Optimal design of hot rolling process for C-Mn steel by combining industrial data-driven model and multi-objective optimization algorithm

Wu, Shijing; Zhou, Xiaoguang; Ren, Jia-kuang; Cao, Guangming; Liu, Zhenyu; Shi, Naian

doi:10.1007/s42243-018-0101-8

Cited by 12 publications

(9 citation statements)

References 16 publications

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“…From Table 5, it can be seen that C (10) and Si (11) as the main components of steel have a major influence on TS. Appropriate amount of C and Si can improve the strength of steel and have little impact on plasticity.…”

Section: ) Description Of Hot Rolling Variablesmentioning

confidence: 99%

“…Lalam et al [9] combined principal component analysis (PCA) with BP neural network, which reduced the influence of redundant variables and variable collinearity. Wu et al [10] constructed Bayesian neural network (BNN) to establish a reliable prediction model for mechanical properties of C-Mn steel. Combined with multiobjective optimization algorithm and expert knowledge, it was also successfully applied in the prediction of mechanical properties of Q235B steel.…”

Section: Introductionmentioning

confidence: 99%

“…Flow diagrams of the algorithm Algorithm 1: Learning procedure of parameters Set hyper-parameters of SSGMR While 1 E-step: For i=1:ncomp(number of components) Calculate the probability density function of the i − th Gaussian component p(z = i|x), p(z = i|x, y) End for Using(9) and(10) to calculate the posterior probabilities of the i − th Gaussian component for labeled and unlabeled samples, respectively.…”

mentioning

confidence: 99%

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Just-in-Time Learning-Based Soft Sensor for Mechanical Properties of Strip Steel via Multi-Block Weighted Semisupervised Models

Dong

Tian

Peng³

2020

IEEE Access

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Mechanical properties are important indexes to evaluate the quality of hot rolling strips. It is a research hotspot in the field of hot rolling that realizing timely and accurate soft sensing of mechanical properties. Traditional soft sensing methods have poor performance in the application of strong nonlinearity and multiple working conditions. Moreover, the utilization rate of data is relatively low, which limit the improvement of prediction accuracy. To solve the problems above, a just-in-time learning (JITL) based multi-block weighted semisupervised Gaussian mixture regression (JMWSSGMR) soft sensor is proposed in the paper. There are two stages in the soft sensor: off-line variable blocking and on-line local modeling. In the off-line phase, process variables are divided into different sub-blocks by partial least square (PLS) according to distinct principal component directions. In each sub-block, original variables with high contribution rate are retained. In the on-line phase, optimized Mahalanobis distance is constructed to select the most similar historical samples to the query sample. Next, various real-time semisupervised sub-models are built to estimate the output of the query sample. Finally, predicted values of sub-models are fused and ultimate prediction of mechanical properties is obtained. Case studies are carried out on a numerical example and a hot rolling process. The feasibility and effectiveness of proposed soft sensor are verified by the predicted results.

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Section: ) Description Of Hot Rolling Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Just-in-Time Learning-Based Soft Sensor for Mechanical Properties of Strip Steel via Multi-Block Weighted Semisupervised Models

Dong

Tian

Peng³

2020

IEEE Access

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“…Based on the study of historical production data, different process parameters and chemical compositions were selected to achieve the customized properties requirements. The predictive model established the correlation between the input and the output, and the intelligent algorithm was set as an effective solver to optimize [12].…”

Section: Introductionmentioning

confidence: 99%

A Novel Multi-Objective Process Parameter Interval Optimization Method for Steel Production

Yan

2021

Metals

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Customized small batch orders and sustainable development requirements pose challenges for product quality control and manufacturing process optimization for steel production. Building a multi-quality objective process parameter optimization method that converts the original single target optimization into multi-objective interval capability optimization has become a new method to ensure product quality qualification rate and reduce production costs. Aiming at the multi-quality objective control problem of plate products, we proposed a novel multi-objective process parameter interval optimization model (MPPIO) with equipment process control capability and parameter sensitive analysis. The multi-output support vector regression method was used to establish a multi-quality objective prediction model, which was settled as a verification model for the process parameter optimization results based on the particle swarm optimization algorithm (PSO). The process control capability functions of key parameters were fitted based on the real data in production. With these functions, each optimized particle of the classical PSO was converted into the particle beam of the MIPPO. The iteration process was weight controlled by calculating the Morris sensitivity between each input parameter and output index in the multi-quality objective prediction model, and finally the processing control window of each key parameter was determined according to the process parameter optimization results. The experimental results show that the MPPIO model can obtain the optimal parameter optimization results with the maximum processing capacity and meet the customized processing range requirements. The MPPIO model can reduce the difficulty of control and save production costs while ensuring the product properties is qualified.

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“…In our previous work, the method of data processing for CMn steel was proposed to improve data quality and balance the data distribution. 9) However, the relationship of chemical composition, process parameters and yield strength (YS) of NbTi microalloyed steel is difficult to be clarified. Compared with hot rolling process of CMn steel, the effect factors of the YS of NbTi microalloyed steel involve much more chemical composition and temperature process parameters.…”

Section: Introductionmentioning

confidence: 99%

Composition-Processing-Property Correlation Mining of Nb–Ti Microalloyed Steel Based on Industrial Data

Yang

Liu

2020

Mater. Trans.

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Modeling strength of hot rolled strip based on industrial data may cause misleading predictions because of the high dimension, low quality and unbalanced original data. Industrial data processing is essential to building a successful composition-process-property corresponding relationship model. In current work, the Pauta criterion was implemented to eliminate abnormal data, and uniform grid technique was applied to select out the represented data for obtaining the balanced training data set. Prior to modeling by Bayesian regularization neural network, principal component analysis was applied to alleviate the effect of correlation variables on the modeling. The yield strength prediction model of NbTi microalloyed steel was established with the relative error of «8%, indicating a good agreement between predicted value and measured value. Finally, the relationship of chemical composition, process parameters and yield strength of hot rolled strip under specific process parameters was analyzed for a further investigation.

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Optimal design of hot rolling process for C-Mn steel by combining industrial data-driven model and multi-objective optimization algorithm

Cited by 12 publications

References 16 publications

Just-in-Time Learning-Based Soft Sensor for Mechanical Properties of Strip Steel via Multi-Block Weighted Semisupervised Models

Just-in-Time Learning-Based Soft Sensor for Mechanical Properties of Strip Steel via Multi-Block Weighted Semisupervised Models

A Novel Multi-Objective Process Parameter Interval Optimization Method for Steel Production

Composition-Processing-Property Correlation Mining of Nb–Ti Microalloyed Steel Based on Industrial Data

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