Energy demand modeling of machining processes is the foundation of energy optimization. Energy demand of machining state transition is integral to the energy requirements of the machining process. However, research focus on energy modeling of state transition is scarce. To fill this gap, an energy demand modeling methodology of key state transitions of the turning process is proposed. The establishment of an energy demand model of state transition could improve the accuracy of the energy model of the machining process, which also provides an accurate model and reliable data for energy optimization of the machining process. Finally, case studies were conducted on a CK6153i CNC lathe, the results demonstrating that predictive accuracy with the proposed method is generally above 90% for the state transition cases.

In order to improve production control ability in the gold ore flotation process, the output index in this process was studied. Flotation is an effective gold recovery process. Gold concentrate grade and gold recovery rate are the key output indicators of the flotation process. However, in the existing studies exploring the impact of parameter changes on the output indicators, the control ability of the output indicators is insufficient, and the interaction between variables is inadequately considered. Therefore, a multi-objective optimization model based on response surface methodology and the non-dominated sorting genetic algorithm-II (NSGA-II) is proposed in this paper. Firstly, the experiment was designed based on the Box-Behnken principle. Based on the experimental results, the interaction between variables was analyzed and the response polynomial was fitted. Secondly, a multi-objective optimization model was constructed, and the NSGA-II was used to solve the model. Finally, an example of gold ore flotation was used to verify the effectiveness of the method. The optimal solution was a gold concentrate grade of 75.46 g/t and a gold recovery rate of 85.98%.

Drilling processes, as some of the most widely used machining processes in the manufacturing industry, play an important role in manufacturing process energy-saving programs. However, research focus on energy modeling of drilling processes, especially for the modeling of material-drilling power, are really scarce. To bridge this gap, an improved material-drilling power model is proposed in this paper. The obtained material-drilling power model can improve the accuracy of the material-drilling power and lay a good foundation for energy modeling and optimization of drilling processes. Finally, experimental studies were carried out on an XHK-714F CNC machining center (Hangzhou HangJi Machine Tool Co., Ltd., Hangzhou, China) and a JTVM6540 CNC milling machine (Jinan Third Machine Tool Co., Ltd., Jinan, China). The results showed that predictive accuracies with the proposed model are generally higher than 96% for all the test cases.