Electrochemical honing (ECH) combines the high removal characteristics of ECD and MA of conventional honing. The process has much higher removal rates than either conventional honing or internal cylindrical grinding. In ECH the cathodic tool is similar to the conventional honing tool, with several rows of small holes to enable the electrolyte to be introduced directly to the interelectrode gap. The electrolyte provides electrons through the ionization process, acts as a coolant, and flushes away chips that are sheared off by MA and metal sludge that results from ECD action. The majority of material is removed by the ECD phase, while the abrading stones remove enough metal to generate a round, straight, geometrically true cylinder. During machining, the MA removes the surface oxides that are formed on the work surface by the dissolution process. The removal of such oxides enhances further the ECD phase as it presents a fresh surface for further electrolytic dissolution. Sodium nitrate solution (240 g/L) is used instead of the more corrosive sodium chloride (120 g/L) or acid electrolytes. An electrolyte temperature of 38°C, pressure of 1000 kPa, and flow rate of 95 L/min can be used. ECH employs dc current at a gap voltage of 6 to 30 V, which ensures a current density of 465 A/cm2 [Randlett et al. (1968)]. Improper electrolyte distribution in the machining gap may lead to geometrical errors in the produced bore.
(Advanced Machining Processes, Hassan El-Hofy, Page 189)
Electrochemical Honing (New) (Finishing process)
Electro chemical honing(ECH) is a hybrid electrolytic precision microfinishing technology that inteegrates physicochemical actions of the and conventional honing processes to provide controlled functional surface-generation and fast material removal capabilities in a single operation. Dubey[75] presented a Taguchi loss function-based hybrid strategy for the multi- performance optimization of electrochemical honing process. The proposed strategy utilizes a radial basis function neural network (RBFNN) for the process parametric mapping with the loss functions of ECH multi-performance characteristics determined through a Taguchi matrix robust experimental design. The network outputs were then unified using desirability function approach to provide an objective function to genetic algorithm (GA). Finally, GA predicts the optimal process parametric settings for multi-performance optimization of ECH.
(By R. Venkata Rao, Advanced Modeling and Optimization of Manufacturing Processes, p.152)
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