Search

Ti-6Al-4V is an alloy that has a high strength-to-weight ratio. It is known as an alpha-beta titanium alloy with excellent corrosion resistance. This alloy has a wide range of applications, e.g., in the aerospace and biomedical industries. Examples of alpha stabilizers are aluminum, oxygen, nitrogen, and carbon, which are added to titanium. Examples of beta stabilizers are titanium–iron, titanium–chromium, and titanium–manganese. Despite the exceptional properties, the processing of this titanium alloy is challenging when using conventional methods as it is quite a hard and tough material. Nonconventional methods are required to create intricate and complex geometries, which are difficult with the traditional methods. The present study focused on machining Ti-6Al-4V using wire electrical discharge machining (WEDM) and conducting numerous experiments to establish the machining parameters. The optimal setting of the machining parameters was predicted using a multiresponse optimization technique. Experiments were planned using the response surface methodology (RSM) technique and analysis of variance (ANOVA) was used to determine the significance and contribution of the input parameters to changes in the output characteristics (cutting speed and surface roughness). The cutting speed obtained during the processing of the annealed titanium alloy using WEDM was quite large as compared to the cutting speed obtained in the case of processing the pure, quenched, and hardened titanium alloys using WEDM. The maximum cutting speed obtained while processing the annealed titanium alloy was 1.75 mm/min.

In the present work, a copper (Cu) tool, reinforced with titanium carbide (TiC) particles, synthesized using powder metallurgy, is used to machine EN31 die steel using EDM machining. Various parameters were selected as process parameters to study tool shape as response. RSM was used to design the experiments and 52 experiments performed. The shape of tool was studied in terms of roundness of tool. Change in roundness of tool was analysed by analysis of variance and regression. Results revealed current and pulse on/off most significant parameters. Roundness value rose with increase in discharge current. On the other hand, it fell with rise in time of pulse on/off. Profile of tool and shape of tool were investigated by scanning electron microscopy (SEM). Comparison of Cu-TiC with Cu tooltip was performed to understand the effect cermet tooltip. The comparison of results showed that Cu–TiC tool was reduced out of roundness by 25%.

Surface roughness of the finished part and profile of the tool electrode are significant factors to assess the functionality of electrical discharge machining process. In this study, EDM was utilized for the machining of hardened EN31 steel. A sintered cermet tool tip with 75% copper–25% titanium carbide was fabricated and used as tool electrode. A data set of 262 such samples was developed with machining variables including discharge current (Ip), gap voltage (Vg), pulse on time (Ton), pulse off time (Toff) and flushing pressure (P). By correlating the machining variables, a machine learning-based regression model was developed for the prediction of surface roughness of the machined surface and change in out-of-roundness of tool during the EDM process. With the help of heat maps and a probability table, it was found that Ip, Ton, Toff and P had significant effect on SR, and Ip, Ton and Toff affected OOR. The machine learning-based regression equation predicted SR with average error of 1.6% and OOR with average error of 0.48%. It was found that machine learning-based regression equation had better accuracy as compared to a DOE-based regression equation.

In the electrical discharge machining (EDM) process, especially during the machining of hardened steels, changes in tool shape have been identified as one of the major problems. To understand the aforesaid dilemma, an initiative was undertaken through this experimental study. To assess the distortion in tool shape that occurs during the machining of EN31 tool steel, variations in tool shape were examined by monitoring the roundness of the tooltip before and after machining with a coordinate measuring machine. The change in out-of-roundness of the tooltip varied from 5.65 to 37.8 µm during machining under different experimental conditions. It was revealed that the input current, the pulse on time, and the pulse off time had most significant effect in terms of changes in the out-of-roundness values during machining. Machine learning techniques (decision tree, random forest, generalized linear model, and neural network) were applied for the prediction of changes in tool shape. It was observed that the results predicted by the random forest technique were more convincing. Subsequently, it was gathered from this examination that the usage of the random forest technique for the prediction of changes in tool shape yielded propitious outcomes, with high accuracy (93.67%), correlation (0.97), coefficient of determination (0.94), and mean absolute error (1.65 µm) values. Hence, it was inferred that the random forest technique provided better results in terms of the prediction of tool shape.

In electrical discharge machining (EDM), the machined surface quality can be affected by the excessive temperature generation during the machining process. To achieve a longer life of the finished part, the machined surface quality plays a key role in maintaining its overall integrity. Surface roughness is an important quality evaluation of a material’s surface that has considerable influence on mechanical performance of the material. Herein, a sintered cermet tooltip with 75% copper and 25% titanium carbide was used as tool electrode for processing H13 steel. The experiments have been performed to investigate the effects of EDM parameters on the machined surface roughness. The findings show that, as the pulse current, pulse length, and pulse interval are increased, the surface roughness tends to rise. The most significant determinant for surface roughness was found to be pulse current. A semi-empirical surface roughness model was created using the characteristics of the EDM technique. Buckingham’s theorem was used to develop a semi-empirical surface roughness prediction model. The semi-empirical model’s predictions were in good agreement with the experimental studies, and the built empirical model based on physical features of the cermet tooltip was tested using dimensional analysis.

Electrode wear is a major problem in electrical discharge machining process. Due to electrode wear during machining, the required geometrical dimensions and form of the electrode are not reproduced on the workpiece. In the present study, change in shape of the copper–titanium carbide tooltip during the electrical discharge machining of hardened EN31 steel workpiece has been carried out at different processing conditions. The process parameters selected for the study are peak current, pulse on time, pulse off time, gap voltage and dielectric flushing pressure. Buckingham’s dimensional analysis has been used to model the change in shape, and the required coefficients are calculated based on the experimental data. The physical and electrical properties chosen for the study are density of the material, thermal conductivity, hardness, electrical resistivity and linear thermal expansion coefficient. The developed empirical model based on dimensional analysis has been validated and was found to be in good agreement with experimental findings.