The influence of plasma gas on changes in chemical composition and structures during the cutting process

Cutting is usually one of initial and basic operations of the manufacturing process of welded structures and realization constructions elements. Thermal cutting, in particular plasma arc cutting is often used to prepare elements. The plasma arc cutting process involves melting and ejecting the liquid metal from the cutting gap with a highly concentrated plasma electric arc which is generated between the non-consuable electrode and the workpiece. The paper presents the results of research on the influence of plasma gas on structural changes and chemical compositions changes resulting unalloyed steel cutting by air plasma arc. It was shown that in the air plasma arc cutting process the amorphous layer with a very high nitrogen content (about 1.6%) and a hardness of 750 HV 0.2 was used. This high nitriding effect is due to the diffusion of nitrogen from the plasma gas. As a result of the interaction of air plasma arc gases on the liquid metal, the cutting surface is carburized (about 0.5%). The alloy components are also burnt according to the theory of selective oxidation of chemical elements. The material structure after the air plasma cutting process shows the structures between the structure formed after oxygen cutting processs and nitrogen plasma cutting process. The process of argon-hydrogen plasma cutting has the least influence on the cut material.

Comparison of laser beam, oxygen and plasma arc cutting methods in terms of their advantages and disadvantages in cutting structural steels

The laser beam, plasma arc, and oxygen cutting methods are widely used in metal cutting processes. These methods are quite different from each other in terms of initial setup cost and cutting success. A powered laser beam is used in laser beam cutting, plasma is used in plasma arc cutting, flammable gas - oxygen mixture is used in the oxygen cutting method. In this study, the cutting success of these methods was investigated on tensile specimens. Microstructure, hardness (HV 0.1), surface roughness, and strengths were investigated after the cutting process. The tensile test implemented with tensile samples cut from the same material by these three methods, it was observed that the strength values of the samples changed by about 8% in tensile strength depending on the cutting process. The hardness of the cut surfaces in plasma arc cutting increased from 150 HV to 230 HV for S235JR material. For this reason, it is difficult to perform machining operations after plasma cutting. The hardness value reached after laser beam cutting is 185 HV. Plasma arc cutting is more cost-effective than laser beam cutting. 1-3° vertical inclination (conicity) occurs on the cut surface in plasma arc cutting, while this inclination almost does not occur in laser cutting. In plasma cutting benches, cutting is done with oxygen, and in cutting with oxygen, the taper is seen in a small amount.

A Hybrid Approach of ANFIS—Artificial Bee Colony Algorithm for Intelligent Modeling and Optimization of Plasma Arc Cutting on Monel™ 400 Alloy

This paper focusses on a hybrid approach based on genetic algorithm (GA) and an adaptive neuro fuzzy inference system (ANFIS) for modeling the correlation between plasma arc cutting (PAC) parameters and the response characteristics of machined Monel 400 alloy sheets. PAC experiments are performed based on box-behnken design methodology by considering cutting speed, gas pressure, arc current, and stand-off distance as input parameters, and surface roughness (Ra), kerf width (kw), and micro hardness (mh) as response characteristics. GA is efficaciously utilized as the training algorithm to optimize the ANFIS parameters. The training, testing errors, and statistical validation parameter results indicated that the ANFIS learned by GA outperforms in the forecasting of PAC responses compared with the results of multiple linear regression models. Besides that, to obtain the optimal combination PAC parameters, multi-response optimization was performed using a trained ANFIS network coupled with an artificial bee colony algorithm (ABC). The superlative responses, such as Ra of 1.5387 µm, kw of 1.2034 mm, and mh of 176.08, are used to forecast the optimum cutting conditions, such as a cutting speed of 2330.39 mm/min, gas pressure of 3.84 bar, arc current of 45 A, and stand-off distance of 2.01 mm, respectively. Furthermore, the ABC predicted results are validated by conducting confirmatory experiments, and it was found that the error between the predicted and the actual results are lower than 6.38%, indicating the adoptability of the proposed ABC in optimizing real-world complex machining processes.

An Experimental Analysis of Cutting Quality in Plasma Arc Machining

Plasma arc cutting (PAC) is an unconventional process widely used in manufacturing of heavy plate products. This work reports on the research results of machining quality of the workpiece in the plasma arc cutting on the low carbon low alloy steel. An experimental investigation of the characteristics of machining accuracy and surface integrity was carried out for basic machining parameters (cutting speed, arc current, arc voltage, plasma gas pressure, stand-off distance and nozzle diameter). The kerf geometry was determined with three accuracy parameters (top kerf width, bottom kerf width and kerf taper angle). The parameters of deviation present due to plasma curvature were defined by drag and pitch of drag line. The surface roughness was determined with two main roughness parameters through scanning the surface topography (roughness average and maximum height of the profile). The surface properties were determined over microstructure in heat affected zone (HAZ). The results show an acceptable machining quality of the PAC, so that this process is an excellent choice for fast and efficient material removal. However, the plasma arc cutting is not suitable for the final machining because of the metallurgical variations in the HAZ.

Prediction and Analysis of Multi-Response Characteristics on Plasma Arc Cutting of Monel 400™ Alloy Using Mamdani-Fuzzy Logic System and Sensitivity Analysis

Nickel-based alloys, especially Monel 400™, is gaining its significance in diverse applications owing to its superior mechanical properties and high corrosion resistance. Machining of these materials is extremely difficult through the traditional manufacturing process because of their affinity to rapid work hardening and deprived thermal conductivity. Owing to these difficulties a well-established disruptive metal cutting process namely plasma arc cutting (PAC) can be widely used to cut the sheet metals with intricate profiles. The present work focuses on an intelligent modeling of the PAC process and investigation on the multi-quality characteristics of PAC parameters using the fuzzy logic approach. The Box-Behnken response surface methodology is incorporated to design and conduct the experiments, and to establish the relationship between PAC parameters such as cutting speed, gas pressure, arc current, and stand-off distance and responses which include the material removal rate (MRR), kerf taper (KT), and heat affected zone (HAZ). The quadratic regression models are developed and their performances are assessed using the analysis of variance (ANOVA). Fuzzy set theory-based models are formulated to predict various responses using the Mamdani approach. Fuzzy logic and regression results are compared with the experimental data. A comparative evaluation predicted an average error of 0.04% for MRR, 0.48% for KT, and 0.46% for HAZ, respectively. The effect of variations in PAC process parameters on selected responses are estimated through performing the sensitivity analysis.