Modelling and robustness analysis of tool maintenance strategy based on unit expected loss optimisation

Abstract To improve the scientific and economic nature of tool operation and maintenance strategies in the CNC machining environment, this paper proposes a method for tool inspection and replacement strategy formulation based on life statistical modelling and unit cost optimization. The method is based on the tool failure data collected by actual enterprises, and firstly, the tool life distribution characteristics are fitted and analysed to verify that they approximately obey the normal distribution. Subsequently, a unit expected loss cost model including failure cost, inspection cost, and replacement cost is constructed, and the optimal maintenance strategy is solved by traversing different inspection intervals and tool replacement cycles to minimise the cost. On this basis, this paper further carries out parameter sensitivity analysis and Monte Carlo simulation experiments to verify the model’s responsiveness and robustness to the fluctuation of part loss cost and life span. The experimental results show that the method can provide quantitative and implementable strategy support for tool operation and maintenance in an automated machining environment without relying on real-time sensor data, and has good engineering applicability and promotion value.