International Journal of Experimental Design and Process Optimisation
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International Journal of Experimental Design and Process Optimisation (2 papers in press)
Experimental Design in Complex Model Formulation for Lightning Prediction by Jared Nystrom, Raymond Hill, Andrew Geyer, Joseph Pignatiello Jr., Eric Chicken Abstract: Space launch operations at Kennedy Space Center and Cape Canaveral Space Force Station (KSC/CCSFS) are complicated by unique requirements for near-real time determination of risk from lightning. Weather sensor networks for lightning forecasting produce data that are noisy, high volume, and high frequency time series for which traditional forecasting methods are of- ten ill-suited. Current approaches result in significant residual uncertainties and consequentially may result in forecasting operational policies that are excessively conservative or inefficient. This work first proposes a forecasting methodology using wavelet decomposition of chaotic weather sensor time series and semiparametric single-index models to mitigate the chaotic signal and any possible distributional misspecification. Then, a screening experiment with augmentations is used to demonstrate how to explore the complex factor space of model parameters, guiding decisions regarding model formulation and gaining insight for follow-on research. Results indi- cate a promising technique for operationally relevant lightning prediction from chaotic sensor measurements. Keywords: Wavelet analysis; Time series analysis; Forecasting; Design of Experiments.
Modeling and optimization of nanocoolant minimum quantity lubrication process parameters for grinding performance by Rahul Chakule, Sharad Chaudhari, Poonam Talmale Abstract: Nanocoolant minimum quantity lubrication is economical, sustainable, and an environment-friendly technique of coolant flow for machining compared to flood lubrication. In the present experimental study, the modeling and optimization of nanocoolant minimum quantity lubrication process are carried out for improving the grinding performance of EN 31 hardened steel. The optimized value of input process parameters such as table speed, depth of cut, coolant flow rate, dressing depth, and aluminum oxide nanocoolant concentrations obtained from the Jaya algorithm is used for finding the grinding performance in terms of cutting forces, surface roughness, and material removal rate. The experiments were conducted by response surface methodology using Minitab17 statistical software. The optimization of process parameters is carried out for single and multi-objective responses. The results show that the nanocoolant minimum quantity lubrication process improves the grinding performance significantly using optimized values at 0.30 volume % nanocoolant concentration. Keywords: cutting force; design of experiments; EN 31; grinding performance; Jaya algorithm; modeling; material removal rate; nanocoolant minimum quantity lubrication; optimization; surface roughness.