Key Intelligent Technology of Steel Strip Production through Process

Sun, Jie; Wen, Peng; Ding, Jingguo; Li, Xu; Zhang, Dianhua

doi:10.3390/met8080597

Cited by 18 publications

(11 citation statements)

References 9 publications

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“…Although most literature dedicated to the steel manufacturing process has been on improving the efficiencies of the production process (e.g., [3][4][5][6][7]), as early as 1985, investigations were taking place on the effect uncertainty has on the steel industry and associated investments [9]. Min [1], Price et al [10], and Bucur et al [11] have all investigated uncertainties in steel manufacturing from a global perspective.…”

Section: Existing Literaturementioning

confidence: 99%

“…Globally, countries are implementing stricter environmental policies which current steel processes will likely not be able to meet in the near future [2]. In addition to these environmental and energy consumption problems, new steel technologies are being developed to improve existing steel processes [3][4][5][6][7]. The inherent challenges of executing projects internationally, compounded by both the execution and exportation new technologies, equates to executing steel-plant construction overseas an activity riddled with uncertainties and risk [8].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Risk Assessment and Mitigation Model for Overseas Steel-Plant Project Investment with Analytic Hierarchy Process—Fuzzy Inference System

et al. 2018

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This paper presents an analytic hierarchy process (AHP)-fuzzy inference system (FIS) model to aid decision-makers in the risk assessment and mitigation of overseas steel-plant projects. Through a thorough literature review, the authors identified 57 risks associated with international steel construction, operation, and transference of new technologies. Pairwise comparisons of all 57 risks by 14 subject-matter experts resulted in a relative weighting. Furthermore, to mitigate human subjectivity, vagueness, and uncertainty, a fuzzy analysis based on the findings of two case studies was performed. From these combined analyses, weighted individual risk soring resulted in the following top five most impactful international steel project risks: procurement of raw materials; design errors and omissions; conditions of raw materials; technology spill prevention plan; investment cost and poor plant availability and performance. Risk mitigation measures are also presented, and risk scores are re-assessed through the AHP-FIS analysis model depicting an overall project risk score reduction. The model presented is a useful tool for industry performing steel project risk assessments. It also provides decision-makers with a better understanding of the criticality of risks that are likely to occur on international steel projects.

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Section: Existing Literaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Risk Assessment and Mitigation Model for Overseas Steel-Plant Project Investment with Analytic Hierarchy Process—Fuzzy Inference System

et al. 2018

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“…Due to the influence of raw materials, rolling process and external environment, the strip steel surface will inevitably appear oxide scale, inclusion, scratch and other defects in the production process, which not only seriously affects the appearance, but also reduces the fatigue resistance. At the same time, these shortcomings cannot be completely overcome by improving the process [2,3]. Therefore, the classification of surface defects can provide an important reference for the production process.…”

Section: Introductionmentioning

confidence: 99%

Strip Steel Surface Defects Classification Based on Generative Adversarial Network and Attention Mechanism

Hao

Ren

et al. 2022

Metals

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In a complex industrial environment, it is difficult to obtain hot rolled strip steel surface defect images. Moreover, there is a lack of effective identification methods. In response to this, this paper implements accurate classification of strip steel surface defects based on generative adversarial network and attention mechanism. Firstly, a novel WGAN model is proposed to generate new surface defect images from random noises. By expanding the number of samples from 1360 to 3773, the generated images can be further used for training classification algorithm. Secondly, a Multi-SE-ResNet34 model integrating attention mechanism is proposed to identify defects. The accuracy rate on the test set is 99.20%, which is 6.71%, 4.56%, 1.88%, 0.54% and 1.34% higher than AlexNet, VGG16, ShuffleNet v2 1×, ResNet34, and ResNet50, respectively. Finally, a visual comparison of the features extracted by different models using Grad-CAM reveals that the proposed model is more calibrated for feature extraction. Therefore, it can be concluded that the proposed methods provide a significant reference for data augmentation and classification of strip steel surface defects.

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“…Steel strip manufacturing is a complex and large industrial process. It covers multiple levels of control and multiple procedures [1]. The cold rolling process equipment, measuring instruments and their installation positions of a five-stand UCM tandem cold rolling mill in a factory are shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of Rolling Schedule for Five-Stand Tandem Cold Mill

Wang

Yin

et al. 2020

IEEE Access

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The optimization of rolling schedule is the main content of tandem cold rolling which will affect the quality of products directly. A rolling schedule with the objectives of minimum energy consumption, relative power margin and slippage preventing is established. First, in order to make the rolling schedule more accurate in the calculation process, a mathematical model combines with deep neural network is proposed to calculate the rolling force. Second, a multi-objective particle swarm optimizer with dynamic opposition-based learning is proposed to optimize the rolling schedule. It has a new particle learning strategy to update the moving position of particles. Moreover, opposition-based learning is proposed to make the particles jump out of local optima. Finally, the experiments are carried out based on the field data. Simulation results demonstrate that the accuracy of the rolling force is greatly improved. The proposed algorithm has a promising performance on both diversity and convergence. At the same time, the optimized rolling schedule can well balance the rolling power and prevent slipping between five stands comparing with the original rolling schedule. INDEX TERMS Multi-objective optimization, tandem cold rolling, deep neural network, rolling force, rolling schedule.

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Key Intelligent Technology of Steel Strip Production through Process

Cited by 18 publications

References 9 publications

Risk Assessment and Mitigation Model for Overseas Steel-Plant Project Investment with Analytic Hierarchy Process—Fuzzy Inference System

Risk Assessment and Mitigation Model for Overseas Steel-Plant Project Investment with Analytic Hierarchy Process—Fuzzy Inference System

Strip Steel Surface Defects Classification Based on Generative Adversarial Network and Attention Mechanism

Multi-Objective Optimization of Rolling Schedule for Five-Stand Tandem Cold Mill

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