This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434 .

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.

In recent years, the blended teaching model in Chinese universities has developed rapidly, breaking through the time and space constraints of the teaching process and enriching teaching resources. The emergence of different teaching platforms has also actively promoted the modern development of education. However, there is still room for continuous optimization and development of blended learning, and issues such as some students’ inability to effectively utilize online resources, inability to conduct in-depth learning, lack of thinking training, and insufficient ability cultivation and improvement require resolution. This article fully utilizes the basic concepts of deep learning in the existing blended teaching mode, proposes a blended teaching design based on deep learning, and enriches the existing student assessment and evaluation methods, thereby improving the teaching effectiveness of existing blended teaching, deepening the achievements of teaching reform, and improving teaching quality.