An Improved Methodology For Milling Parameter Optimization Using Swarm Intelligence Approaches

Abstract

The controlling parameters of machining operations should be optimized to ensure newlinemaximum profit. Milling is a versatile manufacturing process having a significant newlinecontribution to the production of complicated shape objects. Machining cost is a major newlinecontributing factor to the overall product cost. To enhance profitability, keeping the newlinemanufacturing cost low poses a major challenge. Total machining cost and total machining newlinetime are two major components of the manufacturing cost. Cutting speed and feed rate of newlinemachining are the two main contributing factors in the milling operation. The total profit rate newlinehas been defined as a function of both cost and time, again expressed in terms of speed and newlinefeed of machining. The selection of a suitable combination of these two parameters is the key newlineto achieving improved profitability. Parallel computation of both cost and time components to newlineachieve a higher profit rate is followed in the presented work. An analytical model is newlinedeveloped for the purpose to calculate the profit rate considering several constraints. newlineExperimental data has been used to establish and analyze the relationship between the milling newlineparameters, achieved surface finish, and Specific Cutting Energy (SCE) for aluminium 7075. newlineAn analytical optimization model was defined for dealing with four different milling newlineoperations. The model proposed can estimate the total unit cost of manufacturing the newlinecomponent, unit time of manufacturing, total profit rate, and energy cost spent in the newlineoperation of machining a component on a Computer Numeric Controlled) CNC milling newlinemachine. The proposed optimization model is fed into a bio-inspired Swarm Intelligence newlinealgorithm named NMS-CS implemented on MATLAB 2021a. The proposed algorithm is newlinesuitable for all types of milling machines (conventional and CNC) and the methodology can newlinebe extended to CNC turning with little modification. newlineFor CNC machines, the process planning problem consists of several sub-problems that newlineresearchers have addressed historically to o