© 2019 JETIR April 2019, Volume 6, Issue 4 www.jetir.org (ISSN-2349-5162) JETIRBB06077 Journal of Emerging Technologies and Innovative Research (JETIR) www.jetir.org 374 Review of Optimization and Modeling of Laser Machined AISI 304 Material Using MATLAB. Shruti Vavhal 1 ,Priyanka Sapkal 1 ,Gayatri Salve 1 ,Amruta Aher 1 ,Aniket Jadhav 2 1 UG Student, Mechanical Engineering, Smt. Kashibai Navale College of Engineering, Pune 2 Assistant Professor, Mechanical Engineering, Smt. Kashibai Navale College of Engineering, Pune Abstract- This review paper says fuzzy logic system, artificial neural fuzzy interface system and genetic algorithm can be used to predict the output results according to the given inputs by using MATLAB. By using these modules the systems can be built to approximately predict the result. Key Words- Fuzzy logic, ANFIS, Genetic algorithm I. Introduction: A. Fuzzy Logic System Fuzzy Logic is a logic or control system of ANN-valued logic system which uses the degrees of state “degrees of truth” of the inputs and produces outputs which depend on the states of the inputs and rate of change of these states. It was developed in 1965, by Professor Lofti Zadeh, at University of California, Berkley. Generally, it’s a method of reasoning Also, it has an approach to decision making in humans. As they involve all intermediate possibilities between digital values YES and NO. Fuzzy logic starts with and builds on a set of user-supplied human language rules. The fuzzy systems convert these rules to their mathematical equivalents. This simplifies the job of the system designer and the computer, and results in much more accurate representations of the way systems behave in the real world. Fuzzy Logic system can be used in automotive systems, for applications like 4-Wheel steering, automatic gearboxes etc. Other applications include Hi-Fi Systems, Photo-Copiers, Humidifiers etc. A Fuzzy Logic System is flexible and allow modification in the rules.The systems can be easily constructed. B. Artificial Neural Fuzzy Interface System: Neuro fuzzy refers to the combination of artificial neural network and fuzzy logic system in the field of artificial intelligence which was proposed by Jang in 1993. The main objective of manufacturing industries is to produce low cost, high quality products in short time. The selection of optimal cutting parameters is a very important issue for every machining process in order to enhance the quality of machined products and reduce the machining cost. In recent years, the trend of modeling the machining processes using artificial intelligence techniques of artificial neural fuzzy interface system is rising. The technique is used for the prediction of machining parameters and to enhance manufacturing automation. This research is used to predict the surface roughness, kerf top width, kerf bottom width, dross height and striation using laser power, cutting speed and gas power as input parameters. AISI 304 material is used as sample for the prediction of the given machining parameters. C. Genetic Algorithm: The GA is coded using software suite Matlab, assisted by the Parallel Computing toolbox. The basic idea is to imitate the natural selection and survival of the fittest that exist sinthegenetics of the species. A synthesis of GA principles, applications and examples can be find in literature. The general principle of GA is to assess the best configurations among a starting random population of configurations, keep the best (those meeting best the objective function or fitness), and then cross and must ate them to get a new child population of the same size, and soon. Genetic algorithm(GA), one of the superior and most applicable optimization methods is conceptualized around the ever evolving techniques of optimization. GA comes with the advantage of locating a global optimum point and does not reflect the deterrent so the gradient method viz. concavity, continuity, and drivability of the objective function. II. Literature Review: [1] Hashmi et al. [1999] studied fuzzy logic based selection of machining parameters. Fuzzy-logic principles have been applied for selecting cutting conditions in machining operations. The materials data used for theoretical calculations were for medium-carbon leaded steel (BHN 125- 425) and free-machining carbon wrought steel (BHN 225- 425). A fuzzy model has been developed for carrying out these calculations.All of the calculations for both materials were done manually and equations expressing cutting speed as a function of hardness were obtained for different cases. The technique is demonstrated to be an effective way to present a large volume of experimental data in a compressed form. The results presented show a very good correlation between the Machining Data Handbook's recommended