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COVID-19 pandemic has accelerated the re-shaping of globalized manufacturing industry. Achieving a high level of resilience is thereby a recognized, essential ability of future manufacturing systems with the advances in smart manufacturing and Industry 4.0. In this work, a conceptual framework for resilient manufacturing strategy enabled by Industrial Internet is proposed. It is elaborated as a four-phase, closed-loop process that centered on proactive industry assessment. Key enabling technologies for the proposed framework are outlined in data acquisition and management, big data analysis, intelligent services, and others. Industrial Internet-enabled implementations in China in response to COVID-19 have then been reviewed and discussed from 3Rs’ perspective, i.e. manufacturer capacity Recovery, supply chain Resilience and emergency Response. It is suggested that an industry-specific and comprehensive selection coordinated with the guiding policy and supporting regulations should be performed at the national, at least regional level.
Tao Peng; QiQi He; Zheng Zhang; Baicun Wang; Xun Xu. Industrial Internet-enabled Resilient Manufacturing Strategy in the Wake of COVID-19 Pandemic: A Conceptual Framework and Implementations in China. Chinese Journal of Mechanical Engineering 2021, 34, 1 -6.
AMA StyleTao Peng, QiQi He, Zheng Zhang, Baicun Wang, Xun Xu. Industrial Internet-enabled Resilient Manufacturing Strategy in the Wake of COVID-19 Pandemic: A Conceptual Framework and Implementations in China. Chinese Journal of Mechanical Engineering. 2021; 34 (1):1-6.
Chicago/Turabian StyleTao Peng; QiQi He; Zheng Zhang; Baicun Wang; Xun Xu. 2021. "Industrial Internet-enabled Resilient Manufacturing Strategy in the Wake of COVID-19 Pandemic: A Conceptual Framework and Implementations in China." Chinese Journal of Mechanical Engineering 34, no. 1: 1-6.
For efficient use of value stream mapping (VSM) for multi-varieties and small batch production in a data-rich environment enabled by Industry 4.0 technologies, a systematic framework of VSM to rejuvenate traditional lean tools is proposed. It addresses the issue that traditional VSM requires intensive on-site investigation and replies on experience, which hinders decisionmaking efficiency in dynamic and complex environments. The proposed framework follows the data-information-knowledge hierarchy model, and demonstrates how data can be collected in a production workshop, processed into information, and then interpreted into knowledge. In this paper, the necessity and limitations of VSM in automated root cause analysis are first discussed, with a literature review on lean production tools, especially VSM and VSM-based decision making in Industry 4.0. An implementation case of a furniture manufacturer in China is presented, where decision tree algorithm was used for automated root cause analysis. The results indicate that automated VSM can make good use of production data to cater for multi-varieties and small batch production with timely on-site waste identification and analysis. The proposed framework is also suggested as a guideline to renew other lean tools for reliable and efficient decision-making.
Hao-Nan Wang; Qi-Qi He; Zheng Zhang; Tao Peng; Ren-Zhong Tang. Framework of automated value stream mapping for lean production under the Industry 4.0 paradigm. Journal of Zhejiang University-SCIENCE A 2021, 22, 382 -395.
AMA StyleHao-Nan Wang, Qi-Qi He, Zheng Zhang, Tao Peng, Ren-Zhong Tang. Framework of automated value stream mapping for lean production under the Industry 4.0 paradigm. Journal of Zhejiang University-SCIENCE A. 2021; 22 (5):382-395.
Chicago/Turabian StyleHao-Nan Wang; Qi-Qi He; Zheng Zhang; Tao Peng; Ren-Zhong Tang. 2021. "Framework of automated value stream mapping for lean production under the Industry 4.0 paradigm." Journal of Zhejiang University-SCIENCE A 22, no. 5: 382-395.
With the aggravation of the global greenhouse effect and environmental pollution, energy saving and emission reduction have already become the consensus of the manufacturing industry to enhance sustainability. A material handling system is an essential component of a modern manufacturing system, and its energy consumption (EC) is a non-negligible part when evaluating the total production EC. As typical transport equipment, automated guided vehicles (AGVs) have been widely applied in various types of manufacturing workshops. Correspondingly, AGV path planning is usually a multi-objective optimization problem, and closely related to the workshop logistics efficiency and the smoothness of the whole manufacturing process. However, the optimization objectives that current AGV path planning research mostly focuses on are transport distance, time, and cost, while EC or EC-related environmental impact indicators are seldom touched on. To address this, an investigation into the energy-saving oriented path planning is executed for a single-load AGV in a discrete manufacturing workshop environment. Based on the analysis of AGV EC characteristics from the perspective of motion state and vehicle structure, transport distance and EC are selected as two optimization objectives, and an energy-efficient AGV path planning (EAPP) model is formulated. Further, two solution methods, i.e., the two-stage solution method and the particle swarm optimization-based method, are put forward to solve the established model. Moreover, the experimental study verifies the effectiveness of the proposed model and its solution methods and indicates that transport task execution order has a significant impact on AGV transport EC.
Zhongwei Zhang; Lihui Wu; Wenqiang Zhang; Tao Peng; Jun Zheng. Energy-efficient path planning for a single-load automated guided vehicle in a manufacturing workshop. Computers & Industrial Engineering 2021, 158, 107397 .
AMA StyleZhongwei Zhang, Lihui Wu, Wenqiang Zhang, Tao Peng, Jun Zheng. Energy-efficient path planning for a single-load automated guided vehicle in a manufacturing workshop. Computers & Industrial Engineering. 2021; 158 ():107397.
Chicago/Turabian StyleZhongwei Zhang; Lihui Wu; Wenqiang Zhang; Tao Peng; Jun Zheng. 2021. "Energy-efficient path planning for a single-load automated guided vehicle in a manufacturing workshop." Computers & Industrial Engineering 158, no. : 107397.
Mass personalization is becoming a reality. It requires responsive and flexible manufacturing operations for producing individualized products in dynamic batch sizes at scale in a cost-effective way. Therefore, manufacturing systems should timely respond to meet changing demands and conditions in the factory, in the supply network, and in customer needs. However, current manufacturing systems fail to adapt to dynamic production environments via changing system configurations and production plans while maintaining stable production performance. Therefore, a manufacturing system is required to be capable of self-optimizing manufacturing operations to achieve flexible, autonomous, and error-tolerant production in the mass personalization context. In this article, we systematically reviewed the literature on Self-Organizing Manufacturing Systems (SOMS) and proposed a complete concept of Self-Organizing Manufacturing Network (SOMN) as the next-generation manufacturing automation technologies for achieving mass personalization. Our review started by tracing the roots, origin, and state-of-the-art research of SOMS and concluded that the existing SOMS work could not achieve the mass personalization goal. As a focus of this review paper, we systematically discussed self-organizing manufacturing's functional requirements to achieve mass personalization and proposed Self-Organizing Manufacturing Network. The concept, functions and essential technological system components (i.e., system modeling and control architecture, peer communications, and adaptive manufacturing control) are discussed by reviewing existing work and highlighting transferrable knowledge from other disciplines. Future research challenges are also discussed.
Zhaojun Qin; Yuqian Lu. Self-organizing manufacturing network: A paradigm towards smart manufacturing in mass personalization. Journal of Manufacturing Systems 2021, 60, 35 -47.
AMA StyleZhaojun Qin, Yuqian Lu. Self-organizing manufacturing network: A paradigm towards smart manufacturing in mass personalization. Journal of Manufacturing Systems. 2021; 60 ():35-47.
Chicago/Turabian StyleZhaojun Qin; Yuqian Lu. 2021. "Self-organizing manufacturing network: A paradigm towards smart manufacturing in mass personalization." Journal of Manufacturing Systems 60, no. : 35-47.
To improve industrial sustainability performance in manufacturing, energy management and optimisation are key levers. This is particularly true for aluminium extrusions manufacturing —an energy-intensive production system with considerable environmental impacts. Many energy management and optimisation approaches have been studied to relieve such negative impact. However, the effectiveness of these approaches is compromised without the support of refined supply-side energy consumption information. Industrial internet of things provides opportunities to acquire refined energy consumption information in its data-rich environment but also poses a range of difficulties in implementation. The existing sensors cannot directly obtain the energy consumption at the granularity of a specific job. To acquire that refined energy consumption information, a supply-side energy modelling method based on existing industrial internet of things devices for energy-intensive production systems is proposed in this paper. First, the job-specified production event concept is proposed, and the layout of the data acquisition network is designed to obtain the event elements. Second, the mathematical models are developed to calculate the energy consumption of the production event in three process modes. Third, the energy consumption information of multiple manufacturing element dimensions can be derived from the mathematical models, and therefore, the energy consumption information on multiple dimensions is easily scaled. Finally, a case of refined energy cost accounting is studied to demonstrate the feasibility of the proposed models.
Chen Peng; Tao Peng; Yang Liu; Martin Geissdoerfer; Steve Evans; Renzhong Tang. Industrial Internet of Things enabled supply-side energy modelling for refined energy management in aluminium extrusions manufacturing. Journal of Cleaner Production 2021, 301, 126882 .
AMA StyleChen Peng, Tao Peng, Yang Liu, Martin Geissdoerfer, Steve Evans, Renzhong Tang. Industrial Internet of Things enabled supply-side energy modelling for refined energy management in aluminium extrusions manufacturing. Journal of Cleaner Production. 2021; 301 ():126882.
Chicago/Turabian StyleChen Peng; Tao Peng; Yang Liu; Martin Geissdoerfer; Steve Evans; Renzhong Tang. 2021. "Industrial Internet of Things enabled supply-side energy modelling for refined energy management in aluminium extrusions manufacturing." Journal of Cleaner Production 301, no. : 126882.
Accurate anomaly detection is critical to the early detection of potential failures of industrial systems and proactive maintenance schedule management. There are some existing challenges to achieve efficient and reliable anomaly detection of an automation system: (1) transmitting large amounts of data collected from the system to data processing components; (2) applying both historical data and real-time data for anomaly detection. This paper proposes a novel Digital Twin-driven anomaly detection framework that enables real-time health monitoring of industrial systems and anomaly prediction. Our framework, adopting the visionary edge AI or edge intelligence (EI) philosophy, provides a feasible approach to ensuring high-performance anomaly detection via implementing Digital Twin technologies in a dynamic industrial edge/cloud network. Edge-based Digital Twin allows efficient data processing by providing computing and storage capabilities on edge devices. A proof-of-concept prototype is developed on a LiBr absorption chiller to demonstrate the framework and technologies' feasibility. The case study shows that the proposed method can detect anomalies at an early stage.
Huiyue Huang; Lei Yang; Yuanbin Wang; Xun Xu; Yuqian Lu. Digital Twin-driven online anomaly detection for an automation system based on edge intelligence. Journal of Manufacturing Systems 2021, 59, 138 -150.
AMA StyleHuiyue Huang, Lei Yang, Yuanbin Wang, Xun Xu, Yuqian Lu. Digital Twin-driven online anomaly detection for an automation system based on edge intelligence. Journal of Manufacturing Systems. 2021; 59 ():138-150.
Chicago/Turabian StyleHuiyue Huang; Lei Yang; Yuanbin Wang; Xun Xu; Yuqian Lu. 2021. "Digital Twin-driven online anomaly detection for an automation system based on edge intelligence." Journal of Manufacturing Systems 59, no. : 138-150.
In the wake of COVID-19, the production demand of medical equipment is increasing rapidly. This type of products is mainly assembled by hand or fixed program with complex and flexible structure. However, the low efficiency and adaptability in current assembly mode are unable to meet the assembly requirements. So in this paper, a new framework of human-robot collaborative (HRC) assembly based on digital twin (DT) is proposed. The data management system of proposed framework integrates all kinds of data from digital twin spaces. In order to obtain the HRC strategy and action sequence in dynamic environment, the double deep deterministic policy gradient (D-DDPG) is applied as optimization model in DT. During assembly, the performance model is adopted to evaluate the quality of resilience assembly. The proposed framework is finally validated by an alternator assembly case, which proves that DT-based HRC assembly has a significant effect on improving assembly efficiency and safety.
Qibing Lv; Rong Zhang; Xuemin Sun; Yuqian Lu; Jinsong Bao. A digital twin-driven human-robot collaborative assembly approach in the wake of COVID-19. Journal of Manufacturing Systems 2021, 60, 837 -851.
AMA StyleQibing Lv, Rong Zhang, Xuemin Sun, Yuqian Lu, Jinsong Bao. A digital twin-driven human-robot collaborative assembly approach in the wake of COVID-19. Journal of Manufacturing Systems. 2021; 60 ():837-851.
Chicago/Turabian StyleQibing Lv; Rong Zhang; Xuemin Sun; Yuqian Lu; Jinsong Bao. 2021. "A digital twin-driven human-robot collaborative assembly approach in the wake of COVID-19." Journal of Manufacturing Systems 60, no. : 837-851.
The vision-based welding status recognition (WSR) provides a basis for online welding quality control. Due to the severe arc and fume interference in the welding area and limited computational resources at the welding edge nodes, it becomes a challenge to mine the most discriminative feature contained in welding images by using a lightweight model. In this paper, we propose an improved three-dimensional convolutional neural network (3DCNN) with separable structure and multi-dimensional attention (3DSMDA-Net) for WSR. The proposed 3DSMDA-Net uses 3DCNN to adaptively extract abstract spatiotemporal features in a welding process and then leverages such time sequence information to improve the recognition accuracy of WSR. In addition, we decompose the classical 3D convolution into depthwise convolution and pointwise convolution to produce a lightweight model. A multi-dimensional attention mechanism is further proposed to compensate for the loss of accuracy caused by the separation operation. The results of experiments reveal that the proposed method reduces the model size to 1/7 of the classical 3DCNN without sacrificing accuracy. The comparison experiment results have indicated that the accuracy of the proposed method is more accurate and noise-resistant than that of the conventional model.
Tianyuan Liu; Jiacheng Wang; Xiaodi Huang; Yuqian Lu; Jinsong Bao. 3DSMDA-Net: An improved 3DCNN with separable structure and multi-dimensional attention for welding status recognition. Journal of Manufacturing Systems 2021, 1 .
AMA StyleTianyuan Liu, Jiacheng Wang, Xiaodi Huang, Yuqian Lu, Jinsong Bao. 3DSMDA-Net: An improved 3DCNN with separable structure and multi-dimensional attention for welding status recognition. Journal of Manufacturing Systems. 2021; ():1.
Chicago/Turabian StyleTianyuan Liu; Jiacheng Wang; Xiaodi Huang; Yuqian Lu; Jinsong Bao. 2021. "3DSMDA-Net: An improved 3DCNN with separable structure and multi-dimensional attention for welding status recognition." Journal of Manufacturing Systems , no. : 1.
Sustainability is increasingly viewed as a desired goal of social development. The logistics industry without exception is aware of the importance of sustainable development. To achieve this goal, the logistics industry is leveraging cutting-edge technologies, such as product service systems (PSS) and cloud manufacturing (CMfg), to design logistics product service systems (LPSS). In LPSS, public logistics resources are allocated to customers and private logistics resources are shared between customers. However, logistics resource allocation and sharing services have been impeded by a lack of efficient methods. In this context, this paper proposes an auction-based cloud service allocation and sharing method for LPSS. Firstly, LPSS is defined and elaborated based on the adoption of PSS in the logistics industry. Secondly, multi-unit Vickery (MV) auctions and one-sided Vickrey-Clarke-Groves (O-VCG) combinatorial auctions are proposed to address logistics resource allocation and sharing problems respectively in LPSS. Two auctions are introduced specifically, and their relevant properties are investigated, including incentive compatibility, allocative efficiency, budget balance, and individual rationality. Thirdly, computational studies are conducted to examine the performance of two auctions. The results reveal that MV auctions can efficiently allocate public logistics resources through ensuring the utility of logistics service providers under dynamic supply and demand. Additionally, O-VCG auctions can effectively integrate and share idle private logistics resources, which promotes sustainability in the logistics industry. Through integrating MV auctions with O-VCG auctions, the utility of logistics service providers can be increased.
Kai Kang; Ray Y. Zhong; Su Xiu Xu; Bing Qing Tan; Lihui Wang; Tao Peng. Auction-based cloud service allocation and sharing for logistics product service system. Journal of Cleaner Production 2020, 278, 123881 .
AMA StyleKai Kang, Ray Y. Zhong, Su Xiu Xu, Bing Qing Tan, Lihui Wang, Tao Peng. Auction-based cloud service allocation and sharing for logistics product service system. Journal of Cleaner Production. 2020; 278 ():123881.
Chicago/Turabian StyleKai Kang; Ray Y. Zhong; Su Xiu Xu; Bing Qing Tan; Lihui Wang; Tao Peng. 2020. "Auction-based cloud service allocation and sharing for logistics product service system." Journal of Cleaner Production 278, no. : 123881.
Selective laser melting is able to produce complicated functional components. However, it may not simultaneously fulfill the diverse requirements of different parts of a component using uniform process parameters. Gradient processing based on the process parameters is proposed to realize localized property design. An industrial valve body in a typical hydraulic valve was presented as a case study to demonstrate this concept. Lightweight design was first executed. Then, an experimental study was performed to investigate the influence of the laser power and exposure time on several properties, including the density, hardness, tensile strength, and tribological performance. Based on the specific property requirements of the shell, the fluid passages, and the contact surfaces of the valve body and electrical energy savings of the SLM process, three sets of process parameters were then selected to correspondingly fabricate different parts of the valve body. Results showed the optimized valve body satisfied the relative density requirement of the shell as well as the yield strength and hardness requirements of the fluid passage. Moreover, compared with a solid valve body fabricated using a single set of default process parameters, the gradient processing leads to reductions from 3.5% to 51.8% in coefficients of friction in different working conditions, and a 4.8% reduction in electric energy consumption. The lightweight design also contributes to a 21.8% reduction in weight and a 22.9% reduction in electric energy consumption. The optimized hydraulic valve also had functional characteristics that were comparable with those for the corresponding conventionally processed valve. This gradient processing was supported by design for property, a valuable technique for integrated design and manufacturing of functional gradient components.
Yi Zhu; Yang Yang; Yanan Wang; Tao Peng; Lei Zhang; Huayong Yang. Localized Property Design and Gradient Processing of a Hydraulic Valve Body Using Selective Laser Melting. IEEE/ASME Transactions on Mechatronics 2020, 26, 1151 -1160.
AMA StyleYi Zhu, Yang Yang, Yanan Wang, Tao Peng, Lei Zhang, Huayong Yang. Localized Property Design and Gradient Processing of a Hydraulic Valve Body Using Selective Laser Melting. IEEE/ASME Transactions on Mechatronics. 2020; 26 (2):1151-1160.
Chicago/Turabian StyleYi Zhu; Yang Yang; Yanan Wang; Tao Peng; Lei Zhang; Huayong Yang. 2020. "Localized Property Design and Gradient Processing of a Hydraulic Valve Body Using Selective Laser Melting." IEEE/ASME Transactions on Mechatronics 26, no. 2: 1151-1160.
Selective laser melting (SLM), a relatively advanced additive manufacturing (AM) technique, enables high design flexibility and manufacturing complexity; therefore, it can facilitate improvement in the environmental performance of a complex component throughout its life cycle. However, existing studies have been unable to conclusively determine whether the environmental impacts of parts produced via AM are lower than those of conventionally manufactured parts. Moreover, few studies have investigated industrially applicable parts with complex inner shapes fabricated via SLM whilst simultaneously considering the design and manufacturing optimization involved thereof. In this study, an industrial hydraulic valve body was investigated in consideration of the part design, material preparation, and part fabrication with respect to their influence on the life-cycle impacts of SLM and conventional manufacturing (CM). The part was first re-designed to achieve a lightweight structure and optimized process parameters. Subsequently, an optimized and a non-optimized sample part were fabricated via SLM. A cradle-to-gate study of this part was conducted, involving a comparison employing life cycle assessment (LCA) for the case of CM. The life-cycle inventory data were obtained from the relevant enterprise, literature, databases, and our experiments. According to our results, the environmental impact of SLM without optimization was 37.42% lower than that of CM with respect to a hydraulic valve body, and the SLM-optimized design can lead to a 10–23% reduction in environmental impacts. Of all the life-cycle stages, the powder preparation stage was observed to exert the highest impact per part. Moreover, the electricity requirement for SLM is the main reason for environmental damage. With the optimization of the valve body design, the proportion of environmental impact caused by SLM processing increases. This indicates that when a part has a high lightweight potential, the SLM process will have a more significant cradle-to-gate environmental impact. In this case, manufacturing optimization will play a more important role. The results can be used to guide AM practitioners to improve the life-cycle environmental performance of AM-fabricated industrial parts through integrated design and manufacturing optimization.
Tao Peng; Yanan Wang; Yi Zhu; Yang Yang; Yiran Yang; Renzhong Tang. Life cycle assessment of selective-laser-melting-produced hydraulic valve body with integrated design and manufacturing optimization: A cradle-to-gate study. Additive Manufacturing 2020, 36, 101530 .
AMA StyleTao Peng, Yanan Wang, Yi Zhu, Yang Yang, Yiran Yang, Renzhong Tang. Life cycle assessment of selective-laser-melting-produced hydraulic valve body with integrated design and manufacturing optimization: A cradle-to-gate study. Additive Manufacturing. 2020; 36 ():101530.
Chicago/Turabian StyleTao Peng; Yanan Wang; Yi Zhu; Yang Yang; Yiran Yang; Renzhong Tang. 2020. "Life cycle assessment of selective-laser-melting-produced hydraulic valve body with integrated design and manufacturing optimization: A cradle-to-gate study." Additive Manufacturing 36, no. : 101530.
This paper investigates the influences of process parameters on part quality, electrical energy consumption, and corresponding energy effectiveness (EE) of AlSi10Mg specimens fabricated by selective laser melting (SLM). Here, EE is defined as the ratio between equivalent quality and specific energy consumption (SEC), where SEC refers to the energy consumption per kilogram of part produced during the building process. The reduction of electrical energy without significantly compromising quality via process parameter configuration was studied. Three parameters, laser power, scan speed and overlap rate, were selected and full factorial design was employed. Single track and single layer experiments were conducted to determine the ranges of process parameters, and multiple layer specimens were prepared for the testing of quality performances, including density, tensile strength, and hardness. The energy consumption of the auxiliary system and laser of the SLM machine were measured for SEC calculation. Results show that the density does not increase with increased SEC, while the tensile strength and hardness show increasing trends. EE can be improved without significantly sacrificing density and hardness, but the tensile strength will be greatly reduced. A case showed that a significant percentage (27.8%) of electrical energy could be saved while satisfying the quality requirements via proper selection of process parameters for the manufacturing of SLMed parts. The findings will help process designers to foster the sustainability of additive manufacturing.
Tao Peng; Jingxiang Lv; Arfan Majeed; Xihui Liang. An experimental investigation on energy-effective additive manufacturing of aluminum parts via process parameter selection. Journal of Cleaner Production 2020, 279, 123609 .
AMA StyleTao Peng, Jingxiang Lv, Arfan Majeed, Xihui Liang. An experimental investigation on energy-effective additive manufacturing of aluminum parts via process parameter selection. Journal of Cleaner Production. 2020; 279 ():123609.
Chicago/Turabian StyleTao Peng; Jingxiang Lv; Arfan Majeed; Xihui Liang. 2020. "An experimental investigation on energy-effective additive manufacturing of aluminum parts via process parameter selection." Journal of Cleaner Production 279, no. : 123609.
Selective laser melting (SLM) is known to be potentially sustainable, enabled manufacturing with complex parts with high flexible design. Existing studies have explored the environmental sustainability of SLMed parts, but there is not yet a consolidated conclusion. In this paper, industrial hydraulic valve body was taken as an example. Its part design, material preparation, and part manufacturing were included in the comparative analysis for SLM and conventional manufacturing (CM). A redesigned part using Design for Property (DfP) method was fabricated using SLM. Then, a cradle-to-gate comparative LCA study of these SLMed and CMed parts was performed, with the data from our experiments, enterprise, literature, and databases. The environmental impact of the SLMed hydraulic valve body was found to be 37.42% lower, and the SLMed optimal design can further reduce the environmental impacts 16.84%. Powder preparation is the highest stage of unit impact. For heavier valve body, with the increase of the potentials of lightweight, the environmental impacts of SLM process will become the main phrase during life cycle. Electricity is the main reason of SLM environmental impact. Therefore, to improve environmental performance, collective efforts from both designer and manufacturer are important.
Yanan Wang; Tao Peng; Yi Zhu; Yang Yang; Renzhong Tang. A comparative life cycle assessment of a selective-laser-melting-produced hydraulic valve body using design for Property. Procedia CIRP 2020, 90, 220 -225.
AMA StyleYanan Wang, Tao Peng, Yi Zhu, Yang Yang, Renzhong Tang. A comparative life cycle assessment of a selective-laser-melting-produced hydraulic valve body using design for Property. Procedia CIRP. 2020; 90 ():220-225.
Chicago/Turabian StyleYanan Wang; Tao Peng; Yi Zhu; Yang Yang; Renzhong Tang. 2020. "A comparative life cycle assessment of a selective-laser-melting-produced hydraulic valve body using design for Property." Procedia CIRP 90, no. : 220-225.
Products with functional fluid channels can transfer mass or energy and have been widely used in various fields, including aerospace, robotic, and biomedical applications. The manufacturing of these products with complex fluid channels is usually difficult for conventional fabrication methods; thus, a portion of the fluid flow function must be sacrificed to realize the feasible and smooth fabrication. Additive manufacturing not only provides a new way to manufacture the products with complex internal channels but also highlights design revolution for existing products with regular internal channels currently limited by conventional fabrication methods. To date, many publications have been reported on the design and manufacture of products with functional fluid channels, but there is a lack of an overview on this topic. In this paper, the current state of additively manufactured products with fluid channels are reviewed. Different functional fluid channels in heat exchangers, fluid power components, bipolar plates of proton exchange membrane fuel cells, and artificial blood vessels are summarized, and the challenges and further direction of additively manufactured products with functional fluid channels are also discussed.
Chao Zhang; Shuai Wang; Jian Li; Yi Zhu; Tao Peng; Huayong Yang. Additive manufacturing of products with functional fluid channels: A review. Additive Manufacturing 2020, 36, 101490 .
AMA StyleChao Zhang, Shuai Wang, Jian Li, Yi Zhu, Tao Peng, Huayong Yang. Additive manufacturing of products with functional fluid channels: A review. Additive Manufacturing. 2020; 36 ():101490.
Chicago/Turabian StyleChao Zhang; Shuai Wang; Jian Li; Yi Zhu; Tao Peng; Huayong Yang. 2020. "Additive manufacturing of products with functional fluid channels: A review." Additive Manufacturing 36, no. : 101490.
Smart manufacturing is arriving. It promises a future of mass-producing highly personalized products via responsive autonomous manufacturing operations at a competitive cost. Of utmost importance, smart manufacturing requires end-to-end integration of intra-business and inter-business manufacturing processes and systems. Such end-to-end integration relies on standards-compliant and interoperable interfaces between different manufacturing stages and systems. In this paper, we present a comprehensive review of the current landscape of manufacturing automation standards, with a focus on end-to-end integrated manufacturing processes and systems towards mass personalization and responsive factory automation. First, we present an authentic vision of smart manufacturing and the unique needs for next-generation manufacturing automation. A comprehensive review of existing standards for enabling manufacturing process automation and manufacturing system automation is presented. Subsequently, focusing on meeting changing demands of efficient production of highly personalized products, we detail several future-proofing manufacturing automation scenarios via integrating various existing standards. We believe that existing automation standards have provided a solid foundation for developing smart manufacturing solutions. Faster, broader and deeper implementation of smart manufacturing automation can be anticipated via the dissemination, adoption, and improvement of relevant standards in a need-driven approach.
Yuqian Lu; Xun Xu; Lihui Wang. Smart manufacturing process and system automation – A critical review of the standards and envisioned scenarios. Journal of Manufacturing Systems 2020, 56, 312 -325.
AMA StyleYuqian Lu, Xun Xu, Lihui Wang. Smart manufacturing process and system automation – A critical review of the standards and envisioned scenarios. Journal of Manufacturing Systems. 2020; 56 ():312-325.
Chicago/Turabian StyleYuqian Lu; Xun Xu; Lihui Wang. 2020. "Smart manufacturing process and system automation – A critical review of the standards and envisioned scenarios." Journal of Manufacturing Systems 56, no. : 312-325.
Additive manufacturing has been increasingly applied. As one of the most commonly used technologies, fused deposition modeling (FDM) still faces the challenge of instable performance. The appearance of the printed part is an important feature to assess its quality. As FDM processes usually take a long time, it is very important to timely identify the defects to avoid unnecessary waste of time and cost. At current stage, this identification work is usually done by the operators. However, it is difficult to realize continuous monitoring for multiple printers and identify surface defects shortly. With the advanced artificial intelligence techniques, a vision-based adaptive monitoring system is proposed in this paper to achieve online monitoring with high efficiency and accuracy. The system design is introduced for common FDM printers that allows one camera to move to different angles and capture the images of the printing part. A heuristic algorithm is then proposed to achieve adaptive shooting position planning according to the part geometries. Furthermore, a convolutional neural network (CNN)-based model is designed to achieve efficient defect classification with high accuracy. A series of experiments have been conducted to illustrate the effectiveness of the proposed system.
Yuanbin Wang; Jiakang Huang; Yuan Wang; Sihang Feng; Tao Peng; Huayong Yang; Jun Zou. A CNN-Based Adaptive Surface Monitoring System for Fused Deposition Modeling. IEEE/ASME Transactions on Mechatronics 2020, 25, 2287 -2296.
AMA StyleYuanbin Wang, Jiakang Huang, Yuan Wang, Sihang Feng, Tao Peng, Huayong Yang, Jun Zou. A CNN-Based Adaptive Surface Monitoring System for Fused Deposition Modeling. IEEE/ASME Transactions on Mechatronics. 2020; 25 (5):2287-2296.
Chicago/Turabian StyleYuanbin Wang; Jiakang Huang; Yuan Wang; Sihang Feng; Tao Peng; Huayong Yang; Jun Zou. 2020. "A CNN-Based Adaptive Surface Monitoring System for Fused Deposition Modeling." IEEE/ASME Transactions on Mechatronics 25, no. 5: 2287-2296.
High-performance aerospace component manufacturing requires stringent in-process geometrical and performance-based quality control. Real-time observation, understanding and control of machining processes are integral to optimizing the machining strategies of aerospace component manufacturing. Digital Twin can be used to model, monitor and control the machining process by fusing multi-dimensional in-context machining process data, such as changes in geometry, material properties and machining parameters. However, there is a lack of systematic and efficient Digital Twin modeling method that can adaptively develop high-fidelity multi-scale and multi-dimensional Digital Twins of machining processes. Aiming at addressing this challenge, we proposed a Digital Twin modeling method based on biomimicry principles that can adaptively construct a multi-physics digital twin of the machining process. With this approach, we developed multiple Digital Twin sub-models, e.g., geometry model, behavior model and process model. These Digital Twin sub-models can interact with each other and compose an integrated true representation of the physical machining process. To demonstrate the effectiveness of the proposed biomimicry-based Digital Twin modeling method, we tested the method in monitoring and controlling the machining process of an air rudder.
Shimin Liu; Jinsong Bao; Yuqian Lu; Jie Li; Shanyu Lu; Xuemin Sun. Digital twin modeling method based on biomimicry for machining aerospace components. Journal of Manufacturing Systems 2020, 58, 180 -195.
AMA StyleShimin Liu, Jinsong Bao, Yuqian Lu, Jie Li, Shanyu Lu, Xuemin Sun. Digital twin modeling method based on biomimicry for machining aerospace components. Journal of Manufacturing Systems. 2020; 58 ():180-195.
Chicago/Turabian StyleShimin Liu; Jinsong Bao; Yuqian Lu; Jie Li; Shanyu Lu; Xuemin Sun. 2020. "Digital twin modeling method based on biomimicry for machining aerospace components." Journal of Manufacturing Systems 58, no. : 180-195.
The demand for aluminum products is expected to continually increase. Die casting is an important technology for processing aluminum products. It is energy-intensive and its melting and holding sub-processes consume large amounts of energy, but in low energy efficiency. Therefore, improving their energy efficiency can significantly reduce energy costs and environmental impact. Based on an in-depth field survey of die casting factories, two obstacles hindering the melting and holding energy efficiency improvement were identified: 1) the determination of optimal furnace operation parameters in the production planning stage, and 2) the timely adjustment of furnace operation parameters when an incident occurs in the production stage. An Internet of Things-enabled model-based approach, including a parameter optimization model and energy-aware incident control strategy, was proposed to address these two issues. The proposed approach was validated in a die casting factory. Optimizing the furnace melting rate and maximum holding height saved 5%–9% cost, product stock was reduced by approximately 3.6% with the online adjustment of the furnace melt-stoppage time, and holding energy consumption was reduced by approximately 2% with the online control of the furnace standby mode. It was revealed that the practical value of the proposed approach was significant for industrial applications.
Weipeng Liu; Tao Peng; Renzhong Tang; Yasushi Umeda; Luoke Hu. An Internet of Things-enabled model-based approach to improving the energy efficiency of aluminum die casting processes. Energy 2020, 202, 117716 .
AMA StyleWeipeng Liu, Tao Peng, Renzhong Tang, Yasushi Umeda, Luoke Hu. An Internet of Things-enabled model-based approach to improving the energy efficiency of aluminum die casting processes. Energy. 2020; 202 ():117716.
Chicago/Turabian StyleWeipeng Liu; Tao Peng; Renzhong Tang; Yasushi Umeda; Luoke Hu. 2020. "An Internet of Things-enabled model-based approach to improving the energy efficiency of aluminum die casting processes." Energy 202, no. : 117716.
Machine-to-machine (M2M) communication is a crucial technology for collaborative manufacturing automation in the Industrial Internet of Things (IIoT)-empowered industrial networks. The new decentralized manufacturing automation paradigm features ubiquitous communication and interoperable interactions between machines. However, peer-to-peer (P2P) interoperable communications at the semantic level between industrial machines is a challenge. To address this challenge, we introduce a concept of Semantic-aware Cyber-Physical Systems (SCPSs) based on which manufacturing devices can establish semantic M2M communications. In this work, we propose a generic system architecture of SCPS and its enabling technologies. Our proposed system architecture adds a semantic layer and a communication layer to the conventional cyber-physical system (CPS) in order to maximize compatibility with the diverse CPS implementation architecture. With Semantic Web technologies as the backbone of the semantic layer, SCPSs can exchange semantic messages with maximum interoperability following the same understanding of the manufacturing context. A pilot implementation of the presented work is illustrated with a proof-of-concept case study between two semantic-aware cyber-physical machine tools. The semantic communication provided by the SCPS architecture makes ubiquitous M2M communication in a network of manufacturing devices environment possible, laying the foundation for collaborative manufacturing automation for achieving smart manufacturing. Another case study focusing on decentralized production control between machines in a workshop also proved the merits of semantic-aware M2M communication technologies.
Yuqian Lu; Muhammad Rizwan Asghar. Semantic communications between distributed cyber-physical systems towards collaborative automation for smart manufacturing. Journal of Manufacturing Systems 2020, 55, 348 -359.
AMA StyleYuqian Lu, Muhammad Rizwan Asghar. Semantic communications between distributed cyber-physical systems towards collaborative automation for smart manufacturing. Journal of Manufacturing Systems. 2020; 55 ():348-359.
Chicago/Turabian StyleYuqian Lu; Muhammad Rizwan Asghar. 2020. "Semantic communications between distributed cyber-physical systems towards collaborative automation for smart manufacturing." Journal of Manufacturing Systems 55, no. : 348-359.
As a promising additive manufacturing (AM) technology, the applications of selective laser melting (SLM) are expanding. Yet, due to the complex structure of SLM machines and low processing rates, the SLM process is highly energy-intensive. Energy forecasting is crucial for accurate evaluation and reduction of SLM energy consumption. However, due to the diversity of SLM machines and their various operating states, the energy consumption of SLM processes is difficult to predict. This article presents a novel method to forecast the energy consumption of SLM processes. The proposed approach is based on the power modelling of machine subsystems and the temporal modelling of sub-processes. Through identifying the working statuses of subsystems of SLM machines in each sub-process, forecast accuracy can be greatly improved. Two cases of aluminium components fabricated by an SLM process using an SLM 280HL facility are selected to demonstrate the effectiveness of the proposed method. Results show that the proposed method outperforms specific, stage-based and subsystem-based energy benchmark models in energy consumption forecasting.
Jingxiang Lv; Tao Peng; Yingfeng Zhang; Yuchang Wang. A novel method to forecast energy consumption of selective laser melting processes. International Journal of Production Research 2020, 59, 2375 -2391.
AMA StyleJingxiang Lv, Tao Peng, Yingfeng Zhang, Yuchang Wang. A novel method to forecast energy consumption of selective laser melting processes. International Journal of Production Research. 2020; 59 (8):2375-2391.
Chicago/Turabian StyleJingxiang Lv; Tao Peng; Yingfeng Zhang; Yuchang Wang. 2020. "A novel method to forecast energy consumption of selective laser melting processes." International Journal of Production Research 59, no. 8: 2375-2391.