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To gain high cost effective products along with quality and productivity, Pulsed Gas Metal Arc Welding (P-GMAW) process is used in many highly developed industries for fabrication of welded joints. The input parameters are the most important factors which affects the productivity, quality and cost effective for the welding process. The processes enable low net heat input, stable spray transfer and with low mean current. To enhance efficiencies with constant voltage GMAW process, P-GMAW is an outstanding substitute for those industries which are looking to improve quality of welds since the process helps over varying operator’s skills. It is essential to determine the input/output relationship parameters, in order to recognize and control the P-GMAW welding process. P-GMAW applies waveform control logic to fabricate a very precise control of the arc during speed range and a broad wire feed. The power source switches between low background current and a high peak current between 30 to 400 times per second to obtain modified spray transfer process. The peak current pinches off wire droplets and drive it to the welded joints over this period. The process produces low heat input allowing weld pool to solidify, that metal transfer cannot occur but by the mean time, background current maintains the arc with stable spray transfer. Trials have been conducted on SS 304 material using copper coated filler wire of size 1.4 mm based on the Taguchi’s L27 standard orthogonal array. Current, Gas Flow Rate (GFR) and Wire Feed Rate (WFR) with a constant speed are the input parameters considered to carry out trials. The output parameters are Yield strength (YS, N/mm2), percentage of elongation and Ultimate Tensile Strength (UTS, N/mm2). Indirect response parameters are Viz., AE signals such as welding AERMS, welding AEENERGY, tensile AERMS and tensile AEENERGY along with MV signals like area and height of the weld bead are considered to assess the performance of the weld bead joint. It is clearly observed from the obtained results that an excellent relationship exists between welding AERMS welding AEENERGY with tensile AERMS and tensile AEENERGY along with MV signals which were taken at the time of tensile test to evaluate the performance of the weld bead joint. Verification of the results are carried out through performing different NDT testing methods on weld bead joint Viz., X–radiography, Scanning Electron Microscope (SEM) images to analyse external defects in the welded joints. On different zones of welded joint, Energy dispersive analysis (EDX) examination is carried out for elemental composition.

Wire Electrical Discharge Machining (WEDM) is a specialized thermal machining process capable of accurately machining parts with varying hardness or complex shapes, which have sharp edges that are very difficult to be machined by the main stream machining processes. This study outlines the development of model and its application to estimation of machining performances using Group Method Data Handling Technique (GMDH) and Artificial Neural Network (ANN). Experimentation was performed as per Taguchi’s L’16 orthogonal array for Stavax (modified AISI 420 steel) material. Each experiment has been performed under different cutting conditions of pulse-on, pulse-off, current and bed speed. Among different process parameters voltage and flush rate were kept constant. Molybdenum wire having diameter of 0.18 mm was used as an electrode. Four responses namely accuracy, surface roughness, Volumetric Material Removal Rate (VMRR) and Electrode Wear (EW) have been considered for each experiment. Estimation and comparison of responses was carried out using GMDH and ANN. Group method data handling technique is ideal for complex, unstructured systems where the investigator is only interested in obtaining a high-order input-output relationship. Also, the method is heuristic in nature and is not based on a solid foundation as in regression analysis. The GMDH algorithm is designed to learn the process by training the algorithm with the experimental data. The experimental observations are divided into two sets viz., the training set and testing set. The training set is used to make the GMDH learn the process and the testing set will check the performance of GMDH. Different models can be obtained by varying the percentage of data in the training set and the best model can be selected from these, viz., 50%, 62.5% & 75%. The best model is selected from the said percentages of data. Number of variables selected at each layer is usually taken as a fixed number or a constantly increasing number. It is usually given as fractional increase in number of independent variables present in the previous level. Three different criterion functions, viz., Root Mean Square (Regularity) criterion, Unbiased criterion and Combined criterion were considered for estimation. The choice of the criterion for node selection is another important parameter for proper modeling. The Artificial Neural Network is used to study and predict the machining responses. Input data are fed into the neural network and corresponding weights and bias are extracted. Then weights and bias are integrated in the program which is used to calculate and predict the machining responses. Estimation of machining performances was obtained by using ANN for various cutting conditions. ANN estimates were obtained for various percentages of total data in the training set viz., 50%, 60% & 70%. The best model is selected from the said percentages of data. Estimation and comparison of machining performances were carried out using GMDH and ANN. Estimates from GMDH and ANN were compared and it was observed that ANN with 70% of data in training set gives better results than GMDH.

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