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.

An Evaluation of Optimal Friction Stir Welding Factors (FSWF) For Yellow Brass 405-20

Friction stir welding (FSW) is a green, environmentally amicable, and solid-state joining technology. FSW can successfully weld a wide range of materials (similar/dissimilar parent materials) including aluminum, copper, steel, different alloys from these materials, plastics, composites. FSW of brass has already been accomplished by fewer researchers. In this research, yellow brass 405-20 is, therefore, welded with FSW that was never welded before. In this study, tool material utilized was M2 HSS that was also novel. Effect of two friction stir weld factors (FSWF), rotational speed (RS) and traverse speed (TS), was found on three output parameters i.e., weld temperature, weld strength and weld hardness. Weld temperature developed, was found to be 63.72% of melting point of base metal. A significant improvement in friction stir weld strength (FSWS) was also measured that was found to be 106.37% of the base brass strength. Finally, weld hardness was measured which was found to be 87.80% of original brass hardness. Based on main effects, optimal FSW factors were found to be 1450 rpm and 60 mm/min resulting interestingly in optimal temperature, optimal weld strength, and optimal hardness. Rotational speed (RS) was found to be significant to affect the weld temperature only at the friction stir weld zone (FSWZ) with the highest percent contribution (PCR) of 65.69%. However, PCR of transverse speed was found to be maximum for affecting weld strength as compared to its PCR towards both weld temperature and weld hardness. Current study was also deepened by microscopic investigation.

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.

Taguchi based fuzzy logic model for optimisation and prediction of surface roughness during AWJM of DRCUFP composites

From last two decades, plant fiber reinforced polymer/polyester composites have been effectively used in structural and automotive applications. Researchers and manufacturers are looking forward for an effective utilization of these composites. However, despite the outstanding properties in terms of load bearing capacity and environmental sustainability of plant fibers the uptake of these composites are limited due to its poor machinability characteristics. Hence in this paper, Taguchi based fuzzy logic model for the optimization and prediction of process output variable such as surface roughness during Abrasive Water Jet Machining (AWJM) of new class of plant fiber reinforced polyester composites i.e., Discontinuously Reinforced Caryota Urens Fiber Polyester (DRCUFP) composites has been explored. Initially machining experiments has been carried out using L27 orthogonal array obtained from Taguchi Design of Experiments (TDOE). Finally, Taguchi based fuzzy logic model has been developed for optimisation and prediction of surface roughness. From the extensive experimentation using TDOE it was observed that the optimum cutting conditions for obtaining minimum surface roughness value, water pressure (A): 300 bar, traverse speed (B): 50 mm, stand of distance: 1 mm, abrasive flow rate: 12 g/s, depth of cut (C): 5 mm and Abrasive Size:200 microns. Further from FLM, it is observed that minimum water pressure (A): 100 bar, traverse speed (B): 50 mm, stand of distance: 1 mm, abrasive flow rate: 8 g/s, depth of cut (C): 5 mm and abrasive size:100 microns gave higher surface roughness values (3.47 microns) than that at maximum water pressure (A): 300 bar, traverse speed (B): 150 mm, stand of distance: 4 mm, abrasive flow rate: 12 g/s, depth of cut (C): 15 mm and abrasive size:200 microns the surface roughness values (3.25 microns).

INFLUENCE OF TECHNOLOGICAL PARAMETERS ON THE AWJ PROCESS PERFORMANCE

The present study aims to perform a comparative analysis of the technological parameters influence on the output parameters for two biodegradable polymeric materials, Arbofill Fichte and Arboblend V2 Nature. The varied input parameters during abrasive water jet cuuting (AWJ) were water jet pressure, traverse speed and abrasive material flow. The quantitative and qualitative output parameters proposed are the amount of material removed (MR) and the inclination angle - α° of the resulted surfaces. The measured MR and α° values highlighted the fact that they fall within the admissible parameters, so that the obtained parts by cutting the Arbofill Fichte and Arboblend V2 Nature samples can be used in industrial applications that require this type of processing and more. Was also achieved the optimization of the technological parameters used for processing according to the next criteria: minimum inclination angle and minimum amount of material removed.

OPTIMIZATION OF PROCESS PARAMETERS FOR SURFACE ROUGHNESS OF GFRP WITH AWJ MACHINING USING TAGUCHI AND GRA METHODS

For machining of composites like glass fibre reinforced polymers, abrasive water jet machining is generally used. In the present paper, an experimental investigation is described which is focused on studying the influence of AWJM process parameters on surface roughness of machined samples. Four process parameters namely water pressure, traverse speed, stand-off distance and abrasive mass flow rate are considered in the present study. Taguchi technique is used for the design of experiments and analysis of variance (ANOVA) is performed to determine the significance of parameters. It is found that water pressure and traverse speed are the major significant parameters for influencing surface roughness. Surface roughness decreases with increase in water pressure and decrease in traverse speed. For surface roughness minimization Grey Relational Analysis (GRA) technique is used for multi optimization of process parameters and experimental results are analyzed using Grey Relational Grades (GRG).

Impact of Preparation of Titanium Alloys on Their Abrasive Water Jet Machining

Several titanium alloys, i.e., grade 2 Ti, Ti6Al4V and NiTi alloy, prepared by selected deformation procedures were subjected to abrasive water jet (AWJ) cutting and subsequently analysed. The study describes samples’ preparations and respective material structures. The impact of deformation processing of the selected alloys on the declination angle during cutting, and the results of measurements of surface wall quality performed for the selected samples at the Department of Physics of Faculty of Electrical Engineering and Computer Science at VŠB–Technical University of Ostrava, are presented and discussed, as are also the influences of structural features of the processed titanium alloys on surface qualities of the investigated samples. The results showed that the highest resistance to AWJ machining exhibited the Ti6Al4V alloy prepared by forward extrusion. Its declination angle (recalculated to the thickness 10 mm to compare all the studied samples) was 12.33° at the traverse speed of 100 mm/min, pumping pressure of 380 MPa, and abrasive mass flow rate of 250 g/min.

Multi-Response Optimization of Abrasive Waterjet Machining of Ti6Al4V Using Integrated Approach of Utilized Heat Transfer Search Algorithm and RSM

Machining of Titanium alloys (Ti6Al4V) becomes more vital due to its essential role in biomedical, aerospace, and many other industries owing to the enhanced engineering properties. In the current study, a Box–Behnken design of the response surface methodology (RSM) was used to investigate the performance of the abrasive water jet machining (AWJM) of Ti6Al4V. For process parameter optimization, a systematic strategy combining RSM and a heat-transfer search (HTS) algorithm was investigated. The nozzle traverse speed (Tv), abrasive mass flow rate (Af), and stand-off distance (Sd) were selected as AWJM variables, whereas the material removal rate (MRR), surface roughness (SR), and kerf taper angle (θ) were considered as output responses. Statistical models were developed for the response, and Analysis of variance (ANOVA) was executed for determining the robustness of responses. The single objective optimization result yielded a maximum MRR of 0.2304 g/min (at Tv of 250 mm/min, Af of 500 g/min, and Sd of 1.5 mm), a minimum SR of 2.99 µm, and a minimum θ of 1.72 (both responses at Tv of 150 mm/min, Af of 500 g/min, and Sd of 1.5 mm). A multi-objective HTS algorithm was implemented, and Pareto optimal points were produced. 3D and 2D plots were plotted using Pareto optimal points, which highlighted the non-dominant feasible solutions. The effectiveness of the suggested model was proved in predicting and optimizing the AWJM variables. The surface morphology of the machined surfaces was investigated using the scanning electron microscope. The confirmation test was performed using optimized cutting parameters to validate the results.