Pub Date : 2026-01-24DOI: 10.1016/j.eng.2025.11.034
Xiaoliang Yan, Zhichao Wang, Shreyes N. Melkote, David W. Rosen
Cyber manufacturing services, which aim to connect geographically distributed designers and manufacturing service providers via the internet, are emerging to address the market shift from mass production to mass personalization. Recent advances in the Internet of Things (IoT) and machine learning enable new capabilities that promise improved efficiencies across the cyber manufacturing ecosystem. In this paper, we focus on machine learning methods that facilitate cyber manufacturing services in the areas of manufacturing process planning and design for manufacturing (DFM). To enable automated manufacturing process planning, we review recent advances in manufacturing capability modeling, manufacturing process selection, and feature recognition for process planning. To facilitate DFM, data-driven tools for generative design are reviewed and new methods and results presented. In the context of the literature review, we summarize work from our research group and present some new methods and results in the DFM area. Critical summaries of research challenges are provided to set the stage for recommendations on future research directions toward realizing cyber manufacturing services.
{"title":"Machine Learning-Based Cyber Manufacturing Services: A Review of Manufacturing Process Selection, Process Planning, and Design for Manufacturing","authors":"Xiaoliang Yan, Zhichao Wang, Shreyes N. Melkote, David W. Rosen","doi":"10.1016/j.eng.2025.11.034","DOIUrl":"https://doi.org/10.1016/j.eng.2025.11.034","url":null,"abstract":"Cyber manufacturing services, which aim to connect geographically distributed designers and manufacturing service providers via the internet, are emerging to address the market shift from mass production to mass personalization. Recent advances in the Internet of Things (IoT) and machine learning enable new capabilities that promise improved efficiencies across the cyber manufacturing ecosystem. In this paper, we focus on machine learning methods that facilitate cyber manufacturing services in the areas of manufacturing process planning and design for manufacturing (DFM). To enable automated manufacturing process planning, we review recent advances in manufacturing capability modeling, manufacturing process selection, and feature recognition for process planning. To facilitate DFM, data-driven tools for generative design are reviewed and new methods and results presented. In the context of the literature review, we summarize work from our research group and present some new methods and results in the DFM area. Critical summaries of research challenges are provided to set the stage for recommendations on future research directions toward realizing cyber manufacturing services.","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"395 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146042692","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 : 2026-01-23DOI: 10.1016/j.eng.2025.11.033
Shuo Wang, Lin Zhou, Shiyu Zhong, Gan Li, Lei Zhang, Xu Wang, Zhiqiang Li, Jian Lu
{"title":"Recent Advances in Metal Additive Manufacturing: Materials Design and Artificial Intelligence Applications","authors":"Shuo Wang, Lin Zhou, Shiyu Zhong, Gan Li, Lei Zhang, Xu Wang, Zhiqiang Li, Jian Lu","doi":"10.1016/j.eng.2025.11.033","DOIUrl":"https://doi.org/10.1016/j.eng.2025.11.033","url":null,"abstract":"","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"86 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033469","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 : 2026-01-22DOI: 10.1016/j.eng.2026.01.013
Zuo-Jun Max Shen, Shaochong Lin
This study introduces a novel conceptual framework to understand the transformative impact of Artificial Intelligence (AI) on global supply chains. We propose a Three-Chain Four-Intelligence framework that systematically analyzes how AI reconfigures supply chain architecture and capabilities through enhanced contextual awareness. The Three-Chain perspective examines how AI transforms the logistics chain (physical flow), information chain (data flow), and value chain (value creation) from fragmented operations to synchronized intelligent ecosystems. The Four-Intelligence pathway maps the evolutionary progression from digital connectivity to operational optimization, collaborative ecosystems, and ultimately self-evolving intelligent systems. AI serves as an orchestrating force that processes rich contextual information spanning product attributes, market dynamics, environmental conditions, and operational realities. We demonstrated the practical application of the framework through a comprehensive case study of JD.com, where AI implementation across all dimensions yielded quantifiable improvements. Our analysis reveals that the most transformative supply chain advancements emerge at the intersection of multiple chains with increasingly sophisticated contextual awareness. The paper concludes by identifying six emerging research frontiers, such as generative AI integration with decision optimization.
{"title":"AI Empowers Supply Chain Intelligence: A Three-Chain Four-Intelligence Framework","authors":"Zuo-Jun Max Shen, Shaochong Lin","doi":"10.1016/j.eng.2026.01.013","DOIUrl":"https://doi.org/10.1016/j.eng.2026.01.013","url":null,"abstract":"This study introduces a novel conceptual framework to understand the transformative impact of Artificial Intelligence (AI) on global supply chains. We propose a Three-Chain Four-Intelligence framework that systematically analyzes how AI reconfigures supply chain architecture and capabilities through enhanced contextual awareness. The Three-Chain perspective examines how AI transforms the logistics chain (physical flow), information chain (data flow), and value chain (value creation) from fragmented operations to synchronized intelligent ecosystems. The Four-Intelligence pathway maps the evolutionary progression from digital connectivity to operational optimization, collaborative ecosystems, and ultimately self-evolving intelligent systems. AI serves as an orchestrating force that processes rich contextual information spanning product attributes, market dynamics, environmental conditions, and operational realities. We demonstrated the practical application of the framework through a comprehensive case study of JD.com, where AI implementation across all dimensions yielded quantifiable improvements. Our analysis reveals that the most transformative supply chain advancements emerge at the intersection of multiple chains with increasingly sophisticated contextual awareness. The paper concludes by identifying six emerging research frontiers, such as generative AI integration with decision optimization.","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"71 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146022047","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 : 2026-01-22DOI: 10.1016/j.eng.2026.01.012
Toshiro Fujimori
{"title":"Ammonia-Fueled Power Generation for Energy Transition","authors":"Toshiro Fujimori","doi":"10.1016/j.eng.2026.01.012","DOIUrl":"https://doi.org/10.1016/j.eng.2026.01.012","url":null,"abstract":"","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"38 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033476","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 : 2026-01-21DOI: 10.1016/j.eng.2026.01.011
Qiongni Lin, Qixing Nie, Chunhua Chen, Yonggan Sun, Shanshan Zhang, Jianqiao Zou, Shaoping Nie
{"title":"Deciphering the Effects of Food Colorants on Host Health: Gut Microbiota Dependent and Independent Pathways","authors":"Qiongni Lin, Qixing Nie, Chunhua Chen, Yonggan Sun, Shanshan Zhang, Jianqiao Zou, Shaoping Nie","doi":"10.1016/j.eng.2026.01.011","DOIUrl":"https://doi.org/10.1016/j.eng.2026.01.011","url":null,"abstract":"","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"14 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146015012","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 : 2026-01-21DOI: 10.1016/j.eng.2026.01.008
Tao Jiang, Miaoran Peng, Yu Zhang, Xin Zhu, Shuqi Tang
{"title":"[0,1] Modulated Backscatter with Lower-Power Integration of Sensing and Communication for I-IoE in 6G","authors":"Tao Jiang, Miaoran Peng, Yu Zhang, Xin Zhu, Shuqi Tang","doi":"10.1016/j.eng.2026.01.008","DOIUrl":"https://doi.org/10.1016/j.eng.2026.01.008","url":null,"abstract":"","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"31 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146014978","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 : 2026-01-20DOI: 10.1016/j.eng.2026.01.006
Haozhe Bai, Kun Xu, Zhen Gao
A systematic review that summarizing the state-of-the-art development and identifying future challenges of FWT technologies from a holistic system-level and full-industry- chain perspective is important for both Chinese market and global stakeholders. This paper reviews the development trajectory and future challenges of FWT technologies in China, contrasting them with international advancements. While Europe pioneered FWT industrialization through projects like Hywind and WindFloat, China has rapidly progressed in prototype development, deploying five representative FWTs—Yin Ling, Fu Yao, Guan Lan, Gong Xiang, and OceanX—to address unique challenges in shallow continental shelf waters. Key technical hurdles—such as stringent draft limitations, nonlinear mooring dynamics in shallow waters, and the need for typhoon resilience—significantly increase the overall cost of FWT projects. Future priorities involve scaling turbines to 20–25 MW for cost reduction, optimizing lightweight, and developing economical taut mooring systems. Advanced coupled analysis tools, hybrid experimental methods, and farm level layout optimization are critical to address multi- body dynamics and wake effects. Integration with aquaculture, wave energy, and hydrogen production offers synergistic opportunities. Monitoring and maintenance are also critical for FWT lifecycle reliability and cost-effectiveness. Despite China’s leadership in pre-commercial FWT projects, commercialization demands interdisciplinary collaboration, standardized design codes, and lifecycle cost reduction strategies. This study underscores the necessity of government-industry-academia partnerships to accelerate FWT industrialization and align with global Net Zero goals.
{"title":"Development and Future Challenges of Offshore Floating Wind Turbine Technologies in China","authors":"Haozhe Bai, Kun Xu, Zhen Gao","doi":"10.1016/j.eng.2026.01.006","DOIUrl":"https://doi.org/10.1016/j.eng.2026.01.006","url":null,"abstract":"A systematic review that summarizing the state-of-the-art development and identifying future challenges of FWT technologies from a holistic system-level and full-industry- chain perspective is important for both Chinese market and global stakeholders.<!-- --> <!-- -->This paper reviews the development trajectory and future challenges of<!-- --> <!-- -->FWT technologies in China, contrasting them with international advancements. While<!-- --> <!-- -->Europe pioneered FWT industrialization through projects like Hywind and WindFloat,<!-- --> <!-- -->China has rapidly progressed in prototype development, deploying five representative<!-- --> <!-- -->FWTs—Yin Ling, Fu Yao, Guan Lan, Gong Xiang, and OceanX—to address unique<!-- --> <!-- -->challenges in shallow continental shelf waters. Key technical hurdles—such as stringent draft limitations, nonlinear mooring dynamics in shallow waters, and the need for typhoon resilience—significantly increase the overall cost of FWT projects.<!-- --> <!-- -->Future priorities involve scaling turbines to 20–25 MW for<!-- --> <!-- -->cost reduction, optimizing lightweight, and developing economical taut mooring<!-- --> <!-- -->systems. Advanced coupled analysis tools, hybrid experimental methods, and farm level<!-- --> <!-- -->layout optimization are critical to address multi- body dynamics and wake effects.<!-- --> <!-- -->Integration with aquaculture, wave energy, and hydrogen production offers synergistic<!-- --> <!-- -->opportunities. Monitoring and maintenance are also critical for FWT lifecycle reliability and cost-effectiveness.<!-- --> <!-- -->Despite China’s leadership in pre-commercial FWT projects,<!-- --> <!-- -->commercialization demands interdisciplinary collaboration, standardized design codes,<!-- --> <!-- -->and lifecycle cost reduction strategies. This study underscores the necessity of<!-- --> <!-- -->government-industry-academia partnerships to accelerate FWT industrialization and<!-- --> <!-- -->align with global Net Zero goals.","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"43 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146022155","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 : 2026-01-20DOI: 10.1016/j.eng.2026.01.009
Luis M. Camarinha-Matos
Over the past decades, the rise of a networked society has been driven by rapid advancements in information and communication technology, particularly in computer networking. This has enabled unprecedented hyper-connectivity among organizations, individuals, smart machines, and intelligent systems. As a result, new forms of collaboration have emerged, composed of distributed, autonomous, and heterogeneous entities. This evolution led to the establishment of Collaborative Networks (CNs) as a distinct discipline with a socio-technical character. Nowadays CNs play a key role in the ongoing digital transformation across industries and services. Although still a relatively young field, CNs have evolved through several generations over the past decades. As we move toward Industry 5.0, the complexity of interactions among a diverse range of agents continues to intensify. This article provides a brief overview of these trends, highlighting the role of CNs in the materialization of the goals of Industry 4.0 and Industry 5.0.
{"title":"Collaborative Networks in Industry 4.0 and Industry 5.0","authors":"Luis M. Camarinha-Matos","doi":"10.1016/j.eng.2026.01.009","DOIUrl":"https://doi.org/10.1016/j.eng.2026.01.009","url":null,"abstract":"Over the past decades, the rise of a networked society has been driven by rapid advancements in information and communication technology, particularly in computer networking. This has enabled unprecedented hyper-connectivity among organizations, individuals, smart machines, and intelligent systems. As a result, new forms of collaboration have emerged, composed of distributed, autonomous, and heterogeneous entities. This evolution led to the establishment of Collaborative Networks (CNs) as a distinct discipline with a socio-technical character. Nowadays CNs play a key role in the ongoing digital transformation across industries and services. Although still a relatively young field, CNs have evolved through several generations over the past decades. As we move toward Industry 5.0, the complexity of interactions among a diverse range of agents continues to intensify. This article provides a brief overview of these trends, highlighting the role of CNs in the materialization of the goals of Industry 4.0 and Industry 5.0.","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"101 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146022048","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 : 2026-01-20DOI: 10.1016/j.eng.2025.12.035
Fang Zhou, Yangang Wang, Man Wang, Si Xiong, Qiao Zhou
The aerodynamic characteristics of distributed electric propulsion (DEP) aircraft change significantly in the near-ground hovering state. In this paper, we employed a combination of experimental and numerical methods to investigate aerodynamic characteristics induced by ground effect for both isolated and distributed ducted fan configurations. The steady-state results indicate that the ground effect significantly influences the outlet pressure of the ducted fan. As the distance to the ground decreases, the thrust contribution from the duct decreases, while the thrust from rotor and stator blades increases, resulting in an overall increase in total thrust. At the distance to the ground of 0.8D (D is the ducted fan diameter), the total thrust increases by 3.2% for the isolated ducted fan and by 1.9% for the distributed ducted fans. Steady-state flow analyses further show that the ground effect leads to a decrease in the outlet velocity and a reduction in the effective flow area, thereby lowering the duct thrust. Concurrently, the increased blade angle of attack raises the aerodynamic loads on the rotor and stator blades, enhancing the blade thrust. Transient analyses reveal that for the isolated ducted fan, ground effect induces a tip vortex driven by tip leakage and a ground vortex triggered by shear layer instability, resulting in transient aerodynamic force fluctuations with a standard deviation of 9.1% of the time-averaged thrust. For the distributed ducted fans, aerodynamic interference between fans causes vortex superposition, forming a highly complex three-dimensional vortex structure and leading to a standard deviation in transient force fluctuation as high as 59.9% of the time-averaged thrust.
{"title":"Experimental and Numerical Study on Aerodynamic Characteristics of Distributed Ducted Fans in Ground Effect","authors":"Fang Zhou, Yangang Wang, Man Wang, Si Xiong, Qiao Zhou","doi":"10.1016/j.eng.2025.12.035","DOIUrl":"https://doi.org/10.1016/j.eng.2025.12.035","url":null,"abstract":"The aerodynamic characteristics of distributed electric propulsion (DEP) aircraft change significantly in the near-ground hovering state. In this paper, we employed a combination of experimental and numerical methods to investigate aerodynamic characteristics induced by ground effect for both isolated and distributed ducted fan configurations. The steady-state results indicate that the ground effect significantly influences the outlet pressure of the ducted fan. As the distance to the ground decreases, the thrust contribution from the duct decreases, while the thrust from rotor and stator blades increases, resulting in an overall increase in total thrust. At the distance to the ground of 0.8<em>D</em> (<em>D</em> is the ducted fan diameter), the total thrust increases by 3.2% for the isolated ducted fan and by 1.9% for the distributed ducted fans. Steady-state flow analyses further show that the ground effect leads to a decrease in the outlet velocity and a reduction in the effective flow area, thereby lowering the duct thrust. Concurrently, the increased blade angle of attack raises the aerodynamic loads on the rotor and stator blades, enhancing the blade thrust. Transient analyses reveal that for the isolated ducted fan, ground effect induces a tip vortex driven by tip leakage and a ground vortex triggered by shear layer instability, resulting in transient aerodynamic force fluctuations with a standard deviation of 9.1% of the time-averaged thrust. For the distributed ducted fans, aerodynamic interference between fans causes vortex superposition, forming a highly complex three-dimensional vortex structure and leading to a standard deviation in transient force fluctuation as high as 59.9% of the time-averaged thrust.","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"22 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146021830","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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