In the context of the rapid development of economy and culture, people’s requirements for material and culture are constantly increasing, and the relationship between graphic design and human life is also increasingly close. With the continuous development of Internet of Things technology, information exchange between people and things can be realized by using various sensing devices, and innovative modern art can be established. Therefore, the focus of culture has gradually shifted to the field of visualization. However, in the visual communication design, the product design method of “mainly two-dimensional plan and supplemented by three-dimensional model”, on the one hand, cannot maximize the intuitive effect of three-dimensional modeling, thus affecting the efficiency of design; on the other hand, problems such as information incoordination and resource waste are likely to occur. In short, people’s requirements for design have changed from a two-dimensional plane space to a three-dimensional space, and a two-dimensional plane design can no longer meet people’s growing demand for artistic design. Aiming at tackling these problems, this paper proposed to establish a visual communication system based on artificial intelligence (AI). Through this method, the image can be made clearer and has a larger field of view and magnification. At the same time, the system was applied to modern art design, which is a type of innovation. The experimental results showed that the maximum distortion of the system designed in this paper was approximately 15%, and the maximum distortion of the traditional sample was about 20%. Compared with conventional samples, this system has great advantages in graphics transformation. In addition, the chromatic aberration of the optical system can be corrected to improve the imaging effect.

In the actual mechanical processing of difficult-to-process materials, normal or abnormal tool wear can lead to processing pauses or terminations, which seriously affects the processing accuracy and efficiency of workpieces, leading to workpiece scrapping. Therefore, predicting and monitoring tool wear during the actual machining process plays a crucial role in controlling tool costs and avoiding workpiece losses caused by tool wear. This paper proposed a tool wear prediction model based on power signals, which predicts tool wear by establishing a mapping between power signals and tool wear. Through drilling experiments for model calibration and validation, we verified that the proposed model can effectively predict tool wear under different parameters. In addition, based on the established prediction model, a real-time monitoring method for tool wear using power signals was proposed and implemented. Through experiments, it has been proven that the proposed method is suitable for monitoring normal and abnormal tool wear in actual machining.

Accurate energy consumption modelling is critical for the improvement of energy efficiency in machining. Existing energy models of machining processes mainly focus on turning or milling, and there are few energy models for drilling. However, since drilling is often applied to roughing and semi-finishing, and the cutting parameters are large, the energy consumption is huge, and it is urgent to study the consumption of energy during the drilling process. In this paper, an energy consumption model for drilling processes was proposed. Idle power, cutting power, and auxiliary power were included in the proposed energy consumption model, using the cutting force to obtain the cutting power during drilling. Further, the relationship between cutting power and auxiliary power was analyzed. Cutting experiments were then carried out which confirmed the correctness of the proposed model. In addition, compared with several existing energy consumption models, the proposed model had better accuracy and applicability. It is expected that the proposed energy consumption model will have applications for the minimization of energy consumption and improvement of energy efficiency but not limited to only drilling energy consumption prediction.