Comparison of Modeling Techniques for Selecting Optimized and Automated Plasma Cutting Process Parameters

Abstract

The advancement in technologies made the entire manufacturing system, to be operated with more efficient, flexible, user friendly, more productive and cost effective. One such a system to be focused for advancement is plasma cutting system, which has wider industrial applications. There are many researches pursuing at various area of plasma cutting technology, still the automated and optimized parameters value selection is challenging. The work is aimed to eliminate the manual mode of feeding the input parameters for cutting operation. At present, cutting parameters are fed by referring the past cut data information or with the assistance of experienced employers. The cutting process parameters selections will have direct impact on the quality of the material being cut, and life of the consumables. This paper is intended to automate the process parameters selection by developing the mathematical model with existing cutting process parameters database. In this, three different approaches, multiple regression, multiple polynomial regression and AI technique, are selected and analyzed with the mathematical relations developed between the different cutting process parameters. The accuracy and reliability of those methods are detailed. The advantage and disadvantage of those methods for optimal setting conditions are discussed. The appropriate method that can be preferred for automated and optimal settings are elucidated. Finally, the selected technique is checked for accuracy and reliability for the existing cut data.