Transductive neurofuzzy-based torque control of a milling process: results of a case study

This paper presents the design and implementation of an intelligent control system based on local neurofuzzy models of the milling process relayed through an Ehternet-based application. Its purpose is to control the spindle torque of a milling process by using an internal model control paradigm to modify the feed rate in real time. The stabilization of cutting cutting torque is especially necessary in milling processes such as high-spedd roughing of steel moulds and dies tha present minor geometric uncertainties. Thus, maintenance of the curring torque increaes the material removal rate and reduces the risk of damage due to excessive spindle vibration, a very sensitive and expensive component in all high-speed milling machines. Torque control is therefore an interesting challenge from an industrial point of view.

Newer optics efficiently mix, dim, and color-tune LED light

Low-cost, plastic-injected optics mix light from different color LED dies without a significant decrease in average brightness, simplifying luminaire design both optically and electronically. In solid-state lighting, high-flux and high-color rendering index (CRI) light engines may be achieved by arraying and mixing the light from different color dies or phosphors, or a combination of the two, in the LED package. However, these nonhomogeneous sources, when combined with luminaire optics, tend to produce patterns with undesirable artifacts such as spatial and angular nonuniformities and color separation.