A Novel Curve Pattern Recognition Framework for Hot-Rolling Slab Camber

Self Cite

Optimized production scheduling can greatly improve efficiency and reduce waste in the steel manufacturing industry. With the increasing demands on the economy, the environment, and society, more and more factors need to be considered in the production scheduling process. Currently, only a few methods are developed for the comprehensive evaluation and prioritization of scheduling schemes. This paper proposes a novel MCGDM (multi-criteria group decision making) method for the ranking and selection of production scheduling schemes. First, a novel indicator system involving both qualitative and quantitative indicators is put forward. Diverse statistical methods and evaluation functions are proposed for the evaluation of quantitative indicators. The evaluation method of qualitative indicators is proposed based on heterogeneous data, cloud model theory, and group decision-making techniques. Then, a novel Group AHP model is proposed to determine the weights of all evaluation indicators. Finally, a novel cloud-model-enhanced TOPSIS (technique for order of preference by similarity to ideal solution) method is proposed to rank alternative production scheduling schemes. A practical example is presented to show the implementation details and demonstrate the feasibility of our proposed method. The results and comparative analysis indicate that our hybrid MCGDM method is more reasonable, flexible, practical, and effective in evaluating and ranking production scheduling schemes in an uncertain environment.

Section: Linguistic Termsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Novel Multi-Criteria Group Decision Making Method for Production Scheduling Based on Group AHP and Cloud Model Enhanced TOPSIS

Zhang,

Lv,

Liu

et al. 2024

Self Cite

“…where β r is the linear expansion coefficient, v is the Poisson's ratio of the roll material, T 0 is the initial temperature or the previous temperature, and T is the current roll temperature. However, it is necessary to consider the cascading relationship of parameters between front and rear stands [26,27]. It is essential for better predicting the evolution pattern of the thermal roll profile.…”

Section: Effective Model Of Thermal Roll Profilementioning

confidence: 99%

Online Partition-Cooling System of Hot-Rolled Electrical Steel for Thermal Roll Profile and Its Industrial Application

Wang,

Sun,

Yang

et al. 2024

The shape and convexity are crucial quality assessment indicators for hot-rolled electrical steel strips. Besides bending rolls, shifting rolls, and the original roll profile, the thermal roll profile also plays a significant role in controlling the shape and convexity during the hot-rolling process. However, it is always overlooked due to its dynamic uncertainty. To solve this problem, it is necessary to achieve online cooling-status control for the local thermal expansion of rolls. Based on the existing structure of a mill, a pair of special partition-cooling beams with an intelligent cooling system was designed. For high efficiency and practicality, a new online predictive model was established for the dynamic temperature field of the hot-rolling process. An equivalent treatment was applied to the boundary condition corresponding to the practical cooling water flow. In addition, by establishing the corresponding target distribution curve for the partitioned water flow cooling, online water-flow-partitioning control of the thermal roll profile was achieved. In the practical application process, a large number of onsite results exhibited that the predicted error was within 5% compared to the experimental results. The temperature difference between the upper and lower rolls was within 5 °C, and the temperature difference on both sides of the rolls was controlled within 0.7 °C. The hit rate of convexity (C40) increased by 33%. It was demonstrated that the partition-cooling processes of hot rolling are effective for the local shape and special convexity. They are able to serve as a better control method in the hot-rolling process.

“…Thus, rotating machinery fault diagnosis is of key significance. Fault feature extraction from the measured signals, e.g., vibration, sound, and current, enables accurate fault detection and diagnosis [1][2][3][4][5][6], yet most efforts are based on the assumption of fixed operating conditions. Rotating machinery fault diagnosis under nonstationary operating conditions still deserves deep research.…”

Section: Introductionmentioning

confidence: 99%

Rotating Machinery Fault Diagnosis under Time–Varying Speed Conditions Based on Adaptive Identification of Order Structure

Yu,

Chen,

et al. 2024

Rotating machinery fault diagnosis is of key significance for ensuring safe and efficient operation of various industrial equipment. However, under nonstationary operating conditions, the fault–induced characteristic frequencies are often time–varying. Conventional Fourier spectrum analysis is not suitable for revealing time–varying details, and nonstationary fault feature extraction methods are still in desperate need. Order spectrum can reveal the rotational–speed–related time–varying frequency components as spectral peaks in order domain, thus facilitating fault feature extraction under time–varying speed conditions. However, the speed–unrelated frequency components are still nonstationary after angular–domain resampling, thus causing wide–band features and interferences in the order spectrum. To overcome such a drawback, this work proposes a rotating machinery fault diagnosis method based on adaptive separation of time–varying components and order feature extraction. Firstly, the rotational speed is estimated by the multi–order probabilistic approach (MOPA), thus eliminating the inconvenience of installing measurement equipment. Secondly, adaptive separation of the time–varying frequency component is achieved through time–varying filtering and surrogate test. It effectively eliminates interference from irrelevant components and noise. Finally, a high–resolution order spectrum is constructed based on the average amplitude envelope of each mono–component. It does not involve Fourier transform or angular–domain resampling, thus avoiding spectral leakage and resampling errors. By identifying the fault–related spectral peaks in the constructed order spectrum, accurate fault diagnosis can be achieved. The Rényi entropy values of the proposed order spectrum are significantly lower than those of the traditional order spectrum. This result verifies the effective energy concentration and high resolution of the proposed order spectrum. The results of both numerical simulation and lab experiments confirm the effectiveness of the proposed method in accurately presenting the time–varying frequency components for rotating machinery diagnosing faults.