Experimental Investigation of Surface Roughness and Material Removal Rate in Wire EDM of Stainless Steel 304

Its unexcelled mechanical and physical properties, in addition to its biocompatibility, have made stainless steel 304 a prime candidate for a wide range of applications. Among different manufacturing techniques, electrical discharge machining (EDM) has shown high potential in processing stainless steel 304 in a controllable manner. This paper reports the results of an experimental investigation into the effect of the process parameters on the obtainable surface roughness and material removal rate of stainless steel 304, when slotted using wire EDM. A full factorial design of the experiment was followed when conducting experimental trials in which the effects of the different levels of the five process parameters; applied voltage, traverse feed, pulse-on time, pulse-off time, and current intensity were investigated. The geometry of the cut slots was characterized using the MATLAB image processing toolbox to detect the edge and precise width of the cut slot along its entire length to determine the material removal rate. In addition, the surface roughness of the side walls of the slots were characterized, and the roughness average was evaluated for the range of the process parameters being examined. The effect of the five process parameters on both responses were studied, and the results revealed that the material removal rate is significantly influenced by feed (p-value = 9.72 × 10−29), followed by current tension (p-value = 6.02 × 10−7), and voltage (p-value = 3.77 × 10−5), while the most significant parameters affecting the surface roughness are current tension (p-value = 1.89 × 10−7), followed by pulse-on time (1.602 × 10−5), and pulse-off time (0.0204). The developed regression models and associated prediction plots offer a reliable tool to predict the effect of the process parameters, and thus enable the optimizing of their effects on both responses; surface roughness and material removal rate. The results also reveal the trade-off between the effect of significant process parameters on the material removal rate and surface roughness. This points out the need for a robust multi-objective optimization technique to identify the process window for obtaining high quality surfaces while keeping the material removal rate as high as possible

Industry 4.0-Oriented Deep Learning Models for Human Activity Recognition

According to the Industry 4.0 vision, humans in a smart factory, should be equipped with formidable and seamless communication capabilities and integrated into a cyber-physical system (CPS) that can be utilized to monitor and recognize human activity via artificial intelligence (e.g., deep learning). Recent advances in the accuracy of deep learning have contributed significantly to solving the human activity recognition issues, but it remains necessary to develop high performance deep learning models that provide greater accuracy. In this paper, three models: long short-term memory (LSTM), convolutional neural network (CNN), and combined CNN-LSTM are proposed for classification of human activities. These models are applied to a dataset collected from 36 persons engaged in 6 classes of activities – downstairs, jogging, sitting, standing, upstairs, and walking. The proposed models are trained using TensorFlow framework with a hyper-parameter tuning method to achieve high accuracy. Experimentally, confusion matrices and receiver operating characteristic (ROC) curves are used to assess the performance of the proposed models. The results illustrate that the hybrid model CNN-LSTM provides a better performance than either LSTM or CNN in the classification of human activities. The CNN-LSTM model provides the best performance, with a testing accuracy of 97.76%, followed by the LSTM with a testing accuracy of 96.61%, while the CNN shows the least testing accuracy of 94.51%. The testing loss rates for the LSTM, CNN, and CNN-LSTM are 0.236, 0.232, and 0.167, respectively, while the precision, recall, F1 -Measure, and the area under the ROC curves (AUC S ) for the CNN-LSTM are 97.75%, 97.77%, 97.76%, and 100%, respectively

Experiment-Based Process Modeling and Optimization for High-Quality and Resource-Efficient FFF 3D Printing

This article reports on the investigation of the effects of process parameters and their interactions on as-built part quality and resource-efficiency of the fused filament fabrication 3D printing process. In particular, the influence of five process parameters: infill percentage, layer thickness, printing speed, printing temperature, and surface inclination angle on dimensional accuracy, surface roughness of the built part, energy consumption, and productivity of the process was examined using Taguchi orthogonal array (L50) design of experiment. The experimental results were analyzed using ANOVA and statistical analysis, and the parameters for optimal responses were identified. Regression models were developed to predict different process responses in terms of the five process parameters experimentally examined in this study. It was found that dimensional accuracy is negatively influenced by high values of layer thickness and printing speed, since thick layers of printed material tend to spread out and high printing speeds hinder accurate deposition of the printed material. In addition, the printing temperature, which regulates the viscosity of the used material, plays a significant role and helps to minimize the dimensional error caused by thick layers and high printing speeds, whereas the surface roughness depends very much on surface inclination angle and layer thickness, which together determine the influence of the staircase effect. Energy consumption and productivity are primarily affected by printing speed and layer thickness, due to their high correlation with build time

Self-Flushing in EDM Drilling of Ti6Al4V Using Rotating Shaped Electrodes

This article reports an experimental investigation of the efficacy of self-flushing in the Electrical Discharge Machining (EDM) process in terms of tool wear rate (TWR), hole taper angle and material removal rate (MRR). In addition to a plain cylindrical shape, electrodes of different cross sections (slotted cylindrical, sharp-cornered triangular, round-cornered triangular, sharp-cornered square, round-cornered square, sharp-cornered hexagonal and round-cornered hexagonal) were designed as a means of inducing debris egress and then fabricated in graphite. EDM drilling trials using the rotating shaped electrodes were carried out on a Ti6Al4V workpiece. The results revealed that, although a low TWR and minimum hole taper angle were achieved using a plain cylindrical electrode, the usage of rotating shaped electrodes provided self-flushing of the dielectric fluid during the EDM process, which led to an improvement in MRR compared to that achieved with a plain cylindrical electrode. Besides, in general, the electrodes with rounded corners are associated with a lower TWR, a lower hole taper angle and a higher MRR when compared to the electrodes with sharp corners. Considering these results, it was concluded that different process attributes, i.e., TWR, hole taper angle and MRR, are all greatly affected by the electrode shape, and thus, the proper selection of the electrode shape is a precondition to attain a specific response from the EDM process

Iterative surface warping to shape craters in micro-EDM simulation

This paper introduces a new method for simulating the micro-EDM process in order to predict both the tool’s wear and the workpiece’s roughness. The tool and workpiece are defined by NURBS patches whose shapes result from an iterative crater-by-crater deformation technique driven by physical parameters. Through hundreds of thousands of local surface warping, the method is able to compute the global as well as the local shapes of the tool and workpiece. At each step, the warping vector and function are computed so as to be able to generate a spherical crater whose volume is also controlled. While acting very locally to simulate the real physical phenomenon, not only the method can evaluate the tool’s wear from the overall final shape at a low resolution level, but it can also estimate the workpiece’s roughness from the high resolution level. The simulation method is validated through a comparison with experimental data. Different simulations are presented with an increase in computation accuracy in order to study its influence on the results and their deviation from expected values

Modelling, simulation and experimental investigation of the effects of material microstructure on the micro-endmiling process

Recently it has been revealed that workpiece microstructure has dominant effects on the performance of the micro-machining process. However, so far, there has been no detailed study of these effects on micro-endmilling. In this research, the influence of the microstructure on the matters such as cutting regime, tool wear and surface quality has been investigated. Initially, an experimental investigation has been carried out to identify the machining response of materials metallurgically and mechanically modified at the micro-scale. Tests have been conducted that involved micro-milling slots in coarse-grained (CG) Cu99.9E with an average grain size of 30 μm and ultrafine-grained (UFG) Cu99.9E with an average grain size of 200 nm. Then, a method of assessing the homogeneity of the material microstructure has been proposed based on Atomic Force Microscope (AFM) measurements of the coefficient of friction at the atomic scale, enabling a comparative evaluation of the modified microstructures. The investigation has shown that, by refining the material microstructure, the minimum chip thickness can be reduced and a better surface finish can be achieved. Also, the homogeneity of the microstructure can be improved which in turn reduces surface defects. Furthermore, a new model to simulate the surface generation process during micro- endmilling of dual-phase materials has been developed. The proposed model considers the effects of the following factors: the geometry of the cutting tool, the feed rate, and the workpiece microstructure. In particular, variations of the minimum chip thickness at phase boundaries are considered by feeding maps of the microstructure into the model. Thus, the model takes into account these variations that alter the machining mechanism from a proper cutting to ploughing and vice versa, and are the main cause of micro-burr formation. By applying the proposed model it is possible to estimate more accurately the resulting roughness owing to the dominance of the micro-burrs formation during the surface generation process in micro-milling of multi-phase materials. The model has been experimentally validated by machining two different samples of dual-phase steel, AISI 1040 and AISI 8620, under a range of chip-loads. The results have shown that the proposed model accurately predicts the roughness of the machined surfaces with average errors of 14.5% and 17.4% for the AISI 1040 and AISI 8620 samples, respectively. The developed model successfully elucidates the mechanism of micro-burr formation at the phase boundaries, and quantitatively describes its contributions to the resulting surface roughness after micro-endmilling. (Abstract shortened by UMI.)

FEM-based study of precision hard turning of stainless steel 316L

This study aims to investigate chip formation and surface generation during the precision turning of stainless steel 316L samples. A Finite Element Method (FEM) was used to simulate the chipping process of the stainless steel but with only a restricted number of process parameters. A set of turning tests was carried out using tungsten carbide tools under similar cutting conditions to validate the results obtained from the FEM for the chipping process and at the same time to experimentally examine the generated surface roughness. These results helped in the analysis and understanding the chip formation process and the surface generation phenomena during the cutting process, especially on micro scale. Good agreement between experiments and FEM results was found, which confirmed that the cutting process was accurately simulated by the FEM and allowed the identification of the optimum process parameters to ensure high performance. Results obtained from the simulation revealed that, an applied feed equals to 0.75 of edge radius of new cutting tool is the optimal cutting conditions for stainless steel 316L. Moreover, the experimental results demonstrated that in contrast to conventional turning processes, a nonlinear relationship was found between the feed rate and obtainable surface roughness, with a minimum surface roughness obtained when the feed rate laid between 0.75 and 1.25 times the original cutting edge radius, for new and worn tools, respectively

Iterative surface warping to shape craters in micro‐EDM simulation

This paper introduces a new method for simulat- ing the micro-EDM process in order to predict both the tool’s wear and the workpiece’s roughness. The tool and workpiece are de ned by NURBS patches whose shapes result from an iterative crater-by-crater deformation technique driven by physical parameters. Through hundreds of thousands of local surface warping, the method is able to compute the global as well as the local shapes of the tool and workpiece. At each step, the warping vector and function are computed so as to be able to generate a spherical crater whose volume is also controlled. While acting very locally to simulate the real physical phenomenon, not only the method can evaluate the tool’s wear from the overall nal shape at a low resolu- tion level, but it can also estimate the workpiece’s roughness from the high resolution level. The simulation method is validated through a comparison with experimental data. Dif- ferent simulations are presented with an increase in compu- tation accuracy in order to study its in uence on the results and their deviation from expected values

Part Tailoring in Metal-Additive Manufacturing: A Step towards Functionally Graded Customized Stainless-Steel Components Using Laser Powder Bed Fusion

The aim of this project is to demonstrate a proof of concept by using Additive Manufacturing (AM) technology in order to demonstrate its viability for the production of tailor-made components with regions of varying (higher and lower) hardness and surface roughness within a single part. In order to do this, first a test piece is designed and printed following a full factorial design of the experiment with eight runs with varying process parameters set within different regions of one part. The structure is printed several times with the laser-powder-bed-fusion-based metal-additive-manufacturing system “Sodick LPM 325” using AISI 420 in order to test and validate the change in the achievable mechanical property and surface roughness. The above-mentioned quality marks are characterized using a tactile profilometer, Rockwell test and part density, and the results are statistically analyzed using MATLAB. The results show that the linear energy density plays a significant role in controlling the surface roughness of the top surface of the components while the hardness on the top surface is unaffected. On the side surfaces, it is known that the layer thickness plays a significant role on the surface roughness as well as hardness. Looking at the results obtained, it is seen that the variation in the obtained side surface roughness is not significant to changes in the Linear Energy Density (LED) as the layer thickness was kept constant, with only slight reductions in hardness seen. The annealing process resulted in a significant reduction in hardness. This work has shown that through the careful tailoring of processing conditions, multi-functionality within one part can be integrated and has created promising avenues for further research into achieving fully functionally graded structures