Adaptive control generally refers to a special type of control system capable of modifying its algorithms to better regulate physical systems which have time varying or uncertain dynamics. Such systems typically employ advanced nonlinear feedback or feed-forward approaches, and usually involve some sort of learning technique, whereby the control analyzes collective feedback and improves system performance over time.
In the machine tool industry, however, adaptive control refers to a relatively simple strategy whereby the machine control adjusts machining feed rate depending on tool spindle feedback (typically, for example, bulk power, torque or current) in order to maintain constant load. Adaptive control is the automated equivalent of a human operator who observes the power level during the process and adjusts the feed rate override setting up or down to maintain a desired power level. Of course the adaptive control system performs this function more reliably and with faster reaction time than a human being.
Adaptive control systems have been known in the machine tool world for several decades. Presently, adaptive control is commercially available through most major computer numerical control (CNC) producers and from many other so-called third party suppliers. Benefits to the machine tool user include greater process stability in cases where workpiece material, tool condition, or other process conditions vary over time, reduced danger of tool and workpiece damage, reduced requirement for human intervention, and reduced setup and process optimization effort.
Adaptive control systems can usually be turned on or off during the machining process, and typically allow the user to program different set point values for different tools or machining operations within a cycle, such as would be advantageous for a universal machining center with automatic tool changer. Provided that key process conditions, such as engagement length or area of the tool in contact with the workpiece, coolant application, etc. are relatively constant during the machining process, bulk tool spindle power yields a reasonable measure of process health. In this case, present day adaptive control systems offer the above mentioned benefits.
A block diagram representing a typical adaptive control system appears in FIG. 1. Set point power is the main command input, which is compared with power measured from the process. The difference between command and feedback power is calculated, filtered if necessary, and fed into the adaptive control block. The output of the adaptive control system is a feed rate override value, which is used to modify machining feed rates of the part program in real time, such that the actual machining process power is regulated as closely as possible to the programmed set point.
FIG. 2 shows a simplified adaptive control function, which could be implemented within the “adaptive control” block of FIG. 1. This function would be applied for at least one tool or operation of a machining cycle. In this diagram, bulk power measured by the system is indicated along the horizontal axis. The feed rate override output is given along the vertical axis. At the programmed set point value P0, the feed rate override value is 100%. As the process power becomes larger, for example due to hard spot in workpiece material or decreasing tool sharpness, the system reduces the feed rate. As the measured power gets smaller, the system increases the override percentage. The adaptive control function shown in FIG. 2 is linear, but does not have to be. Typical adaptive control systems allow the user to specify upper and lower feed rate override limits, as shown in the diagram.
Adaptive control has not seen wide acceptance in gear manufacturing processes such as bevel gear grinding, bevel gear cutting, and stick blade grinding. The primary reason is that the degree of tool engagement in the workpiece varies continuously in bevel gear manufacturing processes. Controlling bulk power to a constant level would create drastically changing load per grit in the grinding wheel (or load per unit cutting edge length in the cutting tool), whereas process optimization seeks to find the highest constant load per grit (or per unit cutting edge length in a cutting tool). Therefore to be effective, an adaptive control system which measures tool spindle power would additionally require knowledge of tool engagement. However, known adaptive control systems are not capable of directly measuring and processing this additional information, and so are not able to provide the normally expected benefits in bevel applications.
One approach to work around limitations of known adaptive control systems in bevel manufacturing applications would be to divide the machining cycle into small segments with different adaptive control set point values. This approach could be effective for bevel gears, but there is no known method other than trial and error to calculate the different set point values. The adaptive control system would thus require time consuming and tedious tuning for every different part geometry, and would require a high degree of operator expertise, thus defeating the purpose of adaptive control.
Also known in the art are simulation software systems which optimize the tool path in an attempt to stabilize tool load. Such systems have knowledge of the tool engagement in the workpiece, so tool path (depth and angle of cutting) as well as feed rate adjustments can be made in the machining part program to maintain constant load. A limitation of such simulation systems is that they do not work in real time, and thus cannot compensate for typical manufacturing environment variation, such as tool wear, material and geometry variation of tool or workpiece, and machine setup changes due to human variation. Another problem is that known simulation systems are only capable of optimizing tool path for processes that use tools with defined cutting edges, i.e. the software lacks capability to deal with material removal processes with undefined tool edges, such as grinding.