Increasing the etching rate is one of the main optimization targets in the chemical machining (CM). Traditionally, this target is fulfilled by some costly techniques like selecting stronger etchants and increasing the etchant concentration. Also, other methods like increasing the etchant temperature and stirring the etchants by agitators are employed for increasing the etching rate.One of the advantages of these methods is reduction of the consumption of acidic etchants which results in the cost reduction and making an eco-friendley process.In this article, a systematic experimental study is performed on vibration-assisted CM of copper. In this technique, the workpiece vibrates in the etchant during the CM. For evaluating the performance of machining, effects of amplitude and frequency of vibrations, along with the temperature and concentration of acidic etchant, on material removal rate, surface roughness and machining undercut are studied experimentally. The experiments are designed by Central Composite Design (CCD) in Response Surface Methodology (RSM). Also, multi-objective optimization is performed by defining a desirability function. The optimal vibroassisted process parameters are temperature 60 ̊C, etchant concentration 600 g/l, vibration frequency 25 Hz, and vibration amplitude 1.5 mm, to get optimal outputs on the desired parameters.
Obtaining the required surface nish and geometric accuracy, together with attaining a high production rate, is a challenge in nishing the inner surfaces of steel pipes and bushes. One of the promising techniques for the reduction of the surface roughness of metal parts is electrochemical machining. In this paper, the roughness and dimensional inaccuracy of the internal surface of a CK45 steel bush were controlled electrochemically. For this, a novel electrochemical nishing setup was constructed. The e ect of electric potential di erence along with temperature, ow rate, and concentration of electrolyte on the process outputs, including the material removal rate, surface roughness, and dimensional accuracy, were investigated. The Box-Behnken Design was utilized to design the empirical experiments. Analysis of variance was performed to validate the experimental models. Also, multi-objective optimization was implemented using response surface methodology to achieve a predetermined level of surface roughness and dimensional accuracy, along with maximizing the material removal rate.
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