During the machining processes, heat gets generated as a result of plastic deformation of metal and friction along the tool–chip and tool–work piece interface. In materials having high thermal conductivity, like aluminium alloys, large amount of this heat is absorbed by the work piece. This results in the rise in the temperature of the work piece, which may lead to dimensional inaccuracies, surface damage and deformation. So, it is needed to control rise in the temperature of the work piece. This paper focuses on the measurement, analysis and prediction of work piece temperature rise during the dry end milling operation of Al 6063. The control factors used for experimentation were number of flutes, spindle speed, depth of cut and feed rate. The Taguchi method was employed for the planning of experimentation and L18 orthogonal array was selected. The temperature rise of the work piece was measured with the help of K-type thermocouple embedded in the work piece. Signal to noise (S/N) ratio analysis was carried out using the lower-the-better quality characteristics. Depth of cut was identified as the most significant factor affecting the work piece temperature rise, followed by spindle speed. Analysis of variance (ANOVA) was employed to find out the significant parameters affecting the work piece temperature rise. ANOVA results were found to be in line with the S/N ratio analysis. Regression analysis was used for developing empirical equation of temperature rise. The temperature rise of the work piece was calculated using the regression equation and was found to be in good agreement with the measured values. Finally, confirmation tests were carried out to verify the results obtained. From the confirmation test it was found that the Taguchi method is an effective method to determine optimised parameters for minimization of work piece temperature.
In this study, Taguchi method is used to find out the effect of micro alloying elements like vanadium, niobium and titanium on the hardness and tensile strength of the normalized cast steel. Based on this method, plan of experiments were made by using orthogonal arrays to acquire the data on hardness and tensile strength. The signal to noise ratio and analysis of variance (ANOVA) are used to investigate the effect of these micro alloying elements on these two mechanical properties of the micro alloyed normalized cast steel. The results indicated that in the micro alloyed normalized cast steel both these properties increases when compared to non-micro-alloyed normalized cast steel. The effect of niobium addition was found to be significantly higher to obtain higher hardness and tensile strength when compared to other micro alloying elements. The maximum hardness of 200HV and the maximum tensile strength of 780 N/mm2 were obtained in 0.05%Nb addition micro alloyed normalized cast steel. Micro-alloyed with niobium normalized cast steel have the finest and uniform microstructure and fine pearlite colonies distributed uniformly in the ferrite. The optimum condition to obtain higher hardness and tensile strength were determined. The results were verified with experiments.
In the calculations presented in the article, an artificial immune system (AIS) was used to plan the routes of the fleet of delivery vehicles supplying food products to customers waiting for the delivery within a specified, short time, in such a manner so as to avoid delays and minimize the number of delivery vehicles. This type of task is classified as an open vehicle routing problem with time windows (OVRPWT). It comes down to the task of a traveling salesman, which belongs to NP-hard problems. The use of the AIS to solve this problem proved effective. The paper compares the results of AIS with two other varieties of artificial intelligence: genetic algorithms (GA) and simulated annealing (SA). The presented methods are controlled by sets of parameters, which were adjusted using the Taguchi method. Finally, the results were compared, which allowed for the evaluation of all these methods. The results obtained using AIS proved to be the best.
The dry sliding wear behavior of heat-treated super duplex stainless steel AISI 2507 was examined by taking pin-on-disc type of wear-test rig. Independent parameters, namely applied load, sliding distance, and sliding speed, influence mainly the wear rate of super duplex stainless steel. The said material was heat treated to a temperature of 850°C for 1 hour followed by water quenching. The heat treatment was carried out to precipitate the secondary sigma phase formation. Experiments were conducted to study the influence of independent parameters set at three factor levels using the L27 orthogonal array of the Taguchi experimental design on the wear rate. Statistical significance of both individual and combined factor effects was determined for specific wear rate. Surface plots were drawn to explain the behavior of independent variables on the measured wear rate. Statistically, the models were validated using the analysis of variance test. Multiple non-linear regression equations were derived for wear rate expressed as non-linear functions of independent variables. Further, the prediction accuracy of the developed regression equation was tested with the actual experiments. The independent parameters responsible for the desired minimum wear rate were determined by using the desirability function approach. The worn-out surface characteristics obtained for the minimum wear rate was examined using the scanning electron microscope. The desired smooth surface was obtained for the determined optimal condition by desirability function approach.