Industrial Internet of Things based cycle time control of assembly lines

Ruppert, Tamás; Abonyi, János

doi:10.1109/fiot.2018.8325590

Cited by 19 publications

(7 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the manufacturing shop floor environment, IPS can be beneficial as it can enrich data acquisition for LM [71] and it can be used to obtain dynamic spaghetti diagrams, which are used for the visualization of the value streams [72]. Camera [73,74]; Bar-code scanner [73]; RFID [62,75]; Machine/Event logs [76]; IPS [37] high Real-time monitoring of the position of semi-finished products and resources, calculation of the lead and cycle times [42,43].…”

Section: Utilisation Of Location-information In Lean 40mentioning

confidence: 99%

“…Compared with other technology including RFID and bar-code scanner, IPS can exclude human error and systematic flexibility, and is robust to any layout change. Due to its intrinsic appropriateness for monitoring logistics units within a manufacturing facility-from items up to packages, transport units, and pallets-IPS has been widely applied in many aspects such as cycle time optimization [42], monitoring production line activities [43], logistics management [44], pallet management [45], safety management [46], and human resource monitoring [47].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Indoor Positioning Systems Can Revolutionise Digital Lean

2021

View full text Add to dashboard Cite

The powerful combination of lean principles and digital technologies accelerates waste identification and mitigation faster than traditional lean methods. The new digital lean (also referred to as Lean 4.0) solutions incorporate sensors and digital equipment, yielding innovative solutions that extend the reach of traditional lean tools. The tracking of flexible and configurable production systems is not as straightforward as in a simple conveyor. This paper examines how the information provided by indoor positioning systems (IPS) can be utilised in the digital transformation of flexible manufacturing. The proposed IPS-based method enriches the information sources of value stream mapping and transforms positional data into key-performance indicators used in Lean Manufacturing. The challenges of flexible and reconfigurable manufacturing require a dynamic value stream mapping. To handle this problem, a process mining-based solution has been proposed. A case study is provided to show how the proposed method can be employed for monitoring and improving manufacturing efficiency.

show abstract

Section: Utilisation Of Location-information In Lean 40mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Indoor Positioning Systems Can Revolutionise Digital Lean

2021

View full text Add to dashboard Cite

show abstract

“…Tamas et al [15], proposed a dynamic CT setting algorithm to improve CT of an industrial open station conveyor. The algorithm is developed taking into account the complexity of the production process and product variability.…”

Section: Literature Reviewmentioning

confidence: 99%

Manufacturing Enhancement through Reduction of Cycle Time using Time-Study Statistical Techniques in Automotive Industry

Mohammed

Ahmad

2019

2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS)

View full text Add to dashboard Cite

show abstract

“…Many techniques including intelligent algorithms, deep learning, cognitive radio have been applied to enhance the robustness, accuracy and efficiency of the IIoT [8][9][10][11]. In particular, many technical solutions which have been adopted in wireless sensor networks can be adapted to IIoT environment after being revised according to the corresponding new characteristics of IIoT [12][13][14][15][16][17]. In [12], a wireless sensor networks based on safe navigation scheme for micro flying robots in the IIoT has been raised to detect the static and dynamic obstacles in indoor environment.…”

Section: Introductionmentioning

confidence: 99%

“…In [14], a multi-level DDoS mitigation framework were devised to defend against DDoS attacks for IIoT, which includes the edge computing level, fog computing level, and cloud computing level. In [15], the authors developed an IIoT based solution to ensure a real-time connection between products and assembly lines. The raised dynamic cycle time setting method considered the varying complexity of the product on the basis of the real-time information offered by sensor nodes and indoor positioning systems.…”

Section: Introductionmentioning

confidence: 99%

Q-Learning-Based Dynamic Spectrum Access in Cognitive Industrial Internet of Things

et al. 2018

View full text Add to dashboard Cite

In recent years, Industrial Internet of Things (IIoT) has attracted growing attention from both academia and industry. Meanwhile, when traditional wireless sensor networks are applied to complex industrial field with high requirements for real time and robustness, how to design an efficient and practical cross-layer transmission mechanism needs to be fully investigated. In this paper, we propose a Q-learning-based dynamic spectrum access method for IIoT by introducing cognitive self-learning technical solution to solve the difficulty of distributed and ordered self-accessing for unlicensed terminals. We first devise a simplified MAC access protocol for unlicensed users to use single available channel. Then, a Qlearning-based multi-channels access scheme is raised for the unlicensed users migrating to other lower cells. The channel with most Q value will be considered to be selected. Every mobile terminals store and update their own channel lists due to distributed network mode and non-perfect sensing ability. Numerical results are provided to evaluate the performances of our proposed method on dynamic spectrum access in IIoT. Our proposed method outperforms the traditional simplified accessing methods without self-learning capability on channel usage rate and conflict probability.

show abstract

Industrial Internet of Things based cycle time control of assembly lines

Cited by 19 publications

References 5 publications

Indoor Positioning Systems Can Revolutionise Digital Lean

Indoor Positioning Systems Can Revolutionise Digital Lean

Manufacturing Enhancement through Reduction of Cycle Time using Time-Study Statistical Techniques in Automotive Industry

Q-Learning-Based Dynamic Spectrum Access in Cognitive Industrial Internet of Things

Contact Info

Product

Resources

About