Intelligent Manufacturing for the Process Industry Driven by Industrial Artificial Intelligence

Yang, Tao; Yi, Xinlei; Lu, Shuaicheng; Johansson, Karl Henrik; Chai, Tianyou

doi:10.1016/j.eng.2021.04.023

Cited by 120 publications

(27 citation statements)

References 15 publications

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“…Using computer vision technology, intelligent industrial system based on convolutional neural network algorithm can solve the problem of granularity recognition. To realize the automation in mature modern applications, the research goals of industrial AI are to develop AI algorithms and AI systems in order to get better recognition and prediction result [1].…”

Section: Introductionmentioning

confidence: 99%

GCN-Unet: A Computer Vision Method with Application to Industrial Granularity Segmentation

Pang¹,

Xiao

Cui

et al. 2022

Mobile Information Systems

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In the field of metallurgical industry, identifying the granularity of raw materials is an essential process during transportation. We propose an image segmentation method by the GCN (global convolutional network)-Unet to extract the contour edge of raw materials granules. To obtain legible images of raw materials, a stationary industrial high speed camera is used to photograph the operating belt conveyor from above. Then, a well-trained GCN-Unet model is used to compute the images and output the results with the contour edge of granules and tiny parts of the materials segmented. We combined the U-Net with several global convolutional network models and boundary refinement blocks and compared the prediction results of the GCN-Unet and the U-Net, showing that the GCN-Unet has a better prediction ability with fewer parameters (7,876,675, while the U-Net has 31,101,448 parameters) and a higher calculating speed (about twice faster than the U-Net). Based on the CNN (convolutional neural network), our computer version method can almost replace traditional manual sampling inspection method for the corresponding overall analysis and the automatically identification process.

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Section: Introductionmentioning

confidence: 99%

GCN-Unet: A Computer Vision Method with Application to Industrial Granularity Segmentation

Pang¹,

Xiao

Cui

et al. 2022

Mobile Information Systems

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“…As for the cost management problem of manufacturing enterprises, not only the management dimensions own multiple analysis hierarchy (such as the actual cost dimension commonly has daily cost, monthly cost, and annual cost three scales [ 26 , 27 ]) but many management objects like manufacturing process also have various hierarchies (see Section 2.1 ). Therefore, the traditional multiscale data model whose objects only exists single-scale could not fully meet the requirement of problem-solving space representation for intelligent manufacturing management.…”

Section: Literature Reviewmentioning

confidence: 99%

A Variable-Scale Data Analysis-Based Identification Method for Key Cost Center in Intelligent Manufacturing

Wang

Gao

2022

Computational Intelligence and Neuroscience

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Although the competitive advantages brought by intelligent manufacturing technology for enterprises have been preliminarily shown, a lack of matched management capacity still greatly limits its effect. This paper focuses on the cost management capacity problem of intelligent manufacturing enterprises. The multiscale cost data model is established on the basis of the three-dimensional cost system model, which contains actual cost, standard cost, and testing cost. According to the scale transformation theory, we propose the dynamic updating mechanism of standard cost. The key cost center identification methods, respectively, for the production performance assessment scenario (KCCI_PPA) and the business decision-making scenario (KCCI_BDM) are also put forward, which could overcome the subjective determination limitation of initial observation scale in the traditional variable-scale data analysis method. Experiments with both industrial statistical and enterprise real datasets verify the efficiency and accuracy of the proposed KCCI_PPA and KCCI_BDM method.

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“…All of these have been employed for the purpose of increasing the level of flexibility, autonomy, interoperability, equipment efficiency, product/process quality and overall productivity, i.e. to achieve an Intelligent management of the manufacturing process [2] As a result of the smart factory concept implementation, Lean, Flexible, Sustainable, Digital, Cloud, Intelligent, Holonic, Additive and Agile manufacturing models have been defined [3], [4], based on utilization of specific groups of I4.0 enabling technologies and their impact on the entire industrial value chain.…”

Section: Introductionmentioning

confidence: 99%

SCADA Systems With Focus on Continuous Manufacturing and Steel Industry: A Survey on Architectures, Standards, Challenges and Industry 5.0

2022

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Recent technological advances encompassed by the smart factory concept have fundamentally changed industrial control systems in the way they are structured and how they operate. Majority of these changes affect Supervisory Control And Data Acquisition (SCADA) systems, shifting them to a higher level of interoperability, heterogeneous networks, big data and toward internet technologies and services in general. However, this transformation does not affect all SCADA systems equally. The immediate industrial environment and controlled processes have a significant impact as well. This paper presents a holistic approach to SCADA systems implemented in continuous flow production control within the steel industry production environment. We outline the multi-layer architecture of the SCADA control framework and the aspects of interoperability and interconnection within the architecture reference models, together with the research challenges and opportunities arising from the recent rapid increasement of the industrial control systems complexity and digital transformation under the Industry 4.0 paradigm, resulting in disrupting levels of the traditional automation pyramid based on Purdue model toward a higher level of integration and interoperability enabling cross-level data exchange empowered by the Industrial Internet of Things. Furthermore, the paper addresses the problem of proprietary SCADA systems and elaborates the causal correlation between SCADA quality requirements and adoption of new technology in relation to the specific industrial environment of the steel manufacturing process.

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Intelligent Manufacturing for the Process Industry Driven by Industrial Artificial Intelligence

Cited by 120 publications

References 15 publications

GCN-Unet: A Computer Vision Method with Application to Industrial Granularity Segmentation

GCN-Unet: A Computer Vision Method with Application to Industrial Granularity Segmentation

A Variable-Scale Data Analysis-Based Identification Method for Key Cost Center in Intelligent Manufacturing

SCADA Systems With Focus on Continuous Manufacturing and Steel Industry: A Survey on Architectures, Standards, Challenges and Industry 5.0

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