Development of Machine Learning Model for Analysis of Total Manufacturing Cost in Medium Turning of C45E Steel

The primary goal of manufacturing technologies in the metalworking industry is to provide products with specified quality characteristics, while maximizing time and cost efficiency. The total manufacturing cost in turning depends on a number of factors. The analysis of their effects and the estimation of the total manufacturing cost are of practical importance in process planning. Therefore, in the present study, the relationship between four inputs (depth of cut, feed rate, cutting speed and volume of material to be removed) and the total manufacturing cost in medium turning of C45E steel was modeled by using an artificial neural network (ANN). The developed ANN model was used for the analysis of the main and interaction effects of the aforementioned inputs on the total manufacturing cost. Verification of the observed effects was also carried out by applying the connection weight approach. The total manufacturing cost was mostly affected by depth of cut, while the effect of cutting speed was least pronounced. In addition, the results also revealed the presence of two-way interactions associated with cutting speed. For the given case study (with defined volume of material to be removed and specified machine tool), an optimized cutting regime was determined by developing and solving a single-objective turning optimization problem with three constraints related to chip slenderness, cutting power and depth of cut. Cutting force, needed for the estimation of cutting power, was estimated by using the dimensional analysis-based prediction model.