Analysing Computer-Aided Manufacturing Systems and Optimising Work Sequence of Complex Shell Parts

A track record in application of multi-operational CNC machines shows that their using is efficient only in the case of a significant increase in productivity rate and a dramatically reduced time-to-market of new products. Manufacturing capabilities of multi-operational machines (MOM) have been most completely revealed when machining the complex shell parts. The more complicated is a design of the part and the more is the number of its surfaces to be machined and the number of tools desirable for its machining and positioning, the more efficient is the use. One way to improve the MOM machining rate is to reduce nonproductive machine time by decreasing the mutual overlap processing of the movable operating elements of the machine.To solve this problem, the computer-aided manufacturing (CAM) systems have been analysed. The analysis has shown that their capabilities are wide enough, however, these systems can calculate only the total execution time of the main manufacturing steps, but cannot calculate the nonproductive machine time and minimise it. This conclusion suggests that the task of optimizing the processing sequence is relevant. The research has shown that the problem can be solved by dynamic programming methods, one of which is the solution of the traveling salesman problem (the Bellman’s method). With a known processing schedule of all the elementary surfaces of the shell part, i.e. the known number of the manufacturing steps to be performed, each step is represented as a vertex of some graph, and technological links between the vertices of its edges. A mathematical model developed on the Bellman principle, which is adapted to the manufacturing tasks allows us to minimise mutual overlap processing time of the operating elements of the machine to perform all the steps in the optimal sequence. Based on the MOM model (1000VBF), the mathematical model has passed tests when machining the shell part with 26 manufacturing steps to reach up to 12% reduction of the nonproductive machine time, as a result of optimization.