"Enhancing with wise" is not a smooth road. According to Liu Qiang, a professor at Beijing University of Aeronautics and Astronautics, intelligent machine tools have three basic common problems: information, state perception and data processing; "machine tool-information system-human" communication protocol and interface; intelligent analysis and control algorithms. The challenge for CNC machine tools in the Industrial 4.0 era is the processing of new materials, the higher quality of products and the need for higher efficiency.
The future of manufacturing faces four new transformations: from a single manufacturing scenario to a multi-mixed manufacturing scenario, from experience-based decision making to evidence-based decision making, from solving visible problems to avoiding invisible problems. Control-based machine learning to the transformation of deep learning based on rich data.
The current intelligent machine tools only achieve some simple perception, analysis, feedback, and control, far from reaching the level of human brain labor. The key is that in the area of independent learning, there has not been a revolutionary technological breakthrough. Due to the reliance on human experts, theoretical modeling and data analysis, the accumulation of knowledge is difficult and slow, which ultimately leads to the lack of adaptability and effectiveness of intelligent machine tools.