Optimization Of Turning Parameters Using Genetic Algorithm
Vijay Tiwari, Dr. Ashutosh Dwivedi
Rolling, Genetic Algorithm, CNC, Tool life
Steel rolling mills may be distinguished in a variety of ways. The mill's flexibility to roll steel in either hot or cold conditions enables it to create a variety of cross-sections, diameters, and grades. Roll supports are utilized in every rolling mill to secure the rollers that are used to roll the materials. These rollers may break because of excessive cyclic loads or fractures in the rolling parts of the rollers. After all necessary machining procedures have been accomplished, a damaged roller is either replaced with new rollers or using the existing rollers. CNC machines are used to manufacture completed rolls from a cylindrical shape, as well as to repair damaged rolls. Straight turning, taper turning, and circular machining processes are used to create these rolls. Among the machining parameters for turning rolls discussed in this thesis are spindle speed, feed rate, and cut depth. The required functions are the machining time (Mt) and the tool life (L) (Tl). The purpose of this research was to optimise turning parameters using regression and GA in order to obtain the shortest possible machining time and the longest possible tool life (GA). It was feasible to spin cast iron rollers with tungsten-coated carbide inserts using a regression design of experiment approach and regression analysis (in EXCEL). Three process factors were studied for their influence on machine time and tool life using a L9 orthogonal array design. Spindle speed, feed rate, and depth of cut were all studied. Regression analysis was used to build mathematical models of how each individual responds to a scenario.
Article Details
Unique Paper ID: 162305

Publication Volume & Issue: Volume 10, Issue 9

Page(s): 129 - 139
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