Modelling of physical vapour deposition (PVD) process on cutting tool using response surface methodology (RSM)

The Physical Vapour Deposition (PVD) magnetron sputtering process is one of the\nwidely used techniques for depositing thin film coatings on substrates for various\napplications such as integrated circuit fabrication, decorative coatings, and hard coatings\nfor tooling. In the area of coatings on cutting tools, tool life can be improved drastically\nwith the application of hard coatings. Application of coatings on cutting tools for various\nmachining techniques, such as continuous and interrupted cutting, requires different\ncoating characteristics, these being highly dependent on the process parameters under\nwhich they were formed. To efficiently optimise and customise the deposited coating\ncharacteristics, PVD process modelling using RSM methodology was proposed. The aim\nof this research is to develop a PVD magnetron sputtering process model which can predict\nthe relationship between the process input parameters and resultant coating characteristics\nand performance. Response Surface Methodology (RSM) was used, this being one of the\nmost practical and cost effective techniques to develop a process model. Even though\nRSM has been used for the optimisation of the sputtering process, published RSM\nmodelling work on the application of hard coating process on cutting tool is lacking.\nThis research investigated the deposition of TiAlN coatings onto tungsten carbide\ncutting tool inserts using PVD magnetron sputtering process. The input parameters\nevaluated were substrate temperature, substrate bias voltage, and sputtering power; the out\nput responses being coating hardness, coating roughness, and flank wear (coating\nperformance). In addition to that, coating microstructures were investigated to explain the\nbehaviour of the developed model. Coating microstructural phenomena assessed were;\ncrystallite grain size, XRD peak intensity ratio I111/I200 and atomic number percentage\nratio of Al/Ti.\nDesign Expert 7.0.3 software was used for the RSM analysis. Three process\nmodels (hardness, roughness, performance) were successfully developed and validated.\nThe modelling validation runs were within the 90% prediction interval of the developed\nmodels and their residual errors compared to the predicted values were less than 10%. The\nmodels were also qualitatively validated by justifying the behaviour of the output\nresponses (hardness, roughness, and flank wear) and microstructures (Al/Ti ratio,\ncrystallographic peak ratio I111/1200, and grain size) with respect to the variation of the\ninput variables based on the published work by researchers and practitioners in this field.\nThe significant parameters that influenced the coating hardness, roughness, and\nperformance (flank wear) were also identified. Coating hardness was influenced by the\nsubstrate bias voltage, sputtering power, and substrate temperature; coating roughness was\ninfluenced by sputtering power and substrate bias; and coating performance was influenced\nby substrate bias.\nThe analysis also discovered that there was a significant interaction between the\nsubstrate temperature and the sputtering power which significantly influenced coating\nhardness, roughness, and performance; this interaction phenomenon has not been reported\nin previously published literature. The correlation study between coating characteristics,\nmicrostructures and the coating performance (flank wear) suggested that the coating\nperformance correlated most significantly to the coating hardness with Pearson coefficient\nof determination value (R2) of 0.7311. The study also suggested some correlation between\ncoating performance with atomic percentage ratio of Al/Ti and grain size with R2 value of\n0.4762 and 0.4109 respectively.