Study on Generating Machining Performance of Two-Dimensional Ultrasonic Vibration-Composited Electrolysis/Electro-Discharge Technology for MMCs

Ultrasonic vibration-composited electrolysis/electro-discharge machining technology (UE/DM) is effective for machining particulate-reinforced metal matrix composites (MMCs). However, the vibration of the tool or workpiece suitable for holes limits the application of UE/DM. To improve the generating machining efficiency and quality of flat and curved surfaces, in this study, we implemented two-dimensional ultrasonic vibration into UE/DM and constructed a novel method named two-dimensional ultrasonic vibration-composited electrolysis/electro-discharge machining (2UE/DM). The influence of vibration on the performance of 2UE/DM compared to other process technologies was studied, and an orthogonal experiment was designed to optimize the parameters. The results indicated that the materiel remove rate (MRR) mainly increased via voltage and tool vibration. The change current was responsible for the MRR in the process. Spindle speed and workpiece vibration were not dominant factors affecting the MRR; the spindle speed and tool and workpiece vibration, which reduced the height difference between a ridge and crater caused by abrasive grinding, were responsible for surface roughness (Ra) and form precision (δ). Additionally, the optimized parameters of 1000 rpm, 3 V, and 5 um were conducted on MMCs of 40 SiCp/Al and achieved the maximum MRR and minimum Ra and δ of 0.76 mm3/min, 3.35 um, and 5.84%, respectively. This study’s findings provide valuable process parameters for improving machining efficiency and quality for MMCs of 2UE/DM.

Identification of Optimal Process Parameters in Electro-Discharge Machining Using ANN and PSO

Electrical Discharge Machining (EDM) process is a widely used machining process in several fabrication, construction and repair work applications. Considering Pulse-On Time, Pulse OFF time, Peak-Current and Gap voltage as the inputs and among all possible outputs, in the present work Material Removal Rate and Surface Roughness are considered as outputs. In order to reduce the number of experiments Design of Experiments (DOE) was undertaken using Orthogonal Array and later on the outputs were optimized using ANN and PSO. It was found that the results obtained from both the techniques were tallying with each other.

Multiresponse Particle Swarm Optimization of Wire-Electro-Discharge Machining Parameters of Nitinol Alloys

The conventional process of machining of nitinol alloy which possesses excess strain hardening and low thermal conductivity makes a complex process that leads to extensive wear on the tool and inadequate surface quality. Wire-electro-discharge machining (WEDM) is widely accepted for machining this alloy involving various input factors, namely, P (pulse-on-duration), Q (pulse-off-duration), C, (maximum-current), and V (voltage). Using the PSO (particle swarm optimization) method, the effect of WEDM process factors on multiresponses such as MRR (metal removal rate) and SR (surface roughness) has been investigated. ANOVA was used to create a relationship model between input factors and response characteristics, which was then optimized using response surface methods (RSM). ANOVA revealed that variables A and C are the most significant. When investigated individually, the influence of WEDM process parameters on SR and MRR is contradictory, as no response provides the best process quality. To find the optimal ideal condition for decreasing SR and maximizing MRR, the MOOPSO approach was used. P = 25.47051 μs, Q = 10.84998 μs, C = 2.026317 A, and V = 49.50757 volts were used to calculate the optimal universal solution for machining characteristics (MRRmax = 3.536791 mm3/min and SRmin = 1.822372 μm). PSO enhanced MRR and SR for every optimal combination of variables, according to the findings. Based on the findings, a wide range of optimal settings for achieving maximum MRR and minimum SR are given, depending on the product requirements.

Study of Machine Characteristics During Rotary EDM Machining using Tools Made of Aluminium Alloy 6061

Electro discharge machining (EDM) is an unconventional machining process that uses spark energy to remove material from the workpiece. EDM process is extensively being used in die/mould making industries, automobile industry, aerospace industry etc. for generating complex and intricate shape on hard material. Numbers of research works have been carried out using EDM process in order to improve the performance. Further, different variant of EDM process like dry EDM, orbital EDM, powder mixed EDM, ultrasonic assisted EDM and rotary EDM etc. have also been studied. Rotary EDM process is very promising process that helps to improve surface finish, overcut as well as out of roundness. In the present paper, a extensive review has been presented on rotary EDM process. Significance of several input parameters has been observed on output characteristics like overcut and out of roundness.

A Comparative Study of Linear, Random Forest and AdaBoost Regressions for Modeling Non-Traditional Machining

Non-traditional machining (NTM) has gained significant attention in the last decade due to its ability to machine conventionally hard-to-machine materials. However, NTMs suffer from several disadvantages such as higher initial cost, lower material removal rate, more power consumption, etc. NTMs involve several process parameters, the appropriate tweaking of which is necessary to obtain economical and suitable results. However, the costly and time-consuming nature of the NTMs makes it a tedious and expensive task to manually investigate the appropriate process parameters. The NTM process parameters and responses are often not linearly related and thus, conventional statistical tools might not be enough to derive functional knowledge. Thus, in this paper, three popular machine learning (ML) methods (viz. linear regression, random forest regression and AdaBoost regression) are employed to develop predictive models for NTM processes. By considering two high-fidelity datasets from the literature on electro-discharge machining and wire electro-discharge machining, case studies are shown in the paper for the effectiveness of the ML methods. Linear regression is observed to be insufficient in accurately mapping the complex relationship between the process parameters and responses. Both random forest regression and AdaBoost regression are found to be suitable for predictive modelling of NTMs. However, AdaBoost regression is recommended as it is found to be insensitive to the number of regressors and thus is more readily deployable.

Improving the Efficiency of Electrical Discharge Machining of Special-Purpose Products with Composite Electrode Tools

The article is devoted to increasing the efficiency of electrical discharge machining of special-purpose items with composite electrode tools. The subject of research is the parameter of the roughness of the processed surface and the work of the electro-discharge machining (EDM) of 40Crsteel in various modes of electrical discharge machining. The aim of the work is to increase the efficiency of the process of copy-piercing electrical discharge machining of parts introduced into the composition of a special-purpose product and the use of electrode tools with the introduction of 20% graphite. Experimental studies were carried out using the method of a full factorial experiment with a subsequent regression analysis. The experiments were carried out using a copy-piercing Smart CNC EDM machine, a tool electrode, and a profile composite electrode. Empirical dependencies were established, reflecting the relationship between the processing modes, productivity, and surface roughness parameter after processing. A theoretical model for calculating the roughness parameter was developed, which makes it possible to predict the quality of the processed surface with a reliability of 10%–15%. To ensure the required ratios of the quality of the processed surface at the maximum performance indicators, technological recommendations were obtained, as a result of which a 35% reduction in machine time was achieved when processing the “screw” part with the required indicators of surface quality.