MULTIPLE TOOLS SELECTION AND CUTTING PLANES DETERMINATION MODEL TO MINIMIZE TIME COEFFICIENT FOR SCULPTURED DIE CAVITY ROUGHING - Widaningsih, Ineu
Several studies of optimization algorithm on the Sculpture Surface Machining (SSM) showed that multi-objective is required to trade-off between contradicting objectives. Currently, there are a lot of dies and mold production in the form of sculpture surfaces, for example, cellphone casing molds, bottle molds, children's toys, and more complicated shapes such as prosthetic products, turbines, etc. thus a more efficient roughing process is needed because the shape of sculpture surfaces has different complexity from prismatic and rotational forms, so the approach commonly used for these shapes is not suitable for sculpture surface shapes. The proportion of roughing process time takes about 60% of the total machining time. With the advancement of CNC milling machine technology, the process of roughing a mold cavity can be made more efficient by utilizing an automatic tool changer for the use of multiple tools. Multiple objectives are needed for a trade-off between contradictory goals to occur. By applying layer-by-layer approach, the selection of cutting tools (tooling) for each layer and cutting plane determination (layering) is considered as complex combinatorial problem. The selection of tool with the largest-tool-possible concept will result in a process with a small machining time but a large enough residual volume. On the other hand, selecting the smallest-tool-possible tool will result in a relatively smoother surface with a small residual volume but a high enough machining time that a trade-off is required. Three algorithms were developed to accommodate trade-offs simultaneously between two performance indicators of the roughing process in the mold cavity with sculptured walls, namely the roughing machining time and the residual volume of roughing represented by one aggregate variable so that the machining configuration produced in the roughing process has considered the finishing process. For the application of the algorithm, 63 data sets were selected that represent the complexity of the problem. Each data set represents a mold cavity with a different volume and wall’s inclination. The results of the implementation of the three developed algorithms show that by improving the algorithm from the objective of machining time to an aggregate variable that represents the coefficient of machining time and residual volume, there is an average improvement of the efficiency of the roughing machining process by 20%. The total enumeration algorithm requires an average solution searching time of 120 minutes per data set. The dynamic programming algorithm average solution searching time only takes 29 seconds, but the pre-processing stage is quite complex. The genetic algorithm, the pre-processing is simpler, the average solution searching time is 255 seconds, 69% of the solutions are optimal, and the rest have a close value of 99% to the optimal value. The practical application of this algorithm is expected to help the CNC machine process planner to produce a more efficient milling process. In its application, it is necessary to consider the tool change’s time by limiting the number of tools used. Suggested further research is to develop coding for pre-processing on a dynamic programming approach so that it can be applied to more complex cases and to develop process integration so that the input to the algorithm is only 3D images.