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Articles by Afifah Mohd Ali
Total Records ( 2 ) for Afifah Mohd Ali
  Erry Yulian T. Adesta , Afifah Mohd Ali , Delvis Agusman , Muhammad Riza and Mohammad Yuhan Suprianto
  Problem statement: In die and mold industries, surface roughness of the dies and molds produced will determine the end product quality. Therefore, the desired finish surface was specified and the appropriate processes were selected to reach the required quality. Among many contributors to surface quality problem, backcutting is one of the factors influencing the surface roughness. Approach: The aim for current research was to study the backcutting phenomena and their effect to the surface roughness of work material, AISI H13 with hardness of 48 HRC during high speed end milling. Machining performed on the Vertical Milling Centre (VMC) high cutting speed from 150-250 m min-1, feed rate 0.05-0.15 mm tooth-1 and depth of cut 0.1-0.5 mm. The analysis and observation of the backcutting phenomena are done by using optical surface roughness machine. Results: The result shown that the pattern of surface roughness was not sufficient enough to compare between the surface with backcutting and without backcutting and the backcutting phenomena were seen mostly in combination of medium to high cutting speed and medium to high feedrate. Conclusion/Recommendations: Further research is needed with incrimination of experiments and adjustments of parameters.
  Muataz Hazza Faizi Al-Hazza , Erry Yulian T Adesta , Afifah Mohd Ali , Delvis Agusman and Mohammad Yuhan Suprianto
  This study presented an empirical study to model the cost of the energy for high speed hard turning. A set of experimental machining data to cut hard AISI 4340 steel was obtained with a different range of cutting speed, feed rate and depth of cut with negative rake angle. Regression models were developed by using Box-Behnken Design (BBD) as one of Respond Surface Methodology (RSM) collections. Neural network technique was deployed using MATLAB to predict the energy as a part of the artificial intelligent methods. The data collected was statistically analyzed using Analysis of Variance (ANOVA) technique. Second order energy prediction models were developed by using (RSM) then the measured data were used to train the neural network models. A comparison of neural network models with regression models is also carried out. Predictive Box-Behnken models are found to be capable of better predictions for energy within the range of the design boundary.
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