Study On The Tool Load Matching System Applied To Rotary Ultrasonic Machining Accessoring Of The Machine Tool

Advanced ceramic materials, also called non-metallic materials, is evolved from the traditional ceramic, it not only inherits many advantages of traditional ceramics, but also have a range of other character which can match the high performance, high temperature resistance, corrosion resistance, high hardness, high strength. However, these superior performance also makes advanced ceramic materials difficult to process, low processing efficiency, processing expensive, which limits its large-scale application. Therefore, the urgent need is to find a way to make the ceramic materials processing high-efficiency, low-cost.Rotary ultrasonic machining process has been proven to be an effective way of advanced ceramic materials, but since it was introduced to the processing area in 1964, it does not have a large-scale application. Recently scientific and technical personnel through years of research, have finally developed a number of commercial tools which be put into industrial production. However, in these commercial machines, one of the key issues of the rotary ultrasonic machining technology - the tool load matching, is not well resolved.In this paper, combined with the experience of the issue in our laboratory ,the rotary ultrasonic machining of the tool load matching, which is the core issues, has been researched comprehensively, and we developed a tool load matching system applied to rotary ultrasonic machine and the system has been used in machining centers, launching a series of processing system performance experiments in order to prove the validity and reliability of the system.The tool load matching system is comprised of high-precision air bearing which can achieve the minimum 6g tool load, and it can reduce the bad influence to the tool and the workpiece when the tool load is changing rapidly.At the same time the paper build a ultrasonic machining process monitoring system which acquire the axial force and acoustic emission signals. These signals have some feature that can be applied to protect the tool and workpicec. Meanwhile, the paper use the wavelet transform to process the AE signals, and we extract the wavelet energy spectrum coefficient and the coefficients are imported to BP neural network to identify the tool state. In the follow-up rotary ultrasonic grinding experiments, in the same processing conditions, we found that the axial force and acoustic emission signal strength are obviously smaller when using load matching system; while the recognition algorithm based on the wavelet analysis of the AE signal can also passively work effectively.