Adaptive Control Optimization of Cutting Parameters for High Quality Machining Operations based on Neural Networks and Search Algorithms

In traditional Computer Numerical Control (CNC) systems, machining parameters are usually selected prior to machining according to handbooks or user’s experience. These practices tend to select conservative parameters in order to avoid machining failure and assure product quality specifications. Less conservative practices try to find optimal machining parameters off-line to increase process productivity after conducting experimentation (Chien & Chou, 2001). However, variations during the machining process due to tool wear, temperature changes, vibrations and other disturbances make inefficient any off-line optimization methodology, especially in high quality machining operations where product quality specifications are very restrictive. Therefore, to assure the quality of machining products, reduce costs and increase machining efficiency, cutting parameters must be optimised in real-time according to the actual state of the process. This optimization process in real-time is conducted through an adaptive control of the machining process. The adaptive control applied in machining systems is classified as (Liang et al., 2004; Ulsoy & Koren, 1989): Adaptive Control with Constraints (ACC), Geometric Adaptive Control (GAC), and Adaptive Control with Optimization (ACO). In the ACC systems, process parameters are manipulated in real time to maintain a specific process variable, such as force or power, at a constraint value. Typically, ACC systems are utilized in roughing operations where material removal rate is maximized by maintaining the cutting forces at the highest possible cutting force such that the tool is not in danger of breaking (Zuperl et al., 2005). In the GAC systems, the economic process optimization problem is dominated by the need to maintain product quality such as dimensional accuracy and/or surface finish (Coker & Shin, 1996). GAC systems are typically used in finishing operations with the objective of maintaining a specific part quality despite structural deflections and tool wear. Sensor feedback is often employed to measure surface roughness and dimensional quality between parts and adjustments, so tool offsets and feed overrides can be adjusted for the next part. In the ACO systems, machine settings are selected to optimize a performance index such as production time, unit cost, etc. Traditionally, ACO systems have dealt with adjusting cutting parameters (feed-rate, spindle speed and depth of cut) to maximise O pe n A cc es s D at ab as e w w w .ite ch on lin e. co m