The only available data are historical logs that record when each lot enters and leaves the station. However, even when cleaning the same product type, the duration that lots spend in the station may vary based on the combination of product types being cleaned simultaneously and the settings of the station. Thus, using the time records, this study proposes a dispatching method based on machine learning models (multiple linear regression, deep neural network, and convolutional neural network). The proposed algorithms were evaluated and verified by comparing them with CPLEX solving a mixed integer programming and dispatching methods used in a semiconductor fab in Korea. Through this experiment, we observed that the proposed models can provide dispatching solutions that are practical and effective in a rapidly changing production setting. These models have the potential to enhance the capacity of a wet clean station and will contribute to artificial intelligence-based manufacturing system control.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"77 ","pages":"Pages 341-355"},"PeriodicalIF":12.2,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142356700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-28DOI: 10.1016/j.jmsy.2024.09.012
Albert Abio , Francesc Bonada , Eduard Garcia-Llamas , Marc Grané , Nuria Nievas , Danillo Lange , Jaume Pujante , Oriol Pujol
The introduction of data-driven surrogate models is a powerful solution to obtain a representation of a manufacturing system, overcoming the limitations of finite element simulations regarding complexity and time. Usually, data acquisition in real manufacturing plants is a very expensive task, and finite element simulations are employed to train Machine Learning-based surrogate models. However, the approximations of the finite element models may induce a deviation from reality that is transferred to the surrogate models. This paper proposes a methodology to combine AI-based surrogate modeling and transfer learning to create a trustworthy and efficient surrogate model of a real manufacturing process, using a low-fidelity finite element model as a source. In particular, the methodology has been demonstrated in a study involving press hardening of boron steel sheet in a pilot plant. Two deep neural networks have been trained with low-fidelity ABAQUS simulations, forming a baseline surrogate model that predicts the key outputs of the process. The use of few experimental real data of the process to perform transfer learning and adapt the original baseline surrogate model to the real environment shows remarkable results, surpassing other Variable-Fidelity Modeling approaches. The final transfer learning surrogate model provides fast and good predictions of the most relevant outputs of the real process with little training, and it removes completely the calibration stage or the need of a high-fidelity simulation model. Additionally, the presented methodology can be a trigger for creating efficient virtual manufacturing environments that can enable developing digital twins or reinforcement learning agents for process optimization.
{"title":"A transfer learning method in press hardening surrogate modeling: From simulations to real-world","authors":"Albert Abio , Francesc Bonada , Eduard Garcia-Llamas , Marc Grané , Nuria Nievas , Danillo Lange , Jaume Pujante , Oriol Pujol","doi":"10.1016/j.jmsy.2024.09.012","DOIUrl":"10.1016/j.jmsy.2024.09.012","url":null,"abstract":"<div><div>The introduction of data-driven surrogate models is a powerful solution to obtain a representation of a manufacturing system, overcoming the limitations of finite element simulations regarding complexity and time. Usually, data acquisition in real manufacturing plants is a very expensive task, and finite element simulations are employed to train Machine Learning-based surrogate models. However, the approximations of the finite element models may induce a deviation from reality that is transferred to the surrogate models. This paper proposes a methodology to combine AI-based surrogate modeling and transfer learning to create a trustworthy and efficient surrogate model of a real manufacturing process, using a low-fidelity finite element model as a source. In particular, the methodology has been demonstrated in a study involving press hardening of boron steel sheet in a pilot plant. Two deep neural networks have been trained with low-fidelity ABAQUS simulations, forming a baseline surrogate model that predicts the key outputs of the process. The use of few experimental real data of the process to perform transfer learning and adapt the original baseline surrogate model to the real environment shows remarkable results, surpassing other Variable-Fidelity Modeling approaches. The final transfer learning surrogate model provides fast and good predictions of the most relevant outputs of the real process with little training, and it removes completely the calibration stage or the need of a high-fidelity simulation model. Additionally, the presented methodology can be a trigger for creating efficient virtual manufacturing environments that can enable developing digital twins or reinforcement learning agents for process optimization.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"77 ","pages":"Pages 320-340"},"PeriodicalIF":12.2,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142356764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-27DOI: 10.1016/j.jmsy.2024.09.014
Rui Qin , Zhifen Zhang , Jing Huang , Zhengyao Du , Xizhang Chen , Yu Su , Guangrui Wen , Weifeng He , Xuefeng Chen
Acoustic emission monitoring in laser shock peening facilitates real-time detection of potential quality issues arising from variations in industrial parameters, enabling iterative optimization of the manufacturing process through material behavior analysis. However, existing research still lacks a comprehensive understanding of the time-varying time-frequency characteristics in dynamic acoustic emission and efficient corresponding models. Therefore, this study proposes an innovative monitoring approach that integrates accelerable adaptive cepstrum (AAC) and L2-Dual Net. Specifically, AAC first employs variable frames and filters to map time-varying features in the signal, and then obtains representative frame length distributions and filter weights for different operating conditions based on statistical information. AAC not only unveils time-varying features in signals but also boasts an efficient computational process. L2-Dual Net is a novel quality assessment model with robust feature extraction and local spatial feature interactions. The incorporation of L2 norm equips the model with robust interference immunity, while the dual spatial attention mechanism helps the model to interact with spatial features exhibiting different time-frequencies. Variable process parameter experiments for aluminum alloy 7075 and titanium alloy TC4 were conducted to validate the reliability of the proposed method. Results demonstrate that AAC showcases optimal computational efficiency and higher feature resolution. When compared with state-of-the-art network architectures, L2-Dual Net exhibits superior information flow, along with higher recognition accuracy and robustness. Moreover, various variants of L2-Dual Net are explored and the code is accessible at https://github.com/Qinr1026/L2-Dual-Net. The proposed method holds promising potential for application in other areas of acoustic emission monitoring.
激光冲击强化中的声发射监测有助于实时检测工业参数变化引起的潜在质量问题，从而通过材料行为分析迭代优化制造过程。然而，现有研究仍缺乏对动态声发射时变时频特性的全面了解和有效的相应模型。因此，本研究提出了一种集成了加速自适应倒频谱（AAC）和 L2-Dual Net 的创新监测方法。具体来说，AAC 首先采用可变帧和滤波器来映射信号中的时变特征，然后根据统计信息获得不同运行条件下的代表性帧长分布和滤波器权重。AAC 不仅能揭示信号中的时变特征，还拥有高效的计算过程。L2-Dual Net 是一种新颖的质量评估模型，具有稳健的特征提取和局部空间特征交互功能。L2 准则的加入使该模型具有强大的抗干扰能力，而双重空间关注机制则有助于该模型与表现出不同时频的空间特征进行交互。对铝合金 7075 和钛合金 TC4 进行了可变工艺参数实验，以验证所提方法的可靠性。结果表明，AAC 具有最佳的计算效率和更高的特征分辨率。与最先进的网络架构相比，L2-Dual Net 具有更优越的信息流、更高的识别精度和鲁棒性。此外，还探讨了 L2-Dual Net 的各种变体，其代码可在 https://github.com/Qinr1026/L2-Dual-Net 上访问。所提出的方法有望应用于声学发射监测的其他领域。
{"title":"Accelerable adaptive cepstrum and L2-Dual Net for acoustic emission-based quality monitoring in laser shock peening","authors":"Rui Qin , Zhifen Zhang , Jing Huang , Zhengyao Du , Xizhang Chen , Yu Su , Guangrui Wen , Weifeng He , Xuefeng Chen","doi":"10.1016/j.jmsy.2024.09.014","DOIUrl":"10.1016/j.jmsy.2024.09.014","url":null,"abstract":"<div><div>Acoustic emission monitoring in laser shock peening facilitates real-time detection of potential quality issues arising from variations in industrial parameters, enabling iterative optimization of the manufacturing process through material behavior analysis. However, existing research still lacks a comprehensive understanding of the time-varying time-frequency characteristics in dynamic acoustic emission and efficient corresponding models. Therefore, this study proposes an innovative monitoring approach that integrates accelerable adaptive cepstrum (AAC) and L2-Dual Net. Specifically, AAC first employs variable frames and filters to map time-varying features in the signal, and then obtains representative frame length distributions and filter weights for different operating conditions based on statistical information. AAC not only unveils time-varying features in signals but also boasts an efficient computational process. L2-Dual Net is a novel quality assessment model with robust feature extraction and local spatial feature interactions. The incorporation of L2 norm equips the model with robust interference immunity, while the dual spatial attention mechanism helps the model to interact with spatial features exhibiting different time-frequencies. Variable process parameter experiments for aluminum alloy 7075 and titanium alloy TC4 were conducted to validate the reliability of the proposed method. Results demonstrate that AAC showcases optimal computational efficiency and higher feature resolution. When compared with state-of-the-art network architectures, L2-Dual Net exhibits superior information flow, along with higher recognition accuracy and robustness. Moreover, various variants of L2-Dual Net are explored and the code is accessible at <span><span>https://github.com/Qinr1026/L2-Dual-Net</span><svg><path></path></svg></span>. The proposed method holds promising potential for application in other areas of acoustic emission monitoring.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"77 ","pages":"Pages 301-319"},"PeriodicalIF":12.2,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142326654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-27DOI: 10.1016/j.jmsy.2024.09.015
Lele Bai , Jun Zhang , Erhan Budak , Yuyang Tang , Wanhua Zhao
The demand for intelligent process monitoring is increasing in aerospace manufacturing to ensure tight tolerances and high surface quality. Real-time monitoring in machining is crucial for machined accuracy and process reliability, reducing production times and costs, and enhancing automation of the manufacturing process. This study presents a robust multi-target condition monitoring method based on the vibration signals. Firstly, three new energy ratio indicators with dimensionless characteristics were defined for tool wear, breakage, and chatter monitoring. Secondly, the vibration energy loss from the tool tip to the tool holder, and spindle housing was measured and compared, and the rules of vibration loss from the tool tip to the spindle housing were revealed. Using force signals as a reference, the monitoring performance of industrially acceptable acceleration and sound signals in multi-target condition monitoring was quantitatively analyzed. Finally, the performance of the proposed vibration energy-based indicators was experimentally illustrated and quantitatively evaluated. It is shown that these indicators can be used to discriminate between tool breakage and chatter, as well as to assess tool wear. The new monitoring method can also minimize the costs of process monitoring by reducing the use of expensive sensors or overusing multiple sensors in a smart manufacturing system.
{"title":"Vibration energy-based indicators for multi-target condition monitoring in milling operations","authors":"Lele Bai , Jun Zhang , Erhan Budak , Yuyang Tang , Wanhua Zhao","doi":"10.1016/j.jmsy.2024.09.015","DOIUrl":"10.1016/j.jmsy.2024.09.015","url":null,"abstract":"<div><div>The demand for intelligent process monitoring is increasing in aerospace manufacturing to ensure tight tolerances and high surface quality. Real-time monitoring in machining is crucial for machined accuracy and process reliability, reducing production times and costs, and enhancing automation of the manufacturing process. This study presents a robust multi-target condition monitoring method based on the vibration signals. Firstly, three new energy ratio indicators with dimensionless characteristics were defined for tool wear, breakage, and chatter monitoring. Secondly, the vibration energy loss from the tool tip to the tool holder, and spindle housing was measured and compared, and the rules of vibration loss from the tool tip to the spindle housing were revealed. Using force signals as a reference, the monitoring performance of industrially acceptable acceleration and sound signals in multi-target condition monitoring was quantitatively analyzed. Finally, the performance of the proposed vibration energy-based indicators was experimentally illustrated and quantitatively evaluated. It is shown that these indicators can be used to discriminate between tool breakage and chatter, as well as to assess tool wear. The new monitoring method can also minimize the costs of process monitoring by reducing the use of expensive sensors or overusing multiple sensors in a smart manufacturing system.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"77 ","pages":"Pages 284-300"},"PeriodicalIF":12.2,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142326802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-26DOI: 10.1016/j.jmsy.2024.09.006
Xin Liu , Gongfa Li , Feng Xiang , Bo Tao , Guozhang Jiang
Human-robot collaboration demonstrates broad application prospects in product customization. Digital twin represents an advanced real-virtual interaction technology that plays an essential role in enhancing perception and interaction for human-robot collaboration. A digital twin-based human-robot collaboration system has been proposed to devise collaborative strategies, simulate collaborative processes, and ensure human safety. However, there exist research gaps in implementing human-robot collaboration digital twin systems. A significant challenge lies in constructing data models for describing data types and content in human-robot collaboration digital twin systems. Additionally, addressing data management aspects, including data sharing and storage, is crucial for the effective operation of human-robot collaboration digital twin systems. To bridge existing deficiencies, a novel approach is introduced for managing data in human-robot collaboration digital twin systems through a blockchain-based cloud-edge collaborative method. Initially, a conceptualization of the human-robot collaboration digital twin system alongside a cloud-edge data management framework is introduced. Subsequently, a data model is delineated to outline data categories and contents of human-robot collaboration digital twin systems. Following this, an exploration is conducted on methodologies for data sharing and storage utilizing blockchain and cloud technologies. Ultimately, the efficacy of the proposed approaches is validated through a case study.
{"title":"Blockchain-based cloud-edge collaborative data management for human-robot collaboration digital twin system","authors":"Xin Liu , Gongfa Li , Feng Xiang , Bo Tao , Guozhang Jiang","doi":"10.1016/j.jmsy.2024.09.006","DOIUrl":"10.1016/j.jmsy.2024.09.006","url":null,"abstract":"<div><div>Human-robot collaboration demonstrates broad application prospects in product customization. Digital twin represents an advanced real-virtual interaction technology that plays an essential role in enhancing perception and interaction for human-robot collaboration. A digital twin-based human-robot collaboration system has been proposed to devise collaborative strategies, simulate collaborative processes, and ensure human safety. However, there exist research gaps in implementing human-robot collaboration digital twin systems. A significant challenge lies in constructing data models for describing data types and content in human-robot collaboration digital twin systems. Additionally, addressing data management aspects, including data sharing and storage, is crucial for the effective operation of human-robot collaboration digital twin systems. To bridge existing deficiencies, a novel approach is introduced for managing data in human-robot collaboration digital twin systems through a blockchain-based cloud-edge collaborative method. Initially, a conceptualization of the human-robot collaboration digital twin system alongside a cloud-edge data management framework is introduced. Subsequently, a data model is delineated to outline data categories and contents of human-robot collaboration digital twin systems. Following this, an exploration is conducted on methodologies for data sharing and storage utilizing blockchain and cloud technologies. Ultimately, the efficacy of the proposed approaches is validated through a case study.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"77 ","pages":"Pages 228-245"},"PeriodicalIF":12.2,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142323916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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