02517nam a2200205 a 4500001001400000003000700014005001700021008004100038020002200079040000800101100002600109245009400135246009200229260003400321300004700355502011100402520172900513650003702242650003202279vtls000039796KUKTEM20251114204418.0090708t2008    my a   f m    00| 0 eng|d  aTHE0006561(Local)  aUMP0 aMohd Saupi Mohd Sauki10aOptimization of milling parameters using ant colony optimization /cMohd Saupi Mohd Sauki3 aOptimization of milling parameters using ant colony optimizationh[electronic resource]  aKuantan, Pahang :bUMP,c2008  a63 p. :bill. ;c30 cm. +e1 computer disc  aProject paper (Bachelor of Mechanical Engineering with Manufacturing) -- Universiti Malaysia Pahang - 20083 aIn  process  planning  of  conventional  milling,  selecting  reasonable  milling   parameters  is  necessary  to  satisfy  requirements  involving  machining  economics,   quality and safety. This study is to develop optimization procedures based on the Ant   Colony Optimization (ACO). This method was demonstrated for the optimization of   machining  parameters  for milling  operation.  The machining  parameters  in milling   operations  consist  of  cutting  speed,  feed  rate  and  depth  of  cut.  These machining   parameters  significantly  impact  on  the  cost,  productivity  and  quality  of machining   parts. The developed strategy based on  the maximize production rate criterion. This   study  describes  development  and  utilization  of  an  optimization  system,  which   determines  optimum  machining  parameters  for  milling  operations.  The  ACO   simulation  is  develop  to  achieve  the  objective  to  optimize  milling  parameters  to   maximize  the production rate  in milling operation. The Matlab software will be use   to  develop  the ACO  simulation. All  the  references  are  taken  from  related  articles,   journals and books. An example  to apply  the Ant Colony Algorithm  to  the problem   has been presented at the end of the paper to give clear picture from the application   of  the  system  and  its  efficiency.  The  result  obtained  from  this  simulation  will   compare with another method like Genetic Algorithm (GA) and Linear Programming   Technique  (LPT)  to  validation.  The  simulation  based  on  ACO  algorithm  are   successful  develop  and  the  optimization  of  parameters  values  is  to  maximize  the   production rate is obtain from the simulation 0aMachine-toolsxNumerical control 0aMachine-toolsxProgrammning