This study aimed to develop a regression-based model for predicting tool life in manufacturing environments, with goals of enhancing productivity and reducing costs. In machining operations, particularly roughing processes, high cutting forces can accelerate tool wear, often leading to process interruptions and increased defect rates. Previous research on tool life prediction has frequently relied on empirical models and statistical methods, which face limitations in reliability across diverse machining conditions. To address this issue, we proposed a data-driven approach that could collects tool wear data under varying machining conditions (such as cutting speed, feed rate, and depth of cut) and applied regression models to predict tool life effectively. The model’s performance was validated under multiple conditions to assess its predictive accuracy. This study offers a practical tool life management solution for manufacturing settings, optimizing tool usage and enhancing operational efficiency.

The directed energy deposition (DED) process has been used for enhancement of the mechanical property, repair, and part manufacturing. Post-process machining is required due to the low quality of the DED printed part. Even if the part is printed under similar conditions, dimensional variations occur frequently due to the accumulation of small printing errors. Due to tool overfeeding and the occurrence of the non-cutting area due to this variation, the quality of the finished part is not guaranteed. Therefore, the post-process machining should be carried out considering the actual printed part shape. Herein, the flexible post-process machining is proposed by utilizing the shape information through the on-machine measurement (OMM) of DED printed parts. The process margin for machining the design shape is calculated through the OMM of the geometric dimension of the printed part. Feedrate (Override) and machining path of each printing parts are flexibly determined depending on the process margin. This technique is applied to the pocket shape part printed with STS 316L material, and the rough and finish machining conditions are established. Rough machining time was reduced by adjusting the feedrate flexibly. The final form of accuracy and surface roughness were achieved under 30 and 0.25 μm, respectively.