Feasibility Assessment MRR and Surface Roughness on Inconel 825 by Abrasive Water Jet Machining Process

M.Shunmuga Priyan — 2026-01-03

The creation of rectangular pockets is a developing area where abrasive water jet machining (AWJM) is utilized. The current trend highlights an increasing focus on milling applications in the AWJM process. This research included performing experiments on Inconel 825 to evaluate the practicality of using the AWJM technique for producing 3D features, such as pockets sized 10mm x 10mm. A design of experiments based on the Taguchi method was utilized, concentrating on the input variables: step over (SO), traverse speed (TS), pressure (P), and abrasive flow rate (AFR) as the variables of the process. The parameters were changed at three levels, maintaining a standoff distance (SOD) of 2 mm, an orifice diameter of 0.35 mm, and using garnet 85# for the abrasive material size. The output parameters obtained, specifically Depth Of Cut (DOC), Material Removal Rate (MRR), and Surface Roughness (Ra), were evaluated through two methods: Hatch strategy and Spiral strategy. A variance analysis was conducted to investigate the interactions between the process parameters and to calculate the F value. The results showed that the traverse speed and step over greatly affected the output parameters DOC, MRR, and Ra.

Evaluating Seasonal Patterns in Pediatric Diarrhea and Pneumonia using SARIMA: A Comparative Analysis of Simulated and Real Time Series in Rivers State, Nigeria

Deebom Zorle Dum — 2026-01-03

This study evaluates the effectiveness of Seasonal Autoregressive Integrated Moving Average (SARIMA) models in forecasting trends of Pediatric Diarrhea and Pneumonia in Rivers State, Nigeria, with particular emphasis on handling auto-correlated errors. The objectives include analyzing seasonal patterns, assessing autocorrelation structures, comparing alternative SARIMA specifications, and identifying the most suitable model using both real and simulated datasets. The empirical data comprise monthly records of reported cases among children under five years, obtained from the Rivers State Primary Healthcare Database. Additionally, a synthetic time series was generated to replicate similar autocorrelation characteristics for robustness testing. Autocorrelation diagnostics, including the Ljung-Box test and ACF/PACF plots, revealed significant serial dependencies across all series, underscoring the limitations of models that ignore autocorrelation. Competing SARIMA models were estimated and evaluated based on Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Root Mean Square Error (RMSE), and residual diagnostic checks. Among the models assessed, SARIMA(0,1,2)(0,0,2)[12][12][12] emerged as the best-fitting model, particularly for pediatric diarrhea, demonstrating strong predictive performance and adherence to diagnostic assumptions. Although Auto ARIMA achieved comparable results for pneumonia, SARIMA models exhibited superior residual behaviour and diagnostic reliability. By integrating actual and simulated data, this research addresses gaps in previous studies that overlooked seasonality and autocorrelation in padiatric health time series modeling. The findings affirm the usefulness of SARIMA for time series epidemiology and highlight its potential for enhancing disease investigation and informing public health interventions in resource-constrained settings such as Rivers State.

Applied Sciences Research Periodicals

Feasibility Assessment MRR and Surface Roughness on Inconel 825 by Abrasive Water Jet Machining Process

Evaluating Seasonal Patterns in Pediatric Diarrhea and Pneumonia using SARIMA: A Comparative Analysis of Simulated and Real Time Series in Rivers State, Nigeria