Laser beam micromachining (LBMM) and micro electro-discharge machining (µEDM) based sequential micromachining technique, LBMM-µEDM has drawn signi cant research attention to utilizing the advantages of both methods, i.e. LBMM and µEDM. In this process, a pilot hole is machined by the LBMM and subsequently nishing operation of the hole is carried out by the µEDM. This paper presents an experimental investigation on the stainless steel (type SS304) to observe the effects of laser input parameters (namely laser power, scanning speed, and pulse frequency) on the performance of the nishing technique that is the µEDM in this case. The scope of the work is limited to 1-D machining, i.e. drilling micro holes. It was found that laser input parameters mainly scanning speed and power in uenced the output performance of µEDM signi cantly. Our study suggests that if an increased scanning speed at a lower laser power is used for the pilot hole drilling by the LBMM process, it could result in signi cantly slower µEDM machining time. On the contrary, if the higher laser power is used with even the highest scanning speed for the pilot hole drilling, then µEDM processing time was faster than the previous case. Similarly, µEDM time was also quicker for LBMMed pilot holes machined at low laser power and slow scanning speed. Our study con rms that LBMM-µEDM based sequential machining technique reduces the machining time, tool wear and instability (in terms of short circuit count) by a margin of 2.5 x, 9 x and 40 x respectively in contrast to the pure µEDM process without compromising the quality of the holes.
A sequential process combining laser beam micromachining(LBMM) and micro electrodischarge machining (μEDM) for the micro-drilling purpose was developed to incorporate both methods' benefits. In this sequential process, a guiding hole is produced through LBMM first, followed by μEDM applied to that same hole for more fine machining. This process facilitates a more stable, efficient machining regime with faster processing (compared to pure μEDM) and much better hole quality (compared to LBMMed holes). Studies suggest that strong correlations exist between the various input and output parameters of the sequential process. However, a mathematical model that maps and simultaneously predicts all these output parameters from the input parameters is yet to be developed. Our experimental study observed that the µEDM finishing operation's various output parameters are influenced by the morphological condition of the LBMMed holes. Hence, an artificial neural network(ANN) based dual-stage modelling method was developed to predict the sequential process's outputs. The first stage of the dual-stage model was utilized to predict various LBMM process outputs from different laser input parameters. Furthermore, in the second stage, LBMM predicted outputs (such as pilot hole entry area, exit area, recast layer, and heat affected zone) were used for the final prediction of the sequential process outputs (i.e. machining time by μEDM, machining stability during μEDM in terms of short circuit count and tool wear during μEDM). The model was evaluated based on the average RMSE (Root Mean Square Errors) values for the individual output parameters' complete set data, i.e. μEDM time, short circuit count and tool wear. The values of Average RMSE for the parameters as mentioned earlier were found to be 0.1272(87.28% accuracy), 0.1085(89.15% accuracy), 0.097 (90.3% accuracy), respectively.
Nanofluids have become a point of intense interest for its usability in sectors where convective heat transfer is a requirement. Whereas knowing the overall thermal transport characteristics of nanofluids is the key for their proper utilisation, the domain of nanofluids turbulent convective heat transfer is still heavily understudied, where conducting a parametric study on their heat transferring behaviour along with assessing the effect of boundary conditions on their heat transfer enhancement and the available CFD models’ efficiency to account for nanoparticle size are vital necessities. In this study, highly turbulent flow of nanofluids inside a circular pipe under constant wall temperature has been simulated using the Mixture model. Correlations between all the parameters related to nanofluids turbulent convective heat transfer have been established and the impact of variable temperature boundary condition on nanofluids heat transfer enhancement has been investigated. In addition, Mixture models’ ability to assess nanoparticle size variation on heat transfer of nanofluids has been shown. Results suggest that nanofluids heat transfer is dominated by the amount of nanoparticle concentration present in the base fluid when Reynolds number is kept constant. Also, for a certain particle concentration, intensification of heat transfer is guided by the degree of turbulence. The findings also depict that nanofluids heat transferring capability is independent of the temperature boundary conditions used and Mixture model is unable to assess the change in heat transfer due to variation in nanoparticle size.
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