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

  • Md Nizam Abd Rahman

    Student thesis: Doctoral ThesisDoctor of Philosophy


    The Physical Vapour Deposition (PVD) magnetron sputtering process is one of the widely used techniques for depositing thin film coatings on substrates for various applications such as integrated circuit fabrication, decorative coatings, and hard coatings for tooling. In the area of coatings on cutting tools, tool life can be improved drastically with the application of hard coatings. Application of coatings on cutting tools for various
    machining techniques, such as continuous and interrupted cutting, requires different coating characteristics, these being highly dependent on the process parameters under which they were formed. To efficiently optimise and customise the deposited coating characteristics, PVD process modelling using RSM methodology was proposed. The aim of this research is to develop a PVD magnetron sputtering process model which can predict
    the relationship between the process input parameters and resultant coating characteristics and performance. Response Surface Methodology (RSM) was used, this being one of the
    most practical and cost effective techniques to develop a process model. Even though RSM has been used for the optimisation of the sputtering process, published RSM modelling work on the application of hard coating process on cutting tool is lacking.

    This research investigated the deposition of TiAlN coatings onto tungsten carbide cutting tool inserts using PVD magnetron sputtering process. The input parameters evaluated were substrate temperature, substrate bias voltage, and sputtering power; the out
    put responses being coating hardness, coating roughness, and flank wear (coating performance). In addition to that, coating microstructures were investigated to explain the behaviour of the developed model. Coating microstructural phenomena assessed were; crystallite grain size, XRD peak intensity ratio I111/I200 and atomic number percentage ratio of Al/Ti.

    Design Expert 7.0.3 software was used for the RSM analysis. Three process models (hardness, roughness, performance) were successfully developed and validated. The modelling validation runs were within the 90% prediction interval of the developed
    models and their residual errors compared to the predicted values were less than 10%. The models were also qualitatively validated by justifying the behaviour of the output responses (hardness, roughness, and flank wear) and microstructures (Al/Ti ratio,
    crystallographic peak ratio I111/1200, and grain size) with respect to the variation of the input variables based on the published work by researchers and practitioners in this field. The significant parameters that influenced the coating hardness, roughness, and performance (flank wear) were also identified. Coating hardness was influenced by the substrate bias voltage, sputtering power, and substrate temperature; coating roughness was influenced by sputtering power and substrate bias; and coating performance was influenced by substrate bias.

    The analysis also discovered that there was a significant interaction between the substrate temperature and the sputtering power which significantly influenced coating hardness, roughness, and performance; this interaction phenomenon has not been reported
    in previously published literature. The correlation study between coating characteristics, microstructures and the coating performance (flank wear) suggested that the coating performance correlated most significantly to the coating hardness with Pearson coefficient of determination value (R2) of 0.7311. The study also suggested some correlation between coating performance with atomic percentage ratio of Al/Ti and grain size with R2value of
    0.4762 and 0.4109 respectively.
    Date of Award2009
    Original languageEnglish
    Awarding Institution
    • Coventry University
    SponsorsUniversiti Teknikal Malaysia Melaka
    SupervisorPhilip Swanson (Supervisor) & Ashraf Jawaid (Supervisor)

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