Preference by Similarity to an Ideal Solution (TOPSIS) to select the preferable Pareto solutions based on the proximity to the ideal solution. The optimal milling parameters selected through TOPSIS have effectively eliminated the surface defects such as fibre fracture and matrix smearing. Similarly, Ge et al. utilized TOPSIS to rank the 400 Pareto solutions and their distributions can been seen in Fig. ). The top-ranking Pareto solution was selected as the optimal solution. The optimal solution has effectively reduced the cutting temperature (below T g ) and delamination within tolerance. In multi-objective optimization with Pareto method, a self-organizing map (SOM) is often deployed to visualize the trade-off as well as the correlation between design variables and objectives , see Fig. The current multi-objective optimization studies have several limitations. Table summarizes the existing optimization methods, which often assume ideal scenarios where the impact of tool wear is neglected. However, in practical CFRP machining applications such as multi-hole drilling, time-varying tool wear can lead to instability in surface finish quality . Consequently, optimal machining conditions vary significantly with tool wear progression . Future work should adopt a forward-looking time-series process optimization approach that accounts the evolving tool wear. When combined with the Adjusted-Parameter-Machining (APM) approach , this strategy enables dynamic adaptation of optimal machining parameters based on the tool wear state. Also, as summarized in Table , the optimization objectives are limited to machining quality and efficiency, while critical environmental aspects such as power consumption, carbon dioxide emissions and waste generation are often overlooked. These factors are essential for advancing sustainability and achieving net-zero goals in the CFRP manufacturing process, which should be considered in future work.