Prediction of steel machinability by genetic programming

Purpose: This paper describes intelligent system to predict steel machinability. Design/methodology/approach: The prediction of machinability of steel, depending on input parameters (percentage of calcium, percentage of oxygen, percentage of sulphur), was performed by means of genetic programming and data on the batches of steel already made. Findings: The mathematical model to predict machinability of steel obtained by genetic programming method gives only 4 wrong predictions out of 146 experimental values. The model was tested also with testing data set. The machinability of the complete test batches (27 experimental values) was successfully predicted. Research limitations/implications: Limitation of the proposed concept is the size of test data (N = 27), which means longer testing period. The 146 batches, which were used for modeling, were collected in the period of February 2004 to April 2005. Practical implications: With the proposed approach, it is possible to establish efficient planning and optimizing of production, to reduce the costs of researches and the handling changes and, finally, to increase satisfaction of the buyers due to shorter delivery times. Originality/value: The paper presents new and innovative approach to predict steel machinability by genetic programming. The prediction precision is at high level. The results show that the proposed concept can be successfully used in practice.