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The use of a diagnostic and prognostic tool for predictive maintenance serves as a continuous inspection, detective and predictive tool for making important decisions on maintenance activities before failure occurs. The prediction of failure is important in the railway industry in reducing the maintenance and operating cost, minimizing interruptions, risk, unscheduled maintenance and accidents while enhancing higher productivity and component lifespan. In this study, a diagnostic and prognostic tool was developed to constantly monitor and predict the rate of degradation and Remaining Useful Life (RUL) of a railcar wheel bearing. The tool uses the envelope spectrum and kurtosis analysis, which employs the wheel acceleration data obtained from a primary source and the data was interpreted with the aid of statistical and computational methods. The input data was first pre-processed and important features are extracted in a MATLAB 2018b environment. The extracted features were thereafter integrated into the diagnostic and prognostic tool with a pre-set threshold value or feature for the wheel acceleration for predictive purpose. The results obtained indicate the suitability of the diagnosis and prognostic tool for the determination of the railcar wheel condition, prediction of the Mean Time to Failure (MTTF), as well as the remaining useful life of the railcar bearing. [ABSTRACT FROM AUTHOR]

In this study, the measurement and optimization of the cutting forces during the milling process of M200 TS was carried out. The Response Surface Methodology (RSM) was used in the design of the numerical experiment while and the dynamometer was employed for the measurement of the cutting forces during the physical experiment on a DMU80monoBLOCK Deckel Maho 5-axis CNC milling. The ranges of the process parameters are as follow: cutting speed (200-250 mm/min), feed per tooth (0.03-0.4 mm) and depth of cut (0.20-6.0 mm) while the cutting force serves as the response of the designed experiment. The results were analysed statistically to produce a mathematical model for the prediction of the values of the cutting forces as a function of the independent process parameters. [ABSTRACT FROM AUTHOR]

Tool geometry and selection of appropriate machining parameters are important considerations that determine the quality of surface finish, rate of tool wear, production cycle time, rate and ease of machinability. In this study, the milling process was designed to optimize the effects of machining parameters namely; the width of cut, cutting force, depth of cut and feed rate for effective AISI P20 removal during milling operation. The numerical design was carried out using the Complete Abaqus Environment (CAE) and the Response Surface Methodology (RSM) while the physical experiment was investigated using the DMU 80 CNC milling machine as well as the dynamometer and dynaware data acquisition system. The design of the numerical experiment consists of four factor-two level factorial of 16 experimental runs. Based on the feasible combination of the machining parameters from numerical experiment, the milling operation of AISI P20 was carried out on the DMU 80 milling machine limited to a maximum load of 900 kg. The resulting values of the cutting force, moment of force and machining time were obtained via the data acquisition system. The analysis of the results led to the formulation of a predictive model that correlates the rate of material removal (RMR) as a function of the independent machining process parameters. The results obtained also indicate that the cutting force, width and depth of cut as well as the feed rate are important parameters that influence the rate of material removal during machining operations, hence, the need for proper process design and control of the milling operation process parameters in order to reduce the total manufacturing time and increase the metal removal rate which is a function of productivity. This will in turn reduce the total manufacturing cost without sacrificing product quality with attendant increase in productivity. [ABSTRACT FROM AUTHOR]

Residual stresses through plastic deformation during the assembly operation of a rail car can cause cracks, distortion or other associated welding flaws if not checked. This study considers the use of analytical and numerical methods involving the simulation of isotropic hardening models for the prediction of AISI 1035 weld residual stresses during the arc welding assembly of the lower brackets of a rail car. Using the isotropic hardening model as well as the ABAQUS simulation software, the dynamic simulation of the gas metal arc welding process was performed at ambient temperature of 25oC over varying temperatures. The thermal modelling of the weld pass was carried out followed by the determination of the stress and strain values from the combined thermal elastic-plastic analysis. The results obtained indicated that the predictive ability of the isotropic model is highly significant, with the values of the weld stress and corresponding strain agreeing closely with the experimental values over varying ranges of temperatures. Hence, the isotropic hardening model can be used for weld residual stress simulation and prediction. Future work can consider the combination of both the isotropic and kinematic models. [ABSTRACT FROM AUTHOR]