In this work, the performance of rapid prototyping (RP) based rapid tool is investigated during electrical discharge machining (EDM) of titanium as work piece using EDM 30 oil as dielectric medium. Selective laser sintering, a RP technique, is used to produce the tool electrode made of AlSi10Mg. The performance of rapid tool is compared with conventional solid copper and graphite tool electrodes. The machining performance measures considered in this study are material removal rate, tool wear rate and surface integrity of the machined surface measured in terms of average surface roughness (Ra), white layer thickness, surface crack density and micro-hardness on white layer. Since the machining process is a complex one, potentiality of application of a predictive tool such as least square support vector machine has been explored to provide guidelines for the practitioners to predict various machining performance measures before actual machining. The predictive model is said to be robust one as root mean square error in the range of 0.11–0.34 is obtained for various performance measures. A hybrid optimization technique known as desirability based grey relational analysis in combination with firefly algorithm is adopted for simultaneously optimizing the performance measures. It is observed that peak current and tool type are the significant parameters influencing all the performance measures.

在这项工作中，在以EDM 30油为电介质的钛为工件的放电加工（EDM）期间，研究了基于快速原型（RP）的快速工具的性能。选择性激光烧结，一种RP技术，用于生产由AlSi10Mg制成的工具电极。快速工具的性能与传统的固态铜和石墨工具电极进行了比较。本研究中考虑的加工性能指标是材料去除率，工具磨损率和加工表面的表面完整性，以平均表面粗糙度（Ra），白层厚度，表面裂纹密度和白层的显微硬度来衡量。由于加工过程很复杂，已经探索了诸如最小二乘支持向量机之类的预测工具的应用潜力，以为从业人员在实际加工之前预测各种加工性能指标提供指导。预测模型被认为是稳健的，因为针对各种性能指标获得的均方根误差在0.11-0.34范围内。同时采用一种称为基于期望度的灰色关联分析的混合优化技术与萤火虫算法相结合来同时优化性能指标。可以看出，峰值电流和工具类型是影响所有性能指标的重要参数。预测模型被认为是稳健的，因为针对各种性能指标获得的均方根误差在0.11-0.34范围内。同时采用一种称为基于期望度的灰色关联分析的混合优化技术与萤火虫算法相结合来同时优化性能指标。可以看出，峰值电流和工具类型是影响所有性能指标的重要参数。预测模型被认为是稳健的，因为针对各种性能指标获得的均方根误差在0.11-0.34范围内。同时采用一种称为基于期望度的灰色关联分析的混合优化技术与萤火虫算法相结合来同时优化性能指标。可以看出，峰值电流和工具类型是影响所有性能指标的重要参数。