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Articles by Adeel H. Suhail
Total Records ( 2 ) for Adeel H. Suhail
  Adeel H. Suhail , N. Ismail , S. V. Wong and N. A. Abdul Jalil
  Problem statement: In machining operation, the quality of surface finish is an important requirement for many turned workpieces. Thus, the choice of optimized cutting parameters is very important for controlling the required surface quality. Approach: The focus of present experimental study is to optimize the cutting parameters using two performance measures, workpiece surface temperature and surface roughness. Optimal cutting parameters for each performance measure were obtained employing Taguchi techniques. The orthogonal array, signal to noise ratio and analysis of variance were employed to study the performance characteristics in turning operation. Results: The experimental results showed that the workpiece surface temperature can be sensed and used effectively as an indicator to control the cutting performance and improves the optimization process. Conclusion: Thus, it is possible to increase machine utilization and decrease production cost in an automated manufacturing environment.
  Adeel H. Suhail , N. Ismail , S.V. Wong and N.A. Abdul Jalil
  As manufacturing technology has been moving to the stage of full automation over the years, one of the fundamental requirements is the ability to accurately predict the output performance of machining processes. The focus of present study is to predict surface roughness using the workpiece surface temperature of a turning workpiece with the aid of an infrared temperature sensor. Relationship between the workpiece surface temperature and the cutting parameters and also between the surface roughness and cutting parameters were found out for indirect measurement of surface roughness through the surface temperature of the workpiece. A 33 full factorial design was used in order to get the output data uniformly distributed all over the ranges of the input parameters. Response Surface Method (RSM) and analysis of variance (ANOVA) are used to get the relation between different response variables (Surface roughness and workpiece surface temperature) and the input parameters (speed, feed and depth of cut). Based on variance analysis for the second order RSM model, most influential design variable is feed rate and depth of cut on surface roughness and workpiece surface temperature respectively and the experimental results show that the workpiece surface temperature can be sensed and used effectively as an indicator of the cutting performance.
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