Application of Response Surface Methodology to the Selection of Machining Variables

This paper presents the use of Response Surface Methodology for analysis of material removal processes. Mathematical models of tool life, surface finish and cutting force are developed in terms of cutting speed, feed, and depth of cut. The models were then used to select the levels of the machining variables such that certain criteria could be achieved. Specific examples include increasing the rate of metal removal without sacrificing surface finish and with a minimum loss in tool life.     

Optimisation of face-milling cutters by use of a computeraided milling-system simulator (Camss)

This paper focuses on the improvement of the cutting process in milling operations by the reduction of vibrations. For that purpose, a non-uniform chiploading pattern is considered as the design parameter, which is achieved by controlling individual insert geometry, namely axial rake and radial rake angles. In the development of the system model, a complete governing equation that describes the milling process is derived and formulated. The model can represent the closed-loop configuration that consists of the cutting process, structural dynamics and a feedback loop. Based on this model, a computer-aided milling-system simulator (CAMSS) is developed. It takes into account the forced-vibration criterion as well as the self-excited vibration to optimise the cutter geometry. Therefore, it should be able to investigate the effects of cutter geometry on chip loading and vibration. A series of comparisons showed that the optimal cutter was subject to fewer vibrations and lower cutting forces than the conventional one.     

Optimization Considerations in Design Reliability by Stress-Strength Interference Theory

Reliability of a component can be computed if the probability distributions for the stress and strength are known. The factors which determine the parameters of the distributions for stress and strength random variables can be controlled in design problems. This leads to the problem of finding the optimal values of these parameters subject to resource and design constraints. Some optimization models are discussed. The special structure of these models is exploited to develop the optimization techniques which are illustrated by simple design examples.