In this article, the effect of abrasive types on the abrasive flow machining process was investigated. Four groups of abrasive media were prepared with different types of abrasives: SiC, AL₂O₃, B₄C and Garnet. An experimental study was performed on DIN 1.2379 tool steel. The specimens were cut using wire electrical discharge machining and finished with the abrasive flow machining process. The results show that the white layer that formed during wire electrical discharge machining was successfully removed by abrasive flow machining in a few cycles. Although the surface roughness improves with similar trends for all media groups, the results show that the media prepared with B₄C and SiC have more surface improvement than the Al₂O₃ and Garnet ones. The resulting average surface roughness (R_a) values are comparable to the surface quality of those obtained from lapping and super-finishing. The material removal is directly related to the hardness of the abrasive.

This study investigates the effects of electro-discharge parameters on the surface integrity in small-hole drilling. Discharge current, pulse on-time and pulse off-time were chosen as variable parameters. The gap voltage, dielectric flushing pressure, electrode rotation, electrode polarity and capacitance were taken as constant. Material removal rate, surface roughness, average white layer thickness, overcut and taper were taken as outputs for this study. Experiments were performed on DIN 1.2080 (X210Cr12) cold working tool steel, and ∅2 mm single-hole brass electrodes were used. The experimental results indicate that a compromise solution is required in selection of the electrical discharge machining parameters (i.e. discharge current, pulse on-time and pulse off-time) to maintain machining stability (melting–evaporation and removal of debris). As the stability is maintained during the process, dimensional accuracy increases and a better finish can be obtained with a reasonable machining time.

Abrasive flow machining (AFM) is a manufacturing technique that uses the flow of a pressurized abrasive media to remove workpiece material. In comparison with other polishing technique, AFM is very efficient, suitable for the finishing of complex inner surfaces. In this paper, the effect of workpiece hardness on the AFM process has been investigated. An experimental study was carried out on AISI D2 tool steel hardened to 31, 45 and 55 HRC. The specimens were cut by using wire electro discharge machining (WEDM) and then finished with AFM. The results show that the white layer formed during WEDM is successfully removed by AFM in a few cycles. Although the surface quality is improved by AFM for all hardness groups, the results show that harder materials have more surface improvement than the softer ones

Selection of appropriate operating conditions is an important attribute to pay attention for in electrical discharge machining (EDM) of steel parts. The achievement of EDM process is affected by many input parameters; therefore, the computational relations between the output responses and controllable input parameters must be known. However, the proper selection of these parameters is a complex task and it is generally made with the help of sophisticated numerical models. This study investigates the capacity of Adaptive Nero-Fuzzy Inference System (ANFIS), genetic expression programming (GEP) and artificial neural networks (ANN) in the prediction of EDM performance parameters. The datasets used in modelling study were taken from experimental study. According to the results of estimating the parameters of all models in the comparison in terms of statistical performance is sufficient, but observed that ANFIS model is slightly better than the other models.

In the present study, V-shaped arc welding of the AISI 1045 steel is modeled by using 2D Finite Element Model (FEM). The temperature distribution, microstructure, grain growth, and the hardness of the heat-affected zone (HAZ) of the welding are simulated. The experimental work is carried out to validate the FE model. The very close agreement between the simulation and experimental results show that the FE model is very effective for predicting the microstructure, the phase transformation, the grain growth and the hardness. The effect of preheat temperature on the martensite formation is analysed, and it is shown that 225 °C preheating completely eliminates the martensite formations for the 12 mm thick plate.