<![CDATA[Guide brushes in the mold headlining system play a crucial role in maintaining the alignment of mold movement and the stability of forming pressure distribution. Dimensional degradation due to wear on these components causes an increase in clearance that can potentially trigger misalignment and product thickness deviation. This study aims to determine the optimal preventive maintenance interval based on the production cycle through the integration of wear progression analysis, reliability maintainability, and total cost function. The method used is a quantitative approach based on mathematical modeling by utilizing actual production data and component dimension measurements. The wear program is modeled linearly against the number of production cycles, then associated with an increase in clearance, product thickness deviation, and a decrease in component reliability function. Optimization of the replacement interval is carried out by minimizing the total cost function that combines preventive costs and the risk of loss due to quality failure. The results show that the guide bush wear rate is 0.99998 mm/cycle which causes an increase in clearance of up to 1 mm in 25 production cycles. This condition results in a product thickness deviation of ±0.2 mm, which is still within the design tolerance limit. Reliability analysis shows an R value of ≈0.3679 at this interval, with a system availability level of 99.53%. Based on the optimization of the total cost function, the optimal preventive maintenance interval is obtained at 25,000 production cycles, which provides a balance between product quality stability and operational cost efficiency. The contribution of this research lies in the development of an integrative model that links the wear progression of precision components with product quality degradation and risk-based maintenance decisions. This approach provides a more representative analytical framework than conventional operating time-based methods, especially for mold alignment components in the automotive manufacturing industry.]]>
