Powder mixed electro discharge machining (PMEDM) is a hybrid machining process where the electrically conductive powder is mixed into the dielectric fluid to enhance the machining efficiency. In this investigation, PMEDM is performed for the machining of AISI 304 stainless steel when silicon carbide powder is mixed into the kerosene dielectric. Peak current, pulse on time, gap voltage, duty cycle and powder concentration are considered as process parameter while material removal rate (MRR), tool wear rate (TWR) and surface roughness (Ra) are considered as response. A face centered central composite design (FCCCD) based response surface methodology (RSM) is applied to design the experiment. A hybrid optimization technique like desirability coupled with fuzzy-logic method is performed to get the optimum level of the multiple performance characteristics. Analysis of variance (ANOVA) is performed for the statistical analysis. The result shows that peak current is the most significant parameter for MRR, TWR and Ra. The optimal setting for maximum MRR, minimum TWR and Ra have been obtained by desirability coupled with fuzzy-logic method.
Electrical Discharge Machining (EDM) is one of the most basic non-conventional machining processes for production of complex geometries and process of hard materials, which are difficult to machine by conventional process. It is capable of machining geometrically complex or hard material components, that are precise and difficult-to-machine such as heat-treated tool steels, composites, super alloys, ceramics, carbides, heat resistant steels etc. The present study is focusing on the die sinking electric discharge machining (EDM) of AISI H 13, W.-Nr. 1.2344 Grade: Ovar Supreme for finding out the effect of machining parameters such as discharge current (GI), pulse on time (POT), pulse off time (POF) and spark gap (SG) on performance response like Material removal rate (MRR), Surface Roughness (Ra) & Overcut (OC) using Square-shaped Cu tool with Lateral flushing. A well-designed experimental scheme is used to reduce the total number of experiments. Parts of the experiment are conducted with the L9 orthogonal array based on the Taguchi methodology and significant process parameters are identified using Analysis of Variance (ANOVA). It is found that MRR is affected by gap current & Ra is affected by pulse on time. Moreover, the signal-to-noise ratios associated with the observed values in the experiments are determined by which factor is most affected by the responses of MRR, Ra and OC. These experimental data are further investigated using Grey Relational Analysis to optimize multiple performances in which different levels combination of the factors are ranked based on grey relational grade. The analysis reveals that substantial improvement in machining performance takes place following this technique.
This paper presents on experimental investigation of the machining characteristics of different grades of EN materials in CNC turning process using TiN coated cutting tools. In machining operation, the quality of surface finish is an important requirement for many turned work pieces. Thus, the choice of optimized cutting parameters is very important for controlling the required surface quality. The purpose of this research paper is focused on the analysis of optimum cutting conditions to get the lowest surface roughness and maximum material removal rate in CNC turning of different grades of EN materials by Taguchi method. Optimal cutting parameters for each performance measure were obtained employing Taguchi techniques. The orthogonal array, signal to noise ratio and analysis of variance were employed to study the performance characteristics in dry turning operation. ANOVA has shown that the depth of cut has significant role to play in producing higher MRR and insert has significant role to play for producing lower surface roughness. Thus, it is possible to increase machine utilization and decrease production cost in an automated manufacturing environment.