In the development of an accurate modeling technique for the design of an efficient machining process, manufacturers must be able to identify the most suitable technique capable of producing a fast and accurate performance. This study evaluates the performance of the Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) models in predicting the machining responses (metal removal rate and tool wear) in an AIS steel turning operation. With data generated from carefully designed machining experimentation, the adequacies of the ANN and ANFIS techniques in modeling and predicting the responses were carefully analyzed and compared. Both techniques displayed excellent abilities in predicting the responses of the machining process. However, a comparison of both techniques indicates that ANN is relatively superior to the ANFIS techniques, considering the accuracy of its results in terms of the prediction errors obtained for the ANN and ANFIS of 6.1% and 11.5% for the MRR and 4.1% and 7.2% for the Tool wear respectively. The coefficient of correlation (R 2 ) obtained from the analysis further confirms the preference of the ANN with a maximum value of 92.1% recorded using the ANN compared to that of the ANFIS of 73%. The experiment further reveals that the performance of the ANN technique can yield the most ideal results when the right parameters are employed.

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The rise in the failure of mechanical components, some of which are attributable to poor weld joints has given rise to research study on the optimization of weld joint strengths. The quality of welds is highly dependent on the right combination of input process parameters. Irrespective of the welding process, the need for the right combination of input process parameters cannot be over emphasized. To achieve a desired weld quality, the weld features such bead geometry and the mechanical properties were examined and related to the weld input parameters. The Response Surface Methodology (RSM) was used to predict and optimize the weld strength properties (tensile strength and hardness) of a Gas Tungsten Arc Welded 10mm thick mild steel plate. Model adequacy checks, was done using analysis of variance (ANOVA) and found to be adequate. The ANOVA showed that current and gas flow rate had the most significant effect on the tensile strength, but on the Hardness, the gas flow rate and filler rod had the most significant effect. The model F-value of 12.69 at a P value of 0.0001 for the tensile strength and F-value of 8.51 at a P value of 0.0001 for the hardness, showed the significance of the model employed. The optimal tensile strength of 497.555N/mm 2 and Hardness of 192.556BHN was observed at a current of 170.12 amp, voltage of 19.84 volt, gas flow rate of 23.92 l/min and filler rod diameter of 2.4mm.

ABSTRACT:Response surface methodology (RSM) was used to evaluate the wear of a cutting tool during a turning machine cutting process. The data obtained reveals optimum values for this process as cutting speed= 1303rev/min, Feed Rate= 0.354 (mm/min), Depth of cut= 0.458 (mm) at a minimized tool wear of 0.173. With the use of the response surface optimization process, the experiment was planned, carried out and analyzed. Analysis of variance (ANOVA) was used to check the significance of the model, as well as identify process parameters with the most significant effect on the tool wear. Machining is one of the five groups of manufacturing processes which comprises casting, forming, powder metallurgy and joining (Nagendra and Mittal, 2007). From the time of the industrial revolution, machining has remained one of the core of the manufacturing industries and may be classified into two main groups as (i) cutting process with traditional machining (e.g., turning, milling, boring and grinding) and (ii) cutting process with modern machining (e.g., electrical discharge machining (EDM) and abrasive waterjet (AWJ)). Turning which is one of the cutting operations performed with traditional machining, is one in which the part is rotated as the cutting tool is held against it on a machine called a lathe. In optimizing the machining process parameters, the selection of machining process parameters is a very crucial part in order for the machine operations to be successful (Rao and Pawar, 2010). The machining parameters (cutting speed, feed rate, depth of cut) accelerate tool wear and it affects the surface finishing. The tool wear is directly related to the machining parameters (Musialek, 1999). Response surface methodology (RSM) is a useful statistical technique in analyzing and modeling data. It's a collection of mathematical and statistical techniques, which are useful for the modeling and analysis of engineering problems and developing, improving, and optimizing processes. It also has important applications in the design, development, and formulation of new products, as well as in the improvement of existing product designs, and it is an effective tool for constructing optimization models (Montgomery, 1991). The RSM comprises the designing of a set of experiments for adequate and reliable measurement of the true mean response of interest, the development of a mathematical model with best fits, finding the optimum set of experimental factors that produces maximum or minimum value of response, and finally representing the direct and interactive effects of process variables on two and three dimensional graphs. Flank wear has been traditionally emphasized more than crater wear during analysis of cutting tool wear. This is because of the more direct influence that flank wear has on the accuracy of the product. Among all the types of wear, flank wear and the resulting recession of the cutting edge affect the workpiece dimension, as well as quality, to a large extent. Flank wear also results in changes to the mechanics of the cutt...

Citation informationCite this article as: The use of multi-objective genetic algorithm (MOGA) in optimizing and predicting weld quality, Samuel O. Sada, Cogent Engineering (2020), 7: 1741310.