Monitoring and Control of Manufacturing Process to Assist the Surface Workpiece Quality When Drilling

There is a variety of reasons for the installation of a monitoring system in a manufacturing process. Hole-making mainly drilling is one of the most common operation used and usually is carried out as one of the last steps in the production process. Holes in rotating turbine and compressor disks are among the most highly-stressed geometric features of jet-engines. For manufacturers of jet-engine components it is important to assess the quality of these at an early stage in the manufacturing of the product. The use of commercially available monitoring systems in hole-making has been successful in individual cases so far. Major reasons for this lack of effectiveness are the large material variations within one production batch, the overall difficult machinability of the materials applied, the small lot size which makes “teach-in” operations ineffective. The paper describes a design of adaptive control system for drilling process of aerospace critical components. The proposed system is directed towards the real time control of selected surface roughness parameter. Proposed model for monitoring and control consists of two subsystems: surface roughness prediction subsystem and decision making subsystem. The artificial neural network was employed to calculate surface roughness parameters throughout process monitoring indices such as torque Mz, force Fz, power P and cutting conditions feed f, cutting speed vc. Due to ability to predict nonlinear behaviour and quickly calculate future values, artificial neural networks are ideal for both predictive and adaptive controllers. Test samples were nickel based super alloy Udimet 720 used in discs for gas turbine engines. The experimental results show that predicted values of surface roughness are very close to the values measured experimentally. Advantages of the proposed subsystem for surface roughness prediction are simplicity, computational power and speed, capacity and ability to learn from system changes as they become.