| 1. |
- Li, Xiaoxia, et al.
(författare)
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Review on Learning-based Methods for shop Scheduling problems
- 2022
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Ingår i: Proceedings 2022 IEEE International Conference on e-Business Engineering ICEBE 2022. - : IEEE. - 9781665492447 - 9781665492454 ; , s. 294-298
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Konferensbidrag (refereegranskat)abstract
- Shop scheduling is an effective way for manufacturers to improve their manufacturing performances. However, due to its complexity, it is difficult to deal with shop scheduling problems (SSP). Thus, SSP has received a lot of attention from industry and academia. Various kinds of methods have been proposed to solve SSP. Learning-based method is just one of the most representative methods for SSP. This paper focuses on reviewing the learning-based methods for SSP. Firstly, the methods for SSP are briefly introduced. Then, its description and model are provided and its classification is discussed. Next, the learning-based methods for SSP are classified according to the machine learning technique used in the methods. Based on the classification, the related work on each type of learning-based methods for SSP is summarized and further analyzed and compared with other traditional methods. Finally, the future research opportunities and challenges of the learning-based methods for SSP are summarized.
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| 2. |
- Lu, Xin, et al.
(författare)
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A generic and modularized Digital twin enabled human-robot collaboration
- 2022
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Ingår i: Proceedings 2022 IEEE International Conference on e-Business Engineering ICEBE 2022. - : IEEE. - 9781665492447 - 9781665492454 ; , s. 66-73
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Konferensbidrag (refereegranskat)abstract
- Recently, the manufacturing paradigm shifts from mass production to mass customization, which results in urgently demands for the development of intelligent, flexible and automatic manufacturing systems for handling complex manufacturing tasks with high efficiency. The use of collaborative robots, an essential enabling technology for developing human-robot collaboration (HRC), is on the rise for human-centric intelligent automation design. An effective virtual simulation platform, which can continuously simulate and evaluate HRC performance in different working scenarios, is lacking in developing an HRC system in a sophisticated industrial arena. This paper presents a generic and modularized digital twin enabled HRC framework based on the synergy effect of human, robotic and environment-related factors to provide a flexible, compatible, re-configurable solution to ease the implementation of HRC in the real world. The feasibility of the proposed framework is validated through the practical implementation of a food packaging job, which involves a human operator and an ABB robotic arm collaboratively working together, on an industrial shop.
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| 3. |
- Lu, Xin, et al.
(författare)
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A Generic Digital Twin Framework for Collaborative Supply Chain Development
- 2022
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Ingår i: 2022 5th International Conference on Computing and Big Data (ICCBD 2022). - : IEEE. - 9781665457163 - 9781665457156 - 9781665457170 ; , s. 177-181
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Konferensbidrag (refereegranskat)abstract
- Current Supply Chains (SCs) are complex and diverse along with fragile to SC disruptions. This leads urgently needs to develop an intelligent, transparent, collaborative and resilient SC system to cope with unexpected SC disruptions. Digital twin (DT) is one of the most promising solutions to develop smart SCs that has been extensively studied recent years. However, SCDT paradigm is still at an early stage. This paper presents a generic and modularized five layers DT framework to provide a flexible and collaborative solution, which can be compatible with different DT systems in various SCs. The feasibility of the proposed framework is validated through a practical implementation in a distributed eyewear industry.
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| 4. |
- Shao, Bing, et al.
(författare)
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Deep Learning based Coffee Beans Quality Screening
- 2022
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Ingår i: Proceedings 2022 IEEE International Conference on e-Business Engineering ICEBE 2022. - : IEEE. - 9781665492447 - 9781665492454 ; , s. 271-275
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Konferensbidrag (refereegranskat)abstract
- Coffee bean quality screening is a time-consuming work, and its workload increases abruptly with the rapid development of coffee beverage consumer market. In this work, a CNN-based classifier is developed to categorizing the coffee beans into sour, black, broken, moldy, shell, insect damage and good beans. The screening test results show that the screening accuracy could reach more than 90% for all other beans except for shell beans (88%). Therefore, the proposed method is feasible and promising. Moreover, a cost-effective automatic coffee bean screening system using the developed classifier is manufactured and implemented for a local company.
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| 5. |
- Xu, Lai, et al.
(författare)
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Digital Twins Approach for Sustainable Industry
- 2022
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Ingår i: Advanced Information Systems Engineering Workshops. - Cham : Springer. - 9783031074776 - 9783031074783 ; , s. 126-134
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Konferensbidrag (refereegranskat)abstract
- Sustainable industry is a part of The European Green Deal, which aims to achieve the EU’s climate and environmental goals based on the circular economy. Digital twins are important technologies for realizing industry 4.0 and related sectors. In this paper, we looked at building the DTs for manufacturing, healthcare and construction industrial sectors in Industry 4.0 architecture to realize a sustainable industry.
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| 6. |
- Zhang, Xiaoyang, et al.
(författare)
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A multi-sensor based online tool condition monitoring system for milling process
- 2018
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Ingår i: Procedia CIRP. - : Elsevier. - 2212-8271 .- 2212-8271. ; 72, s. 1136-1141
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Tidskriftsartikel (refereegranskat)abstract
- Tool condition monitoring has been considered as one of the key enabling technologies for manufacturing optimization. Due to the high cost and limited system openness, the relevant developed systems have not been widely adopted by industries, especially Small and Medium-sized Enterprises. In this research, a cost-effective, wireless communication enabled, multi-sensor based tool condition monitoring system has been developed. Various sensor data, such as vibration, cutting force and power data, as well as actual machining parameters, have been collected to support efficient tool condition monitoring and life estimation. The effectiveness of the developed system has been validated via machining cases. The system can be extended to wide manufacturing applications.
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