Design of Steel-Cu composites for enhancing thermal properties of plastic processing tools by using a numerical model of the microstructure

of conventional materials used for plastic processing tools, particularly related to thermal conductivity, significantly influence production efficiency, with the cooling time of moulded parts appearing as one of the key factors. Therefore, this study focuses on designing Steel-Cu composites with the aid of a numerical model of the composite's microstructure for enhancing their overall thermal properties. We present a novel procedure for designing such composites, which facilitates the identification of the optimal shape for the phases to improve overall thermal conductivity. We have developed a data-driven homogenization model to aid the optimization process and ensure time-efficient solutions. The data has been generated through numerical homogenization based on the finite element method. The proposed shape optimization procedure has been applied to determine the optimal shape of the Cu phase across various volume fractions. We found out that the optimal shape of the Cu phase is not constant but depends on its volume fraction. Emphasizing the practical utility of the proposed procedure, it is noteworthy that once the data-driven model is established, it enables a time-efficient optimization of the Cu phase shape for arbitrary volume fractions of phases. Consequently, this may expedite the decision-making process for material manufacturers. • A new computational approach to designing composites with a steel matrix and copper inclusions is presented. • A multiscale data-driven model is proposed for the time-efficient optimization of the shape of the copper phase. • The optimal shape of the copper phase is not constant but varies depending on its volume fraction. • The proposed procedure enables rapid determination of the optimal shape of the copper phase for a given volume fraction.