Industrial Artificial Intelligence for industry 4.0-based manufacturing systems

Lee, Jay; Davari, Hossein; Singh, Jaskaran; Pandhare, Vibhor

doi:10.1016/j.mfglet.2018.09.002

Cited by 554 publications

(273 citation statements)

References 10 publications

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“…In general, Artificial Intelligence is a cognitive science with strong research activities in the areas of image processing, robotics, machine learning etc. (Lee et al 2018). The developed techniques and knowledge have improved mobile robots both at the device and systems level.…”

Section: Literature Reviewmentioning

confidence: 99%

Increasing flexibility and productivity in Industry 4.0 production networks with autonomous mobile robots and smart intralogistics

et al. 2020

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Manufacturing flexibility improves a firm's ability to react in timely manner to customer demands and to increase production system productivity without incurring excessive costs and expending an excessive amount of resources. The emerging technologies in the Industry 4.0 era, such as cloud operations or industrial Artificial Intelligence, allow for new flexible production systems. We develop and test an analytical model for a throughput analysis and use it to reveal the conditions under which the autonomous mobile robots (AMR)-based flexible production networks are more advantageous as compared to the traditional production lines. Using a circular loop among workstations and inter-operational buffers, our model allows congestion to be avoided by utilizing multiple crosses and analyzing both the flow and the load/unload phases. The sensitivity analysis shows that the cost of the AMRs and the number of shifts are the key factors in improving flexibility and productivity. The outcomes of this research promote a deeper understanding of the role of AMRs in Industry 4.0-based production networks and can be utilized by production planners to determine optimal configurations and the associated performance impact of the AMR-based production networks in as compared to the traditionally balanced lines. This study supports the decision-makers in how the AMR in production systems in process industry can improve manufacturing performance in terms of productivity, flexibility, and costs.

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Section: Literature Reviewmentioning

confidence: 99%

Increasing flexibility and productivity in Industry 4.0 production networks with autonomous mobile robots and smart intralogistics

et al. 2020

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“…The artificial neural network (ANN) is an AI model technique that is already extensively used in property pricing and being widely implemented in other diverse research areas as well. This modelling technique possess highly promising methods and proves significantly efficient for property pricing appraisal research [3]. The most impressively reviewed of this modelling technique is currently applied in a broad range of science disciplines and business fields such as studies in credit card fraud detection, cursive hand writing recognition, loan approvals, real estate analyses and marketing analyses, telecommunications, sound and vibration controls, automotive, and speech recognition [4].…”

Section: A Housing Price Predictionmentioning

confidence: 99%

Machine Learning Housing Price Prediction in Petaling Jaya, Selangor, Malaysia

2019

IJRTE

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This paper demonstrates the utilization of machine learning algorithms in the prediction of housing selling prices on real dataset collected from the Petaling Jaya area, Selangor, Malaysia. To date, literature about research on machine learning prediction of housing selling price in Malaysia is scarce. This paper provides a brief review of the existing machine learning algorithms for the prediction problem and presents the characteristics of the collected datasets with different groups of feature selection. The findings indicate that using irrelevant features from the dataset can decrease the accuracy of the prediction models.

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“…R2R processing is a common processing method for flexible photoelectric thin film materials, and the performance deterioration of the R2R processing device is the primal problem the mass manufacturing of flexible photoelectric thin film materials faces. In recent years, the technology of equipment health prognosis has become a research hotspot [1,2]. In 2017, Lee et al [3] proposed a prognosis algorithm that boosts material removal rate (MRR) based on integrated models and data-driven approach.…”

Section: Introductionmentioning

confidence: 99%

PTS-FNN-Based Health Prediction Method for Flexible Photoelectric Film Material Processing Equipment

Deng

Yao

Jin³

et al. 2020

Mathematical Problems in Engineering

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Roll-to-Roll (R2R) processing is a common processing method for flexible photoelectric film materials. Due to the physical properties of the materials, the change in the performance of the R2R processing equipment can easily cause deformation of the flexible film material, it is particularly important to predict the performance degradation of the processing equipment. Based on the accuracy and real-time requirements of performance degradation prediction, a PTS-FNN model for performance degradation prediction was proposed in this paper, which combines the Possibilistic C-Means (PCM) fuzzy clustering and Takagi–Sugeno Fuzzy Neural Network (TS-FNN). We also studied the PCM classification algorithm of input data of PTS-FNN model, the predecessor network of TS-FNN prediction model and the construction method of post-component network. Finally, the implementation process of PCM classification algorithm and TS-FNN prediction model were given. The R2R processing equipment health prediction experiment system was built and the PTS-FNN model experiment was carried out. The experimental results showed that the training time of PTS-FNN model was 50.37% less than the standard TS-FNN prediction model. The prediction accuracy increased by 5.48%, and the PTS-FNN had no error in the judgment of state 1 and state 4.

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Industrial Artificial Intelligence for industry 4.0-based manufacturing systems

Cited by 554 publications

References 10 publications

Increasing flexibility and productivity in Industry 4.0 production networks with autonomous mobile robots and smart intralogistics

Increasing flexibility and productivity in Industry 4.0 production networks with autonomous mobile robots and smart intralogistics

Machine Learning Housing Price Prediction in Petaling Jaya, Selangor, Malaysia

PTS-FNN-Based Health Prediction Method for Flexible Photoelectric Film Material Processing Equipment

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