Intelligent Manufacturing

Machine tools and other shop-floor equipment need to be able to understand richer information about the part being manufactured and the processes being performed. Machining parameters such as feed rate and spindle speed are typically computed from conservative tables that result in longer-than-optimal cycle times. Off-line optimization can help reduce cycle times, but the off-line method can’t respond to actual cutting conditions like tool wear. Techniques exist for on-line (real-time) optimization using sensed values of spindle power, vibration and other quantities, but these work only when there is sufficient data about what values should be expected.

This project validates data exchange standards for “smart data” that aim to move machining from a widespread but primitive point-to-point description of cutting tool paths to one that includes much more information about what is to be machined. For example, with the ISO 10303 “STEP-NC” standard, machine tool controllers will be able to maximize part throughput automatically, without relying on operators “tweaking the knobs”. STEP-NC’s smart data will enable comparisons between what cutting forces are expected, and what is actually seen, so that problems such as tool wear can be detected before things break and stop production. The goal is to transform the machine tool from an apprentice to an expert, freeing up people to tackle the tough problems rather than watch for the simple ones.