Process parameter Optimization of EDM

By means of the improvement and growths in new mechanisms, low weight-high strength, high hardness and temperature resistant materials have been advanced for distinctive applications which include aerospace, medical, automobile and more. In the machining of hard and metal matrix composite materials, outdated manufacturing processes are being more and more changed by more non-traditional machining processes which include Electrical Discharge Machining (EDM). The work piece material designated in this experiment is Inconel 925 taking into interpretation its wide usage in industrial applications. In today's world stainless steel provides to nearly half of the world's production and consumption for industrial determinations. In this experiment the input variable factors are voltage, current and pulse on time. As we know that Taguchi method is functional to produce an L9 orthogonal array of input variables by means of the Design of Experiments (DOE). So, Taguchi method is used to analysis the output data. The consequence of the compliant parameters stated overhead upon machining characteristics such as Material Removal Rate (MRR) and Tool Wear Rate (TWR) is considered and examined. In this we are focused on to analysis minimum TWR and maximum MRR based on control factors and response parameters.

Electro Discharge Machining Processes are used only when no other traditional machining process, like drilling, milling, grinding and other traditional machining operation can meet the necessary requirements efficiently and economically. Electro discharge machining is a non-traditional

The surface integrity and dimensional accuracy remain critical concern in electrical discharge machining (EDM).
The current research work aims at investigating the influence of various EDM process parameters like pulse current
(Ip), pulse-on time (Ton), tool work time (Tw) and tool lift time (Tup) on various aspects of surface integrity like white
layer thickness (WLT), surface crack density (SCD) and surface roughness (SR). The dimensional accuracy,
characterized by over cut (OC), has also been studied in the similar way. A response surface methodology (RSM) -
based design of experiment has been considered for this purpose. The present study also recommends an optimal
setting of EDM process parameters with an aim to improve surface integrity aspects after EDM of AISI P20 tool
steel. This has been achieved by simultaneous optimization of multiple attributes (i.e. WLT, SCD, SR and OC)
using Fuzzy-TOPSIS-based multi-criteria decision making (MCDM) approach. The optimal solution was obtained
based on five decision makers’ preferences on the four responses (i.e. WLT, SCD, SR, and OC). From this analysis,
an optimal condition of process parameters of Ip= 1A, Ton = 10μs, Tw = 0.2s, and Tup = 1.5s has been determined.
Furthermore, sensitivity analysis is also carried out to study the sensitivity or robustness of five decision makers’
preference of optimal machining parameters. Form this study, decision makers’ preference for surface crack density
has been found to be the most sensitive response and therefore should be chosen first and analyzed very carefully.

Friction Stir Welding is one of the technologies of joining solid states, which still attracts the researchers' interest. In welded joints the mechanical properties are affected by a number of mechanical properties of the joined materials and by the process parameters as well. In the present study, the effect of a number of friction stir welding parameters on the tensile strength of the welded joint have been investigated using the Taguchi method and the analysis of variance (ANOVA). The study considers different levels of friction stir welding variables; namely, different rotational speeds of (2000, 1600, 1250 rpm), different welding speeds (12.5, 16, 20 mm / min), and different welding tilt angles (0, 1, 2 degrees). The optimum process parameters and their contribution rate were selected based on the Taguchi method for test design and by using the Minitab 16 program. In this study, the best results (i.e, higher tensile strength) were obtained at a rotational velocity of 1600 rpm, linear velocity of 16 mm / min, and welding angle, 1o. The highest tensile strength was obtained under these conditions.

This paper empirically shows that the combined effect of applying the selected feature subsets and optimized parameters on machine learning techniques significantly improves the accuracy for solar power prediction. To provide evidence, experiments are carried on in two phases. For all the experiments the machine learning techniques namely Least Median Square (LMS), Multilayer Perceptron (MLP) and Support Vector Machine (SVM) are used. In the first phase five well-known wrapper feature selection methods are used to obtain the prediction accuracy of machine learning techniques with selected feature subsets and default parameter settings. The experiments from the first phase demonstrate that holding the default parameters, LMS, MLP and SVM provides better prediction accuracy (i.e. reduced MAE and MASE) with selected feature subsets rather than without selected feature subsets. After getting improved prediction accuracy from the first phase, the second phase continues the experiments to optimize machine learning parameters and the prediction accuracy of those machine learning techniques are re-evaluated through adopting both the optimized parameter settings and selected feature subsets. The comparison between the results of two phases clearly shows that the later phase (i.e. machine learning techniques with selected feature subsets and optimized parameters) provides substantial improvement in the accuracy for solar power prediction than the earlier phase (i.e. machine learning techniques with selected feature subsets and default parameters). Experiments are carried out using reliable and real life historical meteorological data. The machine learning accuracy of solar radiation prediction is justified in terms of statistical error measurement and validation metrics. Experimental results of this paper facilitate to make a concrete verdict that providing more attention and effort towards the feature subset selection and machine learning parameter optimization (e.g. combined effect of selected feature subsets and optimized parameters on prediction accuracy which is investigated in this paper) can significantly contribute to improve the accuracy of solar power prediction.

— The Firefly Algorithm (FA) is employed to determine the optimal parameter settings in a case study of the osmotic dehydration process of mushrooms. In the case, the functional form of the dehydration model is established through a response surface technique and the resulting mathematical programming is formulated as a non-linear goal programming model. For optimization purposes, a computationally efficient, FA-driven method is used and the resulting optimal process parameters are shown to be superior to those from previous approaches.