Genetic Algorithm Based Optimization ofMaterial Removal Rate and Surface Roughness in Turning Process

• Main Author: Kemala Damanik 13114025, Bivynka
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/26135
• Institution: Institut Teknologi Bandung

&#65279;Turning process is one of the most common machining processes in variousmanufacturing industries. It is conducted by eroding the rotating workpieceusing a tool which moves in a linear motion. The significant development to theneed of manufacturing, consequently, increases the relevancy for anoptimization process in order to have a higher efficiency in production process.Optimization can be interpreted as a mathematical technique to find maximumor minimum value of a function of several variables under the given constraints.This research examined genetic algorithm (GA) as the optimization methodfor turning process. GA is a metaheuristic method which imitates the principleof natural selection where the fittest individuals are selected for reproduction inorder to produce offspring for the next generation. One of the advantages of thismethod is its capability to find the global optimum value unlike any othermethod. The optimization process was started by performing the selected designof experiments in accordance with the control factors and its levels. The toolnose radius, cutting speed, feed speed, and depth of cut were chosen as thecontrol factors in this research. The outcome of this step was a fitness functionwhich explainedthe relationship between the control factors, material removalrate (MRR), and surface roughness (Ra). GA used the fitness function to producethe result with the highest MRR and the lowest Rain a separate optimizationprocess or known as a single-objective optimization.Based on the validation process of the optimization results, geneticalgorithm was able to work properly as an optimization method of MRR and Rain turning process. <br />