Desirability Function Analysis (DFA) in Multiple Responses Optimization of Abrasive Water Jet Cutting Process

This paper introduces optimization of machining parameters for high-pressure abrasive water jet cutting of Hardox 500 steel utilizing desirability function analysis (DFA). The tests were carried out according to the orthogonal matrix (Taguchi) L9. The control parameters of the process such as pressure, abrasive flow rate, and traverse speed was optimized under multi-response conditions namely cutting depth and surface roughness. The optimal set of control parameters was established on the basis of the composite desirability value obtained from desirability function analysis and the significance of these parameters was determined by analysis of variance (ANOVA). The effects show that optimal sets for high cutting depth and small surface roughness is high pressure, middle abrasive flow rate, and small traverse speed. A confirmation test was also leaded to validate the test results. Results of the research have shown that machining efficiency at keeping good level quality of cut surface can be improved this approach.

Machinability study of Aluminium 6082 reinforced with boron carbide and titanium by stir casting method using Abrasive water jet machining process

Aluminium Metal Matrix Composite (AMMC) has broad uses in the medical, aerospace, and automobile industries, which have long sought lightweight materials with superior designs and improved properties to improve performance. This analysis has aimed to prepare an AMMC to investigate its machining and mechanical properties. The AMMC is produced using a stir casting process by reinforcing boron carbide and titanium with aluminium 6082. The material's mechanical properties are studied by using wear test, hardness test, and corrosion test. The wear rate increases when the load increases by varying the load and time with speed as a constant. It is found that the hardness of a material is increased due to titanium and boron carbide as the reinforcement particle in the fabricated AMMC. Using the pitting corrosion technique, the corrosion occurs on the AMMC under the estimated time at room temperature. In order to illustrate the machining characteristics of the aluminium metal matrix composite, an abrasive water jet machining process has been used. The experiments use L9 orthogonal Array using Taguchi's method and ANOVA analysis. The input parameters considered are Traverse rate, Stand-off distance, and Nozzle diameter. To find the optimum value of circularity, cylindricity, and surface roughness by varied input parameters. The respective graphs are also plotted. Scanning electron microscopic analysis was performed on the wear-tested specimen and machining surface of the material to determine the distribution of reinforced material and investigate the material's fracture mechanism. It is found that wear tracks, voids, delamination, micro pits, embedded garnet abrasive particles are located on the machined surface of the AMMC.

MODELING AND MULTI-RESPONSE OPTIMIZATION OF ABRASIVE WATER JET MACHINING USING ANN COUPLED WITH NSGA-II

This paper aims to develop a predictive model and optimize the performance of the abrasive water jet machining (AWJM) during machining of carbon fiber-reinforced plastic (CFRP) epoxy laminates composite through a unique approach of artificial neural network (ANN) linked with the nondominated sorting genetic algorithm-II (NSGA-II). Initially, 80 AWJM experimental runs were carried out to generate the data set to train and test the ANN model. During the experimentation, the stand-off distance (SOD), water pressure, traverse speed and abrasive mass flow rate (AMFR) were selected as input AWJM variables and the average surface roughness and kerf width were considered as response variables. The established ANN model predicted the response variable with mean square error of 0.0027. Finally, the ANN coupled NSGA-II algorithm was applied to determine the optimum AWJM input parameters combinations based on multiple objectives.

Analyses of Vibration Signals Generated in W. Nr. 1.0038 Steel during Abrasive Water Jet Cutting Aimed to Process Control

The presented research was aimed at finding a suitable tool and procedure for monitoring undercuts or other problems such as cutting without abrasive or inappropriate parameters of the jet during the abrasive water jet (AWJ) cutting of hard-machined materials. Plates of structural steel RSt 37-2 of different thickness were cut through by AWJ with such traverse speeds that cuts of various qualities were obtained. Vibrations of the workpiece were monitored by three accelerometers mounted on the workpiece by a special block that was designed for this purpose. After detecting and recording vibration signals through the National Instruments (NI) program Signal Express, we processed this data by means of the LabVIEW Sound and Vibration Toolkit. Statistical evaluation of data was performed, and RMS was identified as the parameter most suitable for online vibration monitoring. We focus on the analysis of the relationship between the RMS and traverse speed.