In-Process Control of Turned Threads

该项目解决了机器切割螺纹的问题，这是可靠制造许多产品的关键步骤。研究目的是改进这一过程，以提高准确性。这将通过使用由神经网络控制的非线性压电致动器来实现，以获得5弧分的刀具定位精度。具体地说，在第一阶段，神经网络将学习控制一组在计算机上模拟的高度非线性的压电致动器，而在第二阶段，将用仪器测量机床以在硬件上演示这一概念。刀具定位信息将由已有的激光测量设备提供。有许多类型的金属切割作业可以应用这项技术来减少浪费和提高质量。神经网络的用途是显而易见的，因为它可以学习补偿加工操作中的许多可重复变量，也可以补偿刀具磨损。

This project addresses machine cutting of threads, a key step in reliably manufacturing many products. The research intent is improving the process for enhanced accuracy. This will be accomplished by using nonlinear piezoactuators controlled by neural nets to obtain 5 arc minutes accuracy in positioning a cutting tool. Specifically, in Phase I, neural networks will learn to control a highly nonlinear set of piezoactuators as simulated on a computer, whereas in Phase II, a machine tool will be instrumented to demonstrate the concept in hardware. Tool positioning information will be supplied by a laser measurement device already available. There are many types of metal cutting operations where this technology could be applied to reduce waste and improve quality. The utility of the neural network is apparent since it can learn to compensate for many repeatable variables in the machining operation and also compensate for tool wear.