2023 Volume 17 Issue 2 Pages 120-127
Process planning is well known as the key toward achieving highly efficient machining. However, it is difficult to standardize machining skills for process planning, which depend heavily on skilled operators. Hence, in a previous study, a computer aided process planning (CAPP) system using machine learning is developed to determine the operation parameters for finish machining of dies and molds. On the other hand, in rough machining, it is assumed that some machining operations are conducted sequentially using a respective tool according to the workpiece shape, which induces a much higher complexity in process planning. Therefore, in this study, machine learning is adopted to determine operation parameters for rough machining. The developed CAPP system converts the removal volume into a voxel model and infers a machining operation for each voxel. The inferred machining operation is visualized using different colors and identified corresponding to the voxel. Finally, the removal volume is classified using three different machining operations. However, machine learning is said to have a critical problem in that it is difficult to understand the reasons for the inferred results. Hence, it is necessary for the CAPP system to demonstrate the certainty level of the determined operation parameters. Thus, this study proposes a method for calculating the degree of certainty. If an artificial neural network is trained sufficiently, similar inferred results would always be obtained. Consequently, by using the Monte Carlo dropout to delete weights at random, the certainty level is defined as the variance of the inferred results. To verify the usefulness of the CAPP system, a case study is conducted by assuming rough machining of dies and molds. The results confirm that the machining operations are inferred with high accuracy, and the proposed method is effective for evaluating the certainty of the inferred results.
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