In-Process Control of Turned Threads

这个项目解决了机器切割的线程，一个关键 可靠地制造许多产品的一步。 研究 目的是改进过程以提高准确性。 这将通过使用非线性压电致动器来实现 由神经网络控制，以获得5弧分精度 来定位切割工具。 具体而言，在第一阶段， 神经网络将学会控制高度非线性的 在计算机上模拟的一组压电致动器，而 在第二阶段，将安装一台机床， 在硬件上展示这个概念。 工具定位 信息将由激光测量设备提供 已经有了。 金属切削有很多种 这些技术可以应用于减少 减少浪费，提高质量。 神经元的效用 网络是显而易见的，因为它可以学习补偿 加工操作中的许多可重复变量， 还补偿工具磨损。

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.