Newly developed D2 steel is widely used for various advanced engineering applications. Machining of D2 steel to obtain desired quality responses has immense importance for the effective utilization of these materials for advanced industrial applications like aerospace, marine, automobile, etc. Wire electrical discharge machining (WEDM) is used to machine difficult to machine materials and to produce sophisticated features with better dimensional accuracy. Obtaining the fine surface roughness in WEDM has highly depends on correct selection of process parameters. In the present work, experimental investigation was planned to study the effects of WEDM input parameters on surface roughness (Ra) of D2 steel. Experimental runs were conducted by using L16 orthogonal array of Taguchi method. The analysis of variance was employed to determine the influences of process parameters on Ra. Response surface methodology (RSM) and cuckoo search optimization (CSO) algorithm had been used to model and optimize the surface roughness. From the study, it was found that Ra value had improved as compared to initial experimental runs.
Surface roughness and material removal rate (MRR) are important technological parameters which describe quality of machined surfaces and productivity of machining process. Surface roughness and MRR are significantly influenced by many interactive process parameters dynamically but those are difficult to quantify adequately in any machining process. Wire electrical discharge machining (WEDM) is one of the advanced machining processes which used thin wire as cutting tool for creating intricate features on machined parts. In WEDM, optimizing surface roughness(Ra) and material removal rate (MRR) combinedly by controlling process variables namely pulse on time, pulse off time, wire feed, voltage gap, etc, is difficult task and much needed area of research. In the present work, investigation is made to study and optimize the surface roughness of H13 steel in WEDM. L16 orthogonal array of Taguchi methodology has been used to conduct the experiments. Analysis of variance (ANOVA) has been applied on experimental data to determine the significance of input parameters on surface roughness and MRR. Mathematical relationships are developed to correlate the machining parameters and output responses: surface roughness and MRR. Contour plots have been drawn to illustrate the combined effects of process parameters on output responses. Multi-objective jaya optimization algorithm (MJOA) applied on developed mathematical equations to predict the multi-responses simultaneously. From the study, it is stated that hybridTaguchi method, RSM and MJOA is useful for studying, modeling and optimizing the multiple responses: surface roughness and MRR, simultaneously in WEDM of H13 steel.
The present work is planned to optimize the material removal rate (MRR) in wire electrical discharge machining (WEDM) of D2 tool steel. Machining parameters namely pulse on time, pulse off-time, voltage gap and wire feed have been chosen for the present investigation to perform experimental runs. The experiments have been conducted using L16orthogonal array of Taguchi method. The effects of machining parameters on MRR has been determined by utilizing signal-to-noise ratio and graphical main affects plot. The optimum parametric condition has been acquired for looking for maximizing MRR by using Taguchi method. Confirmatory experiments have also been conducted to verify the predicted parametric setting. From the present investigation, it is found that Taguchi method is robust to study, analyze and optimize processing conditions in WEDM.
In the present investigation, a new hybrid technique response surface methodology (RSM) based simulated annealing (SA) algorithm approach is proposed to predict surface roughness for stainless steel material in traverse cut cylindrical grinding process. Experiments are designed as per full factorial design, wherein infeed, longitudinal feed and work speed have been considered as the important input parameters. Analysis of variance and graphical main and interaction plots have been plotted for analyzing the experimental data for identifying the relationships between grinding parameters and surface roughness. The variation of the performance parameter (surface roughness) with grinding parameters has been mathematically modeled by RSM. SA algorithm has been employed for solving the obtained mathematical model. Finally, the validation exercise is performed with optimum levels of grinding parameters. The results confirm the efficiency of the approach employed for prediction of surface roughness in this study.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.