Continuous Annealing Research Articles

On the structural and bandgap properties of mist-CVD-grown κ-Ga2O3 post continuous temperature annealing

We investigate the continuous annealing of orthorhombic κ-Ga2O3 films on AlN/c-plane sapphire templates grown by Mist Chemical Vapor Deposition (mist-CVD) using in situ high-temperature x-ray diffraction (HT-XRD). Increasing the annealing temperature from 400 to 1100 °C in both vacuum and ambient air reveals a phase transition onset at 825 °C. High-resolution transmission electron microscopy and XRD demonstrated that annealing within the stability window of 650 to 775 °C effectively improves the crystal quality of the κ-Ga2O3 thin film. Optical transmittance and low-loss electron energy loss spectroscopy (EELS) show the pristine film’s bandgaps to be 4.96 and 4.67 eV, respectively, with reduced bandgaps in annealed films due to increased defect density. EELS-derived optical joint density of states indicates that air-annealing fosters sub-bandgap radiative processes, while vacuum annealing suppresses them, qualitatively correlated with the observed photoluminescence intensity variations. The results of this comprehensive high-temperature annealing study offer crucial insight into the influence of annealing ambient conditions on the crystallographic properties of κ-Ga2O3 films and the associated evolution of extended sub-bandgap states.

Effect of Flash-Ni Plating Layer on Interfacial Behavior of Zn-Al-Mg Coating/DH780 Steel

The Zn-Al-Mg coated DH780 high-strength steel with a flash-Ni plating layer was used in this study. The occurrence state of Ni element at the coating/steel interface was investigated, and the relationship between Mn and Si elements and the internal oxidation at the interface was discussed. The results show that Ni existed between the Fe2Al5 layer and the steel substrate in the form of Fe(Ni) solid solution and Ni elemental substance. The mutual diffusion rate between Fe and Al was greatly reduced after flash-Ni plating, and the continuity of Fe2Al5 layer of the flash-Ni-plated steel was slightly lower than that of the Ni-free steel. Moreover, the diffusion coefficients of Mn and Si in the Ni layer were lower than those in the steel substrate, which indicated that the Ni layer significantly inhibited the diffusion of Mn and Si to the surface. The average depth of internal oxidation was reduced by 70.8% after flash-Ni plating, the average Mn, Si and O contents at the interface after flash-Ni plating were reduced by 19.6%, 6.1% and 22.7%, respectively, which indicated that the flash-Ni plating layer can isolate the steel substrate from the annealing atmosphere, inhibit the internal oxidation and interfacial enrichment of Mn and Si, especially for Mn. Therefore, the flash-Ni plating layer can improve the wettability of high-strength steel sheets during continuous annealing.

PSO-FDM (Particle Swarm Optimization-Finite Difference Method)-Based Simulation Model of Temperature and Velocity of Full-Scale Continuous Annealing Furnace

Improving the accuracy of the temperature field prediction model for continuous annealing line strips and enhancing the model’s adaptability to full-size strips are key technical challenges in continuous annealing lines. This paper developed a continuous annealing temperature prediction model based on a variable step-size strategy for the heating section, even-heat section, slow-cooling section, and fast-cooling section of the continuous annealing line. To improve the prediction accuracy for different strip sizes, the PSO optimization algorithm was employed to determine the optimal heat transfer coefficient for each strip size. Additionally, due to the limited production of certain strip gauges, providing insufficient data for optimization, this study introduces a combined file approach to address gauge vacancies. The experimental results indicate that the optimized model with variable step size can control the absolute prediction error to less than 4 °C, improving prediction accuracy by 61.9% and prediction speed by 26.8% compared to the traditional equal-step prediction model. This study verified that the merger method is effective for addressing side gauge vacancies, while the proposed method is suitable for resolving middle gauge vacancies. The main technical contribution of this study is the establishment of a high-precision prediction model for continuous annealing temperature of variable step length strips, ensuring high temperature control accuracy for full-gauge strips when passing through the continuous annealing production line.

Optimization Study on Hardness of Cold Rolled Strip Steel Based on a Data-Driven Approach

As a key index to measure product quality and processability, the hardness of cold-rolled strip steel has an important influence on the production efficiency of iron and steel enterprises. However, due to the coupling between the process parameters at each stage of the continuous annealing process, and the setting of traditional process parameters depending on the experience of the operator, the lack of scientific and systematic optimization methods, which easily leads to the problem of high hardness fluctuation and low resource utilization. In this study, the data-driven method is used to conduct a preliminary correlation analysis to further clarify the optimal combination of process parameters for achieving the target hardness. The Pearson correlation coefficient is used to determine that carbon content, quenching furnace temperature, and strip speed significantly affect strip hardness. To further clarify the optimal combination of process parameters to achieve the target hardness, a Genetic Algorithm (GA) was tried in this study. The results show that the optimized process parameter combination can effectively improve the stability and consistency of strip hardness, and provide a scientific basis for the process parameter setting in the cold rolled strip production process, which is helpful for enterprises to improve product quality, reduce cost, and enhance market competitiveness.

Crystallinity Control and Strain Release in Wide-Bandgap Perovskite Film via Seed-Induced Growth for Efficient Photovoltaics.

The seed method stands out as a straightforward and efficient approach for fabricating high-performance perovskite solar cells (PSCs). In this study, we propose the utilization of an antisolvent as an additive to induce crystal seeding, thereby facilitating the growth of wide-bandgap perovskite grains. Specifically, we introduce three commonly used antisolvents─ethyl acetate (EA), isopropanol (IPA), and chlorobenzene (CB)─directly into the perovskite precursor solution to generate perovskite seeds, which serve to promote subsequent nucleation. This antisolvent-crystal seeding method (ACSM) results in increased grain sizes, reduced film defects, and overall improved film quality. Consequently, the power conversion efficiencies (PCEs) of 1.647 eV PSCs with EA, IPA, and CB additives are recorded at 19.86%, 20.61%, and 20.45%, respectively, surpassing that of the reference device with a PCE of 18.83%. Furthermore, the stability of the PSCs prepared through ACSM is notably enhanced. Notably, PSCs optimized with IPA retain 75% of the original PCE after being stored in ambient air conditions (25 °C, RH ∼ 15%) for 30 days, better than the CB-added (64%) and the EA-added devices (53%), while the reference devices only retain 31% of the initial PCE. Moreover, even after continuous thermal annealing at 50 °C for 200 h, IPA-assisted devices demonstrate the best stability, followed by those with CB and EA, with the reference exhibiting the poorest stability.

Stable and Highly Emissive Infrared Yb-Doped Perovskite Quantum Cutters Engineered by Machine Learning.

Quantum cutting (QC) allows the conversion of high-energy photons into lower-energy photons, exhibiting great potential for infrared communications. Yb-doped perovskite nanocrystals can achieve an efficient QC process with extremely high photoluminescence quantum yield (PLQY) thanks to the favorable Yb3+ incorporation in the perovskite structure. However, conventionally used oleic acid-oleylamine-based ligand pairs cause instability issues due to highly dynamic binding to surface states that have curbed their potential applications. Herein, zwitterionic type C3-sulfobetaine 3-(N,N-Dimethylpalmitylammonio)propanesulfonate molecule is utilized to build a strong binding state on the nanocrystals' surface through a new phosphine oxide synthesis route. Leveraging machine learning and Bayesian Optimization workflow to determine optimal synthesis conditions, near-infrared PLQY above 190% is achieved. The high PLQY is well maintained after over three months of aging, under high-flux continuous UV irradiation, and long continuous annealing. This is the first report of highly efficient and stable perovskite quantum cutters, which will drive the study of fundamental physics phenomena and near-infrared quantum communications.

Data-Driven Batch Process Monitoring for Continuous Annealing of Cold-Rolled Strip Steel

The continuous annealing process (CAP) is a crucial process of steel production, which has a significant impact on the uniformity and stability of mechanical properties. A novel batch monitoring process based on kernel dissimilarity (KDISSIM) and Kmeans++ is proposed in this paper, focusing on problems such as unequal sample lengths between batches and nonlinearity between variables. First, KDISSIM is used to describe the dissimilarity between batches. Secondly, Kmeans++ is employed to improve the accuracy of clustering tasks based on historical batches. The largest cluster is considered to be at a relatively stable control level, and these batches are further used as training data. Then, the center batch and boundary batch of the training set are used as the reference batch and monitoring threshold for the monitoring model, respectively. Finally, the effectiveness of the proposed method is verified via the actual CAP data, providing a feasible solution for CAP batch monitoring.

Effect of Annealing Temperature on Microstructure and Properties of DH Steel and Optimization of Hole Expansion Property

In this article, DH steel containing Nb and Nb-Cu above 1000 MPa was designed, and its phase transformation law was analyzed through thermal expansion tests. The influence of annealing temperature on the microstructure and properties of DH steel was studied using a continuous annealing simulation testing machine, SEM, and tensile testing machine. The results showed that under a continuous annealing process, the test steel is composed of ferrite, martensite, a small amount of bainite, and residual austenite. The tensile strength decreases with the increase in annealing temperature, Cu element is dissolved in the matrix which produces solid solution strengthening and results in an increase in the strength of Cu-bearing test steel. Finally, 1180 MPa grade DH steel with excellent comprehensive properties was obtained at an annealing temperature of 840 °C and an overaging temperature of 340 °C. The expansion performance of the experimental steel was studied and optimized. Under the step heating annealing process, the experimental steel is composed of tempered martensite, ferrite, and residual austenite, with smaller differences in hardness between different phases, lower average dislocation density, and better expansion performance. Cu-bearing DH steel achieved an excellent match of strength and plasticity of 1289 MPa × 19.8%, with the hole expansion rate of 21.9% and the loss rate of hole expansion rate of 10%.

Experimental Measurement of Emissivity of Polished Steel Strips from a Continuous Annealing Line.

The long-term use of steel strip in various industries makes it an important semi-finished product, which makes it necessary to improve its chemical composition and mechanical properties, reduce its thickness and weight, expand the range of new types of steel strip and increase its production. This entails a large number of technological operations dependent on precise temperature measurement and control. In some industrial plants, the steel strip is in continuous motion, which makes the use of contact measuring devices impossible. When using non-contact measuring devices such as pyrometers or thermal imaging cameras, the emissivity of the materials being measured is a problematic parameter, as setting an incorrect emissivity value to the measuring device results in inaccurate temperature readings. The essence of this research was to establish a measurement method and to perform experimental measurements of the emissivity of a polished steel strip used in a continuous annealing line, the subsequent processing of the data from these measurements and their evaluation. The emissivity measurements were carried out for 5 types of steel strip of different parameters, while the measurement itself was carried out in the long wavelength range of 7.5-14 µm and at strip temperatures of 100-300 °C. Depending on the type of steel strip, the mean emissivity values ranged from 0.0835- to 0.1143. The emissivity of the steel strip increased with increasing strip temperature, and it was not a linear dependence. The emissivity values determined in this research could be applied to measuring equipment in actual production, which could improve the accuracy of temperature measurement in the heat treatment of polished steel strip. Thermal camera measurements in the long wavelength range, taking thermal images and their processing and determining the emissivity value of polished steel strips are the parts of this research that make it different from other already published research.

Investigating the sensitivity of dual-phase steel microstructure to continuous annealing line parameters

The mechanical properties of dual-phase (DP) steels are influenced by ferrite grain size, martensite distribution/morphology, and strength differences between ferrite/martensite phases. A sensitivity study was conducted on a continuous annealing line cycle of DP steel manufacturing process to understand the effect of individual segments on the final microstructural features. From the study results, an optimised cycle was developed to obtain a DP steel microstructure with reduced martensite banding, fine ferrite grain size and less untempered martensite. The main modifications are austenite soaking temperature above Ac3 temperature, higher cooling rate of the slow cooling stage, increased soak time at ferrite soaking temperature, and higher over-aging temperature. The optimised cycle produced DP steel microstructure with reduced martensite banding at a similar phase fraction (35%) and decreased ferrite grain size from 8.4 to 5.1 µm. This improved the mechanical properties, increasing tensile strength from 789 ± 21 to 831 ± 14 MPa and total elongation from 15 ± 3 to 20 ± 2%.

Key nodes of misinformation source inference: A message-passing-based approach

The misinformation spreading in social networks causes unpredictable damage to the networked system, thus inferring the misinformation source is an important research topic in the field of network science and security. Many source inference algorithms have been proposed to find the most likely propagation source through observable snapshot. However, under limited observable conditions, observing different nodes states markedly affects the algorithm’s effectiveness. Yet, we still lack relevant research on which nodes can more accurately assist us in completing source inference. Here, we propose the heuristic message-passing-based algorithm to find the key nodes that can maximize the accuracy of source inference, which uses the average rank of the source in the message-passing method as a measure and performs continuous annealing on this basis to update the set. As a comparison, we propose random selection algorithm as the basic, high-eigenvalue algorithm and high-degree algorithm focused on centrality, and basic message-passing-based algorithm from the perspective of energy entropy in message passing. Through extensive numerical simulation on artificial and real-world networks, compared with other four algorithms, our heuristic message-passing-based algorithm finds the optimal key node set that can more accurately complete source inference. Moreover, it has over 8% higher inference accuracy than other methods in low visibility situations especially.

Development of Q&P steels of third generation under conditions simulating a continuous annealing process, and evaluation of their weldability by the RSW process

Quenching and partitioning, advanced high-strength steels (Q&P-AHSS), are considered as potential candidates to satisfy the current requirements of the automotive industry in terms of passenger safety. Their fabrication at industrial scale has required modifications to well-established processes and thus, obtaining this type of microstructures through easier thermal cycles represents a current challenge for steel manufacturers. Apart from their manufacturing method, their mechanical properties and weldability are equally important. In the present work, Q&P-AHSS were obtained through short-term annealing and isothermal treatments, to further investigate the effects of resistance spot welding (RSW) parameters, on nugget quality and tensile shear strength. Results show that Q&P-AHSS can be welded with success by the RSW process, meeting the required quality specified in the standard AWS D8.9 M-2012.

Scheduling of Continuous Annealing With a Multi-Objective Differential Evolution Algorithm Based on Deep Reinforcement Learning

This paper studies a multi-objective scheduling problem in a continuous annealing operation of the steel industry, which is to simultaneously determine the production batch sizes for coils and their schedules so as to minimize the cost of setup, earliness, and tardiness. A large batch can reduce the setup costs but lead to a cost increase in earliness and tardiness. Thus, the conflict objectives of minimizing setup cost, earliness, and tardiness can be formulated separately. In this paper, we formulate a multi-objective optimization model for the problem and develop an adaptive multi-objective differential evolutionary based on deep reinforcement learning (AMODE-DRL) for effectively obtaining the Pareto solutions. In the proposed AMODE-RDL, DRL is integrated into the MODE algorithm as a controller, which can adaptively select mutation operators and parameters according to different search domains. Computational results on randomly generated instances and the practical problem instances show that DRL can effectively guide MODE to select mutation operators and parameters. The proposed algorithm can obtain better solutions compared to other powerful multi-objective evolutionary and adaptive MODE algorithms. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Setup usually leads to a decrease in production capacity and an increase in production costs. Similarly, earliness and tardiness costs are also crucial. These costs correspond to production, customer demand, and inventory, which are common objectives in production systems. However, the three objectives are generally conflicting, and it is very hard for practitioners to make appropriate decisions with manual experience. The multi-objective optimization methods can provide different scheduling for practitioners who may choose the suitable one according to current working conditions. Accordingly, the proposed model and algorithm can extend to other fields with similar characteristic problems.

Predictive Modeling of Strip Temperature in Continuous Annealing Furnace: An Improved Optimization Algorithm

Predictive Modeling of Strip Temperature in Continuous Annealing Furnace: An Improved Optimization Algorithm

Interfacial Segregation of Sn during the Continuous Annealing and Selective Oxidation of Fe-Mn-Sn Alloys.

The effect of Mn on interfacial Sn segregation during the selective oxidation of Fe-(0-10)Mn-0.03Sn (at.%) alloys was determined for annealing conditions compatible with continuous galvanizing. Significant Sn enrichment was observed at the substrate free surface and metal/oxide interface for all annealing conditions and Mn levels. Sn enrichment at the free surface was insensitive to the Mn alloy concentration, which was partially attributed to the opposing effects of Mn on segregation thermodynamics and kinetics: Mn increases the driving force for Sn segregation via reducing Sn solubility in Fe but also reduces the effective Sn diffusivity by increasing the austenite volume fraction. This insensitivity was exacerbated by the depletion of solute Mn near the surface due to the selective oxidation of Mn. Thus, Sn segregation occurred in regions with a local Mn concentration lower than the nominal bulk composition of the alloys suggested. Sn enrichment at the metal/external oxide interface was reduced compared to the free surface and decreased with increasing bulk Mn content, which was attributed to changes in the external oxide morphology and metal/internal oxide interfaces acting as Sn sinks.

Evolutionary modeling approach based on multiobjective genetic programming for strip quality prediction

In the iron and steel industry, hardness is one of the key indicators of strip quality in the continuous annealing production line (CAPL). However, the complex production process and the strong coupled nonlinearity between process parameters make it difficult to develop accurate mechanism models and pose a challenge for data-driven modeling approaches. More importantly, most of the data-driven learning methods lack interpretability and cannot characterize the mathematical relationship between process parameters and product quality, which in turn makes it extremely hard to understand the process mechanism. Therefore, this paper proposes an interpretable modeling approach (IMA) based on feature decomposition and ensemble to construct interpretable analytical models between process parameters and strip quality. In the IMA, a data-mechanism fusion-based feature decomposition (DM_FD) method is first applied to cope with high-dimensional input feature problems. Then, an improved multiobjective genetic programming algorithm (iMOGP) is developed to construct interpretability sub-models. Finally, a sparse optimization ensemble method (SOE) is used to integrate the sub-models to achieve interpretability and good generalization. Experimental results based on practical strip data demonstrate that the proposed IMA can cope well with high-dimensional input features and achieve model interpretability compared with commonly used machine learning methods and genetic programming (GP)-based modeling methods while ensuring better accuracy and generalization.

On the surface segregation of Sn in cold-rolled Fe under continuous annealing conditions

The surface segregation of Sn in cold-rolled Fe-0.03Sn and Fe-0.01Sn (at.%) model alloys was investigated for annealing parameters characteristic of continuous galvanizing lines (CGLs). The most significant increase in surface coverage occurred during linear heating between 500 and 675 °C, where no significant change in segregation was observed with isothermal holding for 60 – 480 s at peak annealing temperatures of 675 – 825 °C. While the bulk diffusion of Sn in Fe determined the segregation rate during extended isothermal holding up to 10800 s, it could not account for the rapid increase in coverage during heating. It was determined that Sn segregation was accelerated during linear heating by rapid diffusion along dislocation pipes in the cold-rolled starting microstructure. Integrating the decrease in diffusivity due to recrystallization into the McLean model for interfacial segregation resulted in an experimentally verified description of segregation kinetics during linear heating. It was also able to predict the experimentally observed increase in segregation when the linear heating rate between 500 °C and 675 °C was decreased from 5 to 1 °C/s. No significant difference in surface segregation was observed between the 0.01 and 0.03 at.% Sn addition for isothermal holding of 480 s or less, which can be explained by the saturation of easy adsorption sites. A bond-breaking model was used to illustrate their origin from imperfect coordination within the surface layer. As significant surface segregation can be achieved within the CGL processing window, Sn microalloying appears as a promising strategy to improve galvanized coating quality.

Scheduling of parallel continuous annealing lines with alternative processing modes to optimize efficiency under tardiness constraints

Continuous annealing is a core process in steel cold-rolling facilities. It is used to set desired material properties by passing coils of flat steel through a high-temperature furnace in a precisely controlled manner. High efficiency is achieved by feeding the steel through the furnace in the form of a continuous strand. For this purpose, the coils are welded together before being fed into the furnace. Whenever two consecutive coils are incompatible, a special dummy coil called stringer is used to connect them, which reduces efficiency and adds costs and emissions. We consider the scheduling of coils with specific due dates and alternative order-specific processing modes on parallel heterogeneous lines. The problem is to simultaneously assign coils to lines and sequence them on these lines with a defined processing mode with adherence to tardiness constraints while minimizing the number of stringers needed. To address this problem, we formulate a mixed-integer linear program based on a model from the literature and propose a two-phase heuristic solution procedure. The procedure combines an opening phase using shortest path algorithms and local search with an improvement phase based on problem-specific decompositions according to the principles of an Fix-and-Optimize procedure. The results show that our heuristic outperforms a state-of-the-art commercial solver in finding good solutions in short computation time. Based on a sensitivity analysis for several industry-inspired data sets, we observe a trade-off between efficiency and tardiness which strongly depends on the operating conditions.

A dual-population multiobjective co-evolutionary matching ensemble learning for product multi-indicator prediction in continuous annealing

Achieving simultaneous and precise prediction of multiple product performance indicators is crucial to ensure stable production in the continuous annealing process. However, the majority of existing quality prediction modeling methods concentrate on developing single-output regression models that are limited to single-indicator prediction scenarios. When dealing with the multi-indicator prediction scenarios, these methods usually require the construction of a separate model for each performance indicator, which leads to more computational and storage resources and more complex maintenance in practical applications. To address this issue, a dual-population multiobjective co-evolutionary matching ensemble learning method called DP-CEMEL is developed. It utilizes dual-population co-optimization to enable the joint learning of the algorithm on input–output relationships and inter-indicator correlations. More specifically, a two-stage learning based individual encoding and decoding method and a performance boosting degree (PBD) based individual evaluation strategy are designed to achieve co-optimization of the dual populations to evolve a set of individuals (i.e., base learners) with good accuracy and low complexity. Then, a PBD based matching ensemble method is applied to get the final multi-indicator prediction model by the optimal matching and ensemble of the evolved individuals. Experimental results on both benchmark data and practical strip production data demonstrate that the proposed DP-CEMEL attains competitive or better performance compared to other state-of-the-art learning methods.

Multi-working condition performance assessment based on knowledge extraction of optimal operating states for continuous annealing processes

Performance assessment is a key to strip quality improvement and energy consumption reduction of Continuous Annealing Processes (CAP). However, existing methods focus on performing the assessment under a single working condition, and the assessment accuracy must be improved. This study proposes a new multi-working-condition performance assessment method based on the knowledge extraction of the optimal operating states for CAP. First, a mechanism–data fusion-based assessment index construction method is proposed for the key parameter selection. Second, a knowledge extraction strategy for the optimal operating states under multiple working conditions is proposed to construct a benchmark library. Third, a knowledge-enhanced assessment model is built to achieve qualitative performance evaluation and quantitative non-optimal traceability. The experiment based on the process data shows the effectiveness of assessing the operating performance, providing decision guidance for strip quality improvement and energy consumption reduction.