TIME OPTIMIZATION AND ENERGY CONSUMPTION ANALYSIS FOR HOLE MANUFACTURING WITH COMBINATION SIMULATED ANNEALING AND GENETIC METHOD ON CNC BASED TURRET PUNCHING MACHINE

• Main Author: Lukman, Sylvia
• Format: Theses
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/33076
• Institution: Institut Teknologi Bandung

This research describes the method of manufacturing process planning of hole manufacturing, which in every machining process consists of several individual operations with various types of punching blades. The process of setting the trajectory is one of the problems of holes manufacturing process because this kind of process takes a long machining time and often it obtaines undirected sequence path that sometimes caused stress material moreover the damaged of punching blades. The purpose of the optimization process is to minimize the processing time of hole manufacturing so the time function obtained can be converted in the form of energy consumption that helps manufacturers to predict the machining time. The machining process is presented by Dynamic Directed Graph where each node represents a machining operation. The time function can be obtained by studying the shortest path of a directed graph where the structure of objectivity studied is similar to the Traveling Salesman Problem problem. The mathematical model is used to solve the hole optimization problem of the graph structure by proposing Simulated Annealing Algorithms , Genetics Algorithms and the combination of both to solve the optimization problem. The Algorithm realization will be applied in real time CAD / CAM programming by using TOPS300 which is used for TRUMPF TC200 Punching Machine. There are 2 kinds of blade operations in this research which are single stroke operation and multi strokes operation. Both produce different time function. By applying the algorithm, shorter travel time with a more directed path is obtained. Directed Path is needed to avoid the stress of material that often occurs during the manufacturing process. Finally some meaningful conclusions about the application scope of the algorithm is achieved and some advices for further study are given. The final example shows that the hybrid algorithm of Simulated Annealing and Genetic Algorithms shows higher optimize performance and less energy consumption which 26.02 seconds for single stroke operation and 126.02 seconds for multi strokes operation with the acquisition of logical path results with the absence of intersecting trajectories. By applying routing algorithm , cost saving is obtained as much as 32% for single stroke operation with energy consumption as much 0.05237 kWH meanwhile for multiple strokes operation,cost saving is obtained as much as 24% with energy consumption as much as 0,236 kWH.