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Development and application of intelligent manufacturing system forheat treatment workshop

Chu Xizheng, Deng Xiaohu, Guo Jiayi, Wang Xuerui, Xiao Feng, Xu Wangli

Heat Treatment of Metals 2024, 49 (8): 254-260. doi:10.13251/j.issn.0254-6051.2024.08.043

Abstract （74）

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In order to improve the intelligence level of the heat treatment workshop and address the issues of backward workshop management and "isolated data island", the intelligent manufacturing system for the heat treatment workshop was designed and developed by taking into account the discrete production characteristics of the workshop. The results show that, by adopting the information technology to manage the entire workshop production process, the intelligent manufacturing system realizes the functions such as sharing workshop production information, ensuring transparent and controllable execution processes, and enabling the traceability of production history. Secondly, the system achieves the goals of equipment management, safe production and abnormal warning for the workshop by establishing a digital model, binding and storing real-time data, and meanwhile introducing the concept of safety and environmental protection in the design. Finally, based on the comprehensive workshop data, the data analysis technology is used to automatically calculate and analyze the energy consumption costs and working efficiency of each piece of equipment in the workshop, enabling the refined management of each indicator in the workshop. After the successful implementation of this system in the workshop, it improves the management of workshop production and equipment, enhances the intelligent level of the workshop, and provides supports for green manufacturing, energy conservation and emission reduction in the heat treatment workshop.

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Design and implementation of materials database for heat treatment process simulation

Zhang Lunfeng, Wang Zhihan, Zhao Junyu, An Kang, Xu Jun, Gu Jianfeng

Heat Treatment of Metals 2023, 48 (9): 247-252. doi:10.13251/j.issn.0254-6051.2023.09.042

Abstract （72）

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Material parameters are the crucial data support in heat treatment process simulation. However, at present, there is a lack of relevant material databases in China, and a few existing databases have problems such as low data accuracy, poor integrity, and inability to share data, they only distinguish materials based on chemical composition, which cannot meet the parameter requirements of heat treatment process simulation. Therefore, a data structure focusing on chemical composition and microstructure was designed, and an online special material database was also independently developed. The database optimizes the data storage structure according to the characteristics of material parameters required for heat treatment process simulation. Adopting B/S architecture design realizes data sharing and improves the convenience of data use. Furthermore, by using data mining technology, the database introduces four machine learning algorithms: multivariable linear regression, Bayesian linear regression, decision tree, and random forest, and establishes an innovative data extraction mechanism. The effective data extraction strategy can be determined through the application analysis of existing data, and then the actual data requires at present can be obtained, which preliminarily solves the problem of data missing currently, and strongly supports the development of heat treatment process simulation.

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Effect of aging temperature on Cu-rich precipitates in Fe-Cu-Mn-Ni alloy studied by phase-field method

Wang Kanghong, Xu Yangyang

Heat Treatment of Metals 2023, 48 (9): 265-270. doi:10.13251/j.issn.0254-6051.2023.09.045

Abstract （68）

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A phase-field model for simulating the microstructure evolution in the solid-state phase transformation of multi-component alloys was constructed by coupling the CALPHAD thermodynamic database. Taking Fe-Cu-Mn-Ni alloy as an example, the effect of aging temperature on the precipitation mechanism of Cu-rich precipitates was revealed from the kinetic point of view. The results show that the transformation of α to γ phase and the formation of B2 ring depend on the size of Cu-rich phase. In addition, the change of aging temperature results in the change of the morphology and precipitation mechanism of the Cu-rich phase. The higher the aging temperature, the shorter the precipitation time of the Cu-rich phase, the larger the volume fraction and radius of the Cu-rich phase, and the faster the reduction rate of the precipitated phase quantity.

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Design of fuzzy PID heat treatment temperature control system based on Kalman filter

Li Guangbao, Gao Dong, Lu Yong, Ping Hao, Zhou Yuanyuan

Heat Treatment of Metals 2023, 48 (8): 235-241. doi:10.13251/j.issn.0254-6051.2023.08.038

Abstract （55）

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Aiming at the problems of hysteresis, overshoot, easy interference and slow response of temperature control in the heat treatment process of aerospace complex casting parts, a fuzzy PID(proportional-integral-derivative) temperature control method based on Kalman filter was proposed. Particle swarm optimization (PSO) was used to optimize the parameters of PID controller, and Kalman filter was also used to filter the measurement noise. Finally, the fuzzy strategy was used to realize the rapid response and overshoot control of temperature control. Through the research of fuzzy PID control, fuzzy rules were analyzed and fuzzy reasoning was carried out. According to the requirements of heat treatment process temperature control and its reasoning rules, selecting the most suitable control factor, through the simulation comparison with MATLAB software, it is proved that the accuracy of the designed heat treatment temperature control system is greatly improved, the robustness of the system is better, the response speed is obviously improved, the overshoot is reduced, the adjustment time is shortened, and the interference is also strongly inhibited.

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Laser quenching temperature field and phase transformation hardened zone of 45 steel

Ma Zhenghe, Adayi Xieeryazidan, Liu Junjie, Guli Nigaer

Heat Treatment of Metals 2023, 48 (9): 258-264. doi:10.13251/j.issn.0254-6051.2023.09.044

Abstract （55）

PDF （3371KB）（34）

Transient law of temperature field and phase transformation hardening law of 45 steel during quenching process were obtained by numerical simulation, and the influence of single factor change on phase transformation hardened zone was analyzed. The thermal coupling model of laser quenching process of the 45 steel was established based on software Comsol Multiphysics, the whole of which is then modified by JMatpro calculated phase transformation curves and CCT curves, and the change curves of some thermal physical parameters of the material with temperature. The phase transformation hardened zone was defined based on the phase transformation curve and CCT curve. The results show that the hardened layer has a Gaussian distribution, and the laser quenching hardened layer is obviously different under different process parameters. Under the same other conditions, the depth and width of phase transformation hardened layer are positively correlated with laser power, and negatively correlated with scanning speed and spot diameter. It is concluded that, by combining JMatpro with Comsol Multiphysics, a thermodynamic coupling model of laser quenching considering the change of thermal physical parameters can be established and calculated, by which the prediction of laser quenching temperature field and phase transformation hardened zone can be realized.

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Intelligent scheduling of heat treatment based on multi-system information interaction

Liu Qi, Wan Rutao, Zhu Hongbin, Chong Yonggang, Qu Ming, Duan Xuefeng

Heat Treatment of Metals 2024, 49 (5): 237-242. doi:10.13251/j.issn.0254-6051.2024.05.041

Abstract （47）

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Focused on the heat treatment centralized control system that could centrally control multiple heating equipment scattered in the workshop, and an interactive model of heat treatment production information was generated by constructing multiple systems such as process knowledge base, measurement management system, quality management system, production ERP system and personnel authority management. The digital control and quality monitoring of the whole process for production management, process parameters and equipment management of heat treatment products are realized, as well as intelligent production scheduling and pull visual scheduling are established. The network integration of information such as operator, machines, materials, methods as well as environment of product production is completed, and a solution is found to the key issues such as artificial control of process, the randomness of production scheduling, the paper-based transmission of process records and poor traceability of heat treatment process in the heat treatment production process, which provides a new scheme for improving the management level of heat treatment workshop.

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Technology knowledge base framework of aluminum alloy heat treatment integrated management and control based on multi-parameter variables

Liu Qi, Wan Rutao, Chong Yonggang, Li Mutian, Qu Ming

Heat Treatment of Metals 2023, 48 (9): 253-257. doi:10.13251/j.issn.0254-6051.2023.09.043

Abstract （46）

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In response to the process characteristics of multi-parameter variables in heat treatment, aviation aluminum alloy material type and heat treatment process type were selected as variables, and a multi condition judgment rule was adopted to construct an integrated management and control knowledge base for aluminum alloy heat treatment. The temperature rise, holding, cooling, and quenching parameters during the aluminum alloy heat treatment process were digitized, and the integration of heat treatment control process knowledge base in the heat treatment information control system and directly interfaces with other information systems such as MES in the workshop were achieved. It automatically retrieves data-driven process parameters and completes distribution, promoting the direct control of the heat treatment information control system over the process of heat treatment equipment. This has theoretical and practical significance for promoting the informatization, integration, quality monitoring, and process management of special heat treatment process production.

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Prediction of properties and mechanism of Cu-Ni-Co-Si alloys based on machine learning and genetic algorithm

Zhang Yingfan, Chen Huiqin, Dang Shue, Chen Juan, Xu Quan, Fang Xiaotian, Shi Tenglong, Dai Yunyun

Heat Treatment of Metals 2025, 50 (1): 255-265. doi:10.13251/j.issn.0254-6051.2025.01.040

Abstract （37）

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Application of machine learning in materials research is extensive. However, the task of designing alloys based on many composition and process factors remains a significant difficulty. A machine learning approach to develop alloys by considering the physicochemical qualities, composition, and process of the material was proposed. The property prediction of the Cu-Ni-Co-Si alloy was then optimized by using a genetic algorithm. The recursive elimination method was employed to investigate the potential correlation between the characteristics and alloy properties. The results show that the primary factors influencing the hardness and conductivity characteristics of the alloys are the aging treatment and cold rolling deformation. Furthermore, the physicochemical properties primarily influence the conductivity of the alloy by impacting the density of free electrons and the free path of free electron migration. It affects the hardness of the alloy by exerting influence on solution strengthening and dislocation strengthening.

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Grain size grading method based on improved UNet network and optimized algorithm of grain boundary

Qi Xueqian, Huang Xiaohong, Song Yue, Liu Yanping, Zhang Luyue, Zhang Qingjun

Heat Treatment of Metals 2024, 49 (7): 161-167. doi:10.13251/j.issn.0254-6051.2024.07.025

Abstract （32）

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Grain size has an undeniable impact on the properties of metal materials, and the manual grading methods of grain size is difficult to meet the current detection needs of metal materials. Therefore, for austenite microstructure, a grain size automatic grading method based on improved UNet network and grain boundary optimized algorithm was proposed, and the accuracy of the austenite grading results calculated by the method was analyzed by comparing with the manual grading results. The results show that when the improved UNet network is used to segment austenite grain boundaries, and then the Hough transform based grain boundary optimization algorithm is used to detect and remove twin grain boundaries and branch burr, the grain boundary extraction effect can be effectively optimized, and the accuracy of subsequent grain size calculation can be improved. The absolute error between the austenite grading results obtained by the proposed algorithm and that by the manual method is within 0.25, indicating that the algorithm can efficiently, conveniently and accurately complete the grading of austenite grain size.

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Machine learning to predict effect of annealing temperature and time on mechanical properties of SUS321 stainless steel

Wang Huanhuan, Lu Sujun, Li Yuan, Xu Ning, Zhu Tingxian, Wang Xu, Wei Ning, Wu Dali, Peng Weizhong

Heat Treatment of Metals 2025, 50 (1): 266-271. doi:10.13251/j.issn.0254-6051.2025.01.041

Abstract （24）

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In order to reveal the complex relationship between mechanical properties and heat treatment process of SUS321 stainless steel, a prediction model between annealing temperature, annealing time and mechanical properties of the SUS321 stainless steel was established by machine learning method based on random forest model. The results show that the prediction validity parameter R² of the model for the tensile strength, yield strength and elongation of the SUS321 stainless steel exceeds 0.8, showing a good prediction effect. The analysis based on partial dependence plots and individual conditional expectation plots shows that with the increase of annealing temperature and annealing time, the tensile strength and yield strength of the SUS321 stainless steel decrease, while the elongation increases, and the annealing temperature is the main feature parameter.

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Prediction of critical temperature A₁ and A₃ of medium-Mn steel based on machine learning models

Zhang Zhiye, Wang Yan, Zhang Biao, Ji Ze, Liu Yaliang, Zhang Minghe, Feng Yunli

Heat Treatment of Metals 2025, 50 (2): 268-277. doi:10.13251/j.issn.0254-6051.2025.02.044

Abstract （15）

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In order to facilitate the design of heat treatment process of medium-Mn steel, a machine learning model for predicting the critical temperature A₁ and A₃ of medium-Mn steel was optimized. The critical temperature data of 496 groups of medium-Mn steels with different compositions were obtained by Thermal-Calc simulation software. Mn, Al and C compositions were taken as input characteristics, and phase transition temperatures A₁ and A₃ were taken as output targets. Three indexes of root mean square error (RMSE), mean absolute error (MAE) and determination coefficient (R²) were used to evaluate the prediction effect of the model. From seven machine learning models (LR, DT, SVM, GPR, Boosting, Bagging and ANN), the GPR model for predicting A₁ and the GPR and ANN model for predicting A₃ were screened. The results show that the GPR model for predicting A₁ has sufficient accuracy, that is the optimal model for A₁. The grid search method is used to adjust the hyperparameters of the preliminary model for predicting A₃, and the optimal model of A₃ (single-layer ANN model) is obtained. According to the chemical composition of medium-Mn steel in the applied literature, A₁ and A₃ are predicted by using the optimal model. The overall MAE of the predicted value and measured value of A₁ and A₃ is 9.95 ℃ and 13.57 ℃, respectively, and the minimum difference is 0.30 ℃ and 6.20 ℃, respectively, indicating that the model has high accuracy and can be used to predict the critical temperature of medium-Mn steel.

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Simulation calculation of heating efficiency of heat treatment furnace based on ANSYS Fluent

Yu Liang, Zhang Fei, Tian Tong, Liu Yujia

Heat Treatment of Metals 2025, 50 (2): 278-281. doi:10.13251/j.issn.0254-6051.2025.02.045

Abstract （15）

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To improve the heating efficiency of the heat treatment furnace, a design scheme of adding nozzles on the top of furnace was proposed. The flow field distribution inside the furnace at different firing rates (0-120 m/s) of the furnace top nozzles was simulated by ANSYS Fluent software, and the temperature rise changes of the steel plates inside the furnace at different firing rates (30-210 m/s) were analyzed. The results show that after adding nozzles on the top of furnace, the flow field distribution inside the furnace is more uniform, and as the nozzle firing rate increases, the gas flow becomes more stable and uniform, and the temperature distribution inside the furnace becomes more uniform. At different nozzle firing rates, the heating rate of the steel plate in the furnace increases with the increase of firing rate, but beyond a certain threshold, the increase in heating rate of the steel plates is limited. It can be seen that adding nozzles on the top of furnace and optimizing the firing rate parameters can significantly improve the heating efficiency of the heat treatment furnace.

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