State of Art Optimization Techniques for Machining Parameters Optimization during Milling
Satish Kumar1, Arun Kumar Gupta2, Pankaj Chandna3
1Satish Kumar, Research Scholar, Mechanical Engineering Department, NIT Kurukshetra Haryana India.
2Arun Kumar Gupta* , Associate Professor, Mechanical Engineering Department, Maharishi Markandeshwar (Deemed to be) University, Mullana, Ambala, Haryana India.
3Pankaj Chandna, Professor, Mechanical Engineering Department, NIT Kurukshetra Haryana India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 5104-5114 | Volume-8 Issue-6, August 2019. | Retrieval Number: F9562088619/2019©BEIESP | DOI: 10.35940/ijeat.F9562.088619
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Abstract: Optimization of machining parameters becomes more important; when high capital cost NC machines are employed for high precision and efficient machining. Minimizations of unit cost and time along with minimum tool and workpiece deflection, improved surface finish & tool life under certain boundary conditions are key objectives of the optimization problem. Optimization methods for milling include in-process parameters relationship with machining objectives and determination of optimal cutting conditions. Development of cost effective mathematical models is still a challenging task. However, there has been a considerable improvement in the techniques of modeling and optimization during the last two decades. In this paper, several modeling and optimization techniques reported for the milling operations have been reviewed and are for milling, classified for different criteria. Issues related to performance of several evolutionary algorithms, machining parameters, objectives and constraints have also been identified. From the survey of optimization techniques during milling operations it has been found that search techniques perform better than experimental approaches for optimization of process parameters. However, the experimental techniques play a vital role in prediction models for different machining objectives.
Keywords: Optimization of Machining Parameters, Milling, SA, GA, Taguchi, RSM, DOE, Review.