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[Online] Intelligent manufacturing

Guest Editors-in-Chief 
Li, Peigen, Huazhong University of Science and Technology, China
Duan, Zhengcheng, Huazhong University of Science and Technology, China
 
Executive Associate Editors
Shao, Xinyu, Huazhong University of Science and Technology, China
Yang, Haicheng, China Aerospace Science and Technology Corporation, China
 
Members
Gao, Feng, Shanghai Jiao Tong University, China
Hong, Jun, Xi’an Jiaotong University, China
Huang, Chuanzhen, Shandong University, China
Huang, Minghui, Central South University, China
Jia, Zhenyuan, Dalian University of Technology, China
Jiao, Zongxia, Beihang University, China
Wang, Jianmin, Tsinghua University, China
Xu, Xipeng, Huaqiao University, China
Zhang, Shuyou, Zhejiang University, China
 
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Toward New-Generation Intelligent Manufacturing
Zhou Ji, Li Peigen, Zhou Yanhong, Wang Baicun, Zang Jiyuan, Meng Liu
Engineering    2018, 4 (1): 11-20.   https://doi.org/10.1016/j.eng.2018.01.002
Abstract   PDF (1814KB)

Intelligent manufacturing is a general concept that is under continuous development. It can be categorized into three basic paradigms: digital manufacturing, digital-networked manufacturing, and newgeneration intelligent manufacturing. New-generation intelligent manufacturing represents an indepth integration of new-generation artificial intelligence (AI) technology and advanced manufacturing technology. It runs through every link in the full life-cycle of design, production, product, and service. The concept also relates to the optimization and integration of corresponding systems; the continuous improvement of enterprises’ product quality, performance, and service levels; and reduction in resources consumption. New-generation intelligent manufacturing acts as the core driving force of the new industrial revolution and will continue to be the main pathway for the transformation and upgrading of the manufacturing industry in the decades to come. Human-cyber-physical systems (HCPSs) reveal the technological mechanisms of new-generation intelligent manufacturing and can effectively guide related theoretical research and engineering practice. Given the sequential development, cross interaction, and iterative upgrading characteristics of the three basic paradigms of intelligent manufacturing, a technology roadmap for ‘‘parallel promotion and integrated development” should be developed in order to drive forward the intelligent transformation of the manufacturing industry in China.

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Intelligent Manufacturing in the Context of Industry 4.0: A Review
Ray Y. Zhong, Xun Xu, Eberhard Klotz, Stephen T. Newman
Engineering    2017, 3 (5): 616-630.   https://doi.org/10.1016/J.ENG.2017.05.015
Abstract   HTML   PDF (1607KB)

Our next generation of industry—Industry 4.0—holds the promise of increased flexibility in manufacturing, along with mass customization, better quality, and improved productivity. It thus enables companies to cope with the challenges of producing increasingly individualized products with a short lead-time to market and higher quality. Intelligent manufacturing plays an important role in Industry 4.0. Typical resources are converted into intelligent objects so that they are able to sense, act, and behave within a smart environment. In order to fully understand intelligent manufacturing in the context of Industry 4.0, this paper provides a comprehensive review of associated topics such as intelligent manufacturing, Internet of Things (IoT)-enabled manufacturing, and cloud manufacturing. Similarities and differences in these topics are highlighted based on our analysis. We also review key technologies such as the IoT, cyber-physical systems (CPSs), cloud computing, big data analytics (BDA), and information and communications technology (ICT) that are used to enable intelligent manufacturing. Next, we describe worldwide movements in intelligent manufacturing, including governmental strategic plans from different countries and strategic plans from major international companies in the European Union, United States, Japan, and China. Finally, we present current challenges and future research directions. The concepts discussed in this paper will spark new ideas in the effort to realize the much-anticipated Fourth Industrial Revolution.

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Integrated and Intelligent Manufacturing: Perspectives and Enablers
Yubao Chen
Engineering    2017, 3 (5): 588-595.   https://doi.org/10.1016/J.ENG.2017.04.009
Abstract   HTML   PDF (1484KB)

With ever-increasing market competition and advances in technology, more and more countries are prioritizing advanced manufacturing technology as their top priority for economic growth. Germany announced the Industry 4.0 strategy in 2013. The US government launched the Advanced Manufacturing Partnership (AMP) in 2011 and the National Network for Manufacturing Innovation (NNMI) in 2014. Most recently, the Manufacturing USA initiative was officially rolled out to further “leverage existing resources… to nurture manufacturing innovation and accelerate commercialization” by fostering close collaboration between industry, academia, and government partners. In 2015, the Chinese government officially published a 10-year plan and roadmap toward manufacturing: Made in China 2025. In all these national initiatives, the core technology development and implementation is in the area of advanced manufacturing systems. A new manufacturing paradigm is emerging, which can be characterized by two unique features: integrated manufacturing and intelligent manufacturing. This trend is in line with the progress of industrial revolutions, in which higher efficiency in production systems is being continuously pursued. To this end, 10 major technologies can be identified for the new manufacturing paradigm. This paper describes the rationales and needs for integrated and intelligent manufacturing (i2M) systems. Related technologies from different fields are also described. In particular, key technological enablers, such as the Internet of Things and Services (IoTS), cyber-physical systems (CPSs), and cloud computing are discussed. Challenges are addressed with applications that are based on commercially available platforms such as General Electric (GE)’s Predix and PTC’s ThingWorx.

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Control for Intelligent Manufacturing: A Multiscale Challenge
Han-Xiong Li, Haitao Si
Engineering    2017, 3 (5): 608-615.   https://doi.org/10.1016/J.ENG.2017.05.016
Abstract   HTML   PDF (1761KB)

The Made in China 2025 initiative will require full automation in all sectors, from customers to production. This will result in great challenges to manufacturing systems in all sectors. In the future of manufacturing, all devices and systems should have sensing and basic intelligence capabilities for control and adaptation. In this study, after discussing multiscale dynamics of the modern manufacturing system, a five-layer functional structure is proposed for uncertainties processing. Multiscale dynamics include: multi-time scale, space-time scale, and multi-level dynamics. Control action will differ at different scales, with more design being required at both fast and slow time scales. More quantitative action is required in low-level operations, while more qualitative action is needed regarding high-level supervision. Intelligent manufacturing systems should have the capabilities of flexibility, adaptability, and intelligence. These capabilities will require the control action to be distributed and integrated with different approaches, including smart sensing, optimal design, and intelligent learning. Finally, a typical jet dispensing system is taken as a real-world example for multiscale modeling and control.

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A Research Review on the Key Technologies of Intelligent Design for Customized Products
Shuyou Zhang, Jinghua Xu, Huawei Gou, Jianrong Tan
Engineering    2017, 3 (5): 631-640.   https://doi.org/10.1016/J.ENG.2017.04.005
Abstract   HTML   PDF (4339KB)

The development of technologies such as big data and cyber-physical systems (CPSs) has increased the demand for product design. Product digital design involves completing the product design process using advanced digital technologies such as geometry modeling, kinematic and dynamic simulation, multi-disciplinary coupling, virtual assembly, virtual reality (VR), multi-objective optimization (MOO), and human-computer interaction. The key technologies of intelligent design for customized products include: a description and analysis of customer requirements (CRs), product family design (PFD) for the customer base, configuration and modular design for customized products, variant design for customized products, and a knowledge push for product intelligent design. The development trends in intelligent design for customized products include big-data-driven intelligent design technology for customized products and customized design tools and applications. The proposed method is verified by the design of precision computer numerical control (CNC) machine tools.

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Simulating Resin Infusion through Textile Reinforcement Materials for the Manufacture of Complex Composite Structures
Robert S. Pierce, Brian G. Falzon
Engineering    2017, 3 (5): 596-607.   https://doi.org/10.1016/J.ENG.2017.04.006
Abstract   HTML   PDF (2946KB)

Increasing demand for weight reduction and greater fuel efficiency continues to spur the use of composite materials in commercial aircraft structures. Subsequently, as composite aerostructures become larger and more complex, traditional autoclave manufacturing methods are becoming prohibitively expensive. This has prompted renewed interest in out-of-autoclave processing techniques in which resins are introduced into a reinforcing preform. However, the success of these resin infusion methods is highly dependent upon operator skill and experience, particularly in the development of new manufacturing strategies for complex parts. Process modeling, as a predictive computational tool, aims to address the issues of reliability and waste that result from traditional trial-and-error approaches. Basic modeling attempts, many of which are still used in industry, generally focus on simulating fluid flow through an isotropic porous reinforcement material. However, recent efforts are beginning to account for the multiscale and multidisciplinary complexity of woven materials, in simulations that can provide greater fidelity. In particular, new multi-physics process models are able to better predict the infusion behavior through textiles by considering the effect of fabric deformation on permeability and porosity properties within the reinforcing material. In addition to reviewing previous research related to process modeling and the current state of the art, this paper highlights the recent validation of a multi-physics process model against the experimental infusion of a complex double dome component. By accounting for deformation-dependent flow behavior, the multi-physics process model was able to predict realistic flow behavior, demonstrating considerable improvement over basic isotropic permeability models.

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An Intelligent Non-Collocated Control Strategy for Ball-Screw Feed Drives with Dynamic Variations
Hui Liu, Jun Zhang, Wanhua Zhao
Engineering    2017, 3 (5): 641-647.   https://doi.org/10.1016/J.ENG.2017.04.007
Abstract   HTML   PDF (1626KB)

The ball-screw feed drive has varying high-order dynamic characteristics due to flexibilities of the slender screw spindle and joints between components, and an obvious feature of non-collocated control when a direct position measurement using a linear scale is employed. The dynamic characteristics and non-collocated situation have long been the source of difficulties in motion and vibration control, and deteriorate the achieved accuracy of the axis motion. In this study, a dynamic model using a frequency-based substructure approach is established, considering the flexibilities and their variation. The position-dependent variation of the dynamic characteristics is then fully investigated. A corresponding control strategy, which is composed of a modal characteristic modifier (MCM) and an intelligent adaptive tuning algorithm (ATA), is then developed. The MCM utilizes a combination of peak filters and notch filters, thereby shaping the plant dynamics into a virtual collocated system and avoiding control spillover. An ATA using an artificial neural network (ANN) as a smooth parameter interpolator updates the parameters of the filters in real time in order to cope with the feed drive’s dynamic variation. Numerical verification of the effectiveness and robustness of the proposed strategy is shown for a real feed drive.

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