A real-time data-driven collaborative mechanism in fixed-position assembly systems for smart manufacturing

Qian, Cheng; Zhang, Yingfeng; Jiang, Chen; Pan, Shenle; Ye, Rong

doi:10.1016/j.rcim.2019.101841

Cited by 64 publications

(23 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For comparison analysis, the real‐time scheduling strategy proposed in [4] was also tested in this particular scenario, whose outcome is shown in Fig. 3 c .…”

Section: Case Studymentioning

confidence: 99%

“…Although IoT devices have been installed to collect various data, the value‐adding of these data was limited due to the lack of a comprehensive structure that deals with the intercommunication between the networked resources. It can be observed that manufacturing processes will not be assigned to a machine that may recover at some time in the future when using the strategy proposed in [4]. Therefore, the remaining resources (i.e.…”

Section: Case Studymentioning

confidence: 99%

“…The complexity of production processes has been significantly improved due to the variety of production tasks and the specific requirements from individual customers. Consequently, there is a higher chance for exceptions to occur during manufacturing, such as the delayed material handling during task switching, the lack of qualified operators for processes that require specific treatment, and the inaccurate feedback information from customers that misleads the quality inspection system [4]. In addition, some exceptions may be invisible and cannot be timely identified by simply monitor the production progress.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

ResourceNet: a collaboration network among decentralised manufacturing resources for autonomous exception‐handling in smart manufacturing

Qian

Sun

Zhang

2020

IET Collaborative Intelligent Manufacturing

Self Cite

View full text Add to dashboard Cite

“…For comparison analysis, the real‐time scheduling strategy proposed in [4] was also tested in this particular scenario, whose outcome is shown in Fig. 3 c .…”

Section: Case Studymentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

ResourceNet: a collaboration network among decentralised manufacturing resources for autonomous exception‐handling in smart manufacturing

Qian

Sun

Zhang

2020

IET Collaborative Intelligent Manufacturing

Self Cite

View full text Add to dashboard Cite

“…Intelligent equipment is an essential symbol of the modern manufacturing industry. It integrates the smart operation and maintenance system by taking advantage of information technology [1][2][3]. Hence, the monitoring approach of the equipment condition achieves a significant improvement.…”

Section: Introductionmentioning

confidence: 99%

Path Perception Synchronization Trigger Method for Dual Mode Multiplexing Mechanical Vibration Wireless Sensor Networks

Hao¹,

Tang

Huang

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Synchronization triggers for mechanical-vibration wireless sensor networks play an important role in data acquisition and the analysis of multiple nodes. At present, most nodes are designed using a single protocol, which makes it difficult to achieve a balance between network establishment and synchronization. Meanwhile, such synchronization trigger methods rely on a clock, beacon, or communication between nodes. A method based on a clock and beacon requires frequent timestamps, which reduces the synchronization trigger accuracy and increases the power consumption. The communication between nodes requires these nodes to be at a close distance; otherwise, the node cannot receive synchronization information. Aiming at the above problems, a path perception synchronization trigger method based on dual-mode multiplexing and cluster networks is proposed. First, the node is designed using dual mode multiplexing. Second, an adjoint networking method is proposed to construct the cluster network topology. Next, a path perception time measurement method based on the terminal device and terminal assistant measurement without clock synchronization, beacon synchronization, and communication between nodes is discussed. Finally, a maximum time deviation compensation and time block synchronization trigger measurement method are proposed to solve the problems of a time delay compensation and system instruction interruption. The experimental results show that the maximum time error of the path perception synchronization trigger is between 50 and 60 μs, and the minimum time error is between 10 and 16 μs, which significantly improves the synchronization triggering accuracy under conditions that do not rely on clock synchronization, beacon synchronization, or communicating between nodes.

show abstract

“…In addition, these approaches can be very time consuming and difficult to implement in a real-world production system [6]. Therefore, many researchers [7][8][9][10][11][12][13] believe that the development of an effective dynamic scheduling mechanism is crucial for satisfying the various performance criteria of a manufacturing execution system (MES).…”

Section: Introductionmentioning

confidence: 99%

A Reinforcement Learning Approach to Dynamic Scheduling in a Product-Mix Flexibility Environment

Shiue

Lee

2020

IEEE Access

View full text Add to dashboard Cite

Machine bottlenecks, resulting from shifting and unbalanced machine loads caused by resource capacity limitations, impair product-mix flexibility production systems. Thus, the knowledge base (KB) of a dynamic scheduling control system should be dynamic and include a knowledge revision mechanism for monitoring crucial changes that occur in the production system. In this paper, reinforcement learning (RL)-based dynamic scheduling and a selection mechanism for multiple dynamic scheduling rules (MDSRs) are proposed to support the operating characteristics of a flexible manufacturing system (FMS) and semiconductor wafer fabrication (FAB). The proposed RL-based dynamic scheduling MDSR selection mechanism consisted of initial MDSR KB generation and revision phases. According to various performance criteria, the presented approach yields a system performance that is superior to those of the fixed-decision scheduling approach, the machine learning classification approach, and the classical MDSR selection mechanism.

show abstract

A real-time data-driven collaborative mechanism in fixed-position assembly systems for smart manufacturing

Cited by 64 publications

References 28 publications

ResourceNet: a collaboration network among decentralised manufacturing resources for autonomous exception‐handling in smart manufacturing

ResourceNet: a collaboration network among decentralised manufacturing resources for autonomous exception‐handling in smart manufacturing

Path Perception Synchronization Trigger Method for Dual Mode Multiplexing Mechanical Vibration Wireless Sensor Networks

A Reinforcement Learning Approach to Dynamic Scheduling in a Product-Mix Flexibility Environment

Contact Info

Product

Resources

About