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Published in last 1 year |  In last 2 years |  In last 3 years |  All Please wait a minute... For Selected: Download Citations EndNote Ris BibTeX Toggle Thumbnails Select 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）      PDF （4241KB）（48）       Knowledge map    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. Reference | Related Articles | Metrics Select 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）      PDF （3576KB）（18）       Knowledge map    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. Reference | Related Articles | Metrics Select 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）      PDF （6975KB）（12）       Knowledge map    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. Reference | Related Articles | Metrics Select 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）      PDF （2436KB）（19）       Knowledge map    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. Reference | Related Articles | Metrics Select 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）      PDF （3273KB）（13）       Knowledge map    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. Reference | Related Articles | Metrics Select 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）      PDF （5089KB）（8）       Knowledge map    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. Reference | Related Articles | Metrics Select 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）      PDF （2216KB）（5）       Knowledge map    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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