CNC lathe processing is a high-precision, high-efficiency automatic machine tool that uses digital information to control the displacement of parts and cutters. It is an effective way to solve the problems of variable varieties, small batches, complex shapes, and high precision of aerospace products and parts, and to achieve high-efficiency and automated processing.
In the CNC lathe processing industry, the ideal goal of each manufacturer is zero-waste manufacturing. But in the process of achieving this goal, the role and important significance of precision testing technology is self-evident. The processing quality of precision hardware parts and the assembly quality of the whole machine are related to processing equipment, testing equipment (non-standard parts processing) and testing. Information analysis and processing are related, so to achieve zero waste production, from the perspective of precision testing, some issues need to be considered.
In the process of CNC lathe processing, online measurement of workpieces or 100% inspection of workpieces requires research on testing equipment suitable for dynamic or quasi-dynamic, and even special testing equipment that can be integrated into CNC lathe processing to achieve real-time testing. According to the test results, the process parameters are continuously modified, and the CNC lathe processing equipment is supplemented or adjusted or feedback controlled. From the accuracy theory, the dynamic accuracy theory should be studied accordingly, including the evaluation of dynamic accuracy. Research how to make full use of measurement information to achieve zero waste production, through the full use of 100% online measurement data, analyze the dynamic characteristics of the error distribution in the CNC machining and measurement process, and at the same time according to the dynamic characteristics of the processing error and the accuracy loss characteristics of the sensor accuracy , As well as product quality requirements and tolerance regulations, give a basic theoretical model of zero-waste manufacturing, make full use of modern mathematical methods such as artificial neural networks, genetic algorithms, etc. to accurately predict processing quality, and achieve quality control in advance.