Nickel-based alloys (Ni-based alloys) are used on a large scale in military, aerospace, missile and defense applications with the aim of improving performance, life, and fuel efficiency. Grinding is extensively used for final finishing of these components. Due to their specific material properties, such as work-hardening and low thermal conductivity, the workpieces made of Ni-based alloys are difficult to grind. The difficulty consists in finding the combination of dressing and grinding parameters that generate the prescribed dimensions, finish, and surface integrity of the finished part with high productivity. Increasing productivity is generally associated with increasing the material removal rate. This, in turn, can create detrimental effects on the ground parts such as micro-cracks, high residual stresses, white layers, and thermal damage. This paper presents a novel methodology for determining an optimal combination of dressing and grinding parameters with respect to maximizing the material removal rate, while taking into account a number of process constraints including: grinding force, power, surface roughness, wheel wear, and surface integrity. According to this methodology, predictive models for grinding behavior are determined using a reduced number of experiments based on an in-process, fast sensor data acquisition system. The models are used as inputs for the multiple criterion optimization program based on a genetic algorithm approach. A CNC surface grinding machine was instrumented to allow process monitoring and data collection. The model building and the optimization methodology have been validated using specimens made of Ni-based alloys. The workpiece materials and the range of the grinding parameters were selected according to applications from aerospace industry. The results support the use of adopted methodology for finding the optimal combination of dressing and grinding parameters.
Investigation into grindability of a superalloy and effects of grinding parameters on its surface integrity
