Abstract
Groove is a key structure of high-performance integral cutting tools. It has to be manufactured by 5-axis grinding machine due to its complex spatial geometry and hard materials. The crucial manufacturing parameters (CMP) are grinding wheel positions and geometries. However, it is a challenging problem to solve the CMP for the designed groove. The traditional trial-and-error or analytical methods have defects such as time-consuming, limited-applying, and low accuracy. In this study, the problem is translated into a multiple output regression model of groove manufacture (MORGM) based on the big data technology and AI algorithms. The inputs are 34 groove geometry features, and the outputs are 5 CMP. Firstly, two groove machining big data sets with different range are established, each of which is includes 46,656 records. They are used as data resource for MORGM. Secondly, 7 AI algorithms, including linear regression, k nearest-neighbor regression, decision trees, random forest regression, support vector regression, and ANN algorithms, are discussed to build the model. Then, 28 experiments are carried out to test the big data set and algorithms. Finally, the best MORGM is built by ANN algorithm and the big data set with a larger range. The results show that CMP can be calculated accurately and conveniently by the built MORGM.
Highlights
Groove is one of the most important structure of integral cutting tools, such as end mill, drill and reamer
With the emergence of diverse groove geometries, how to set the crucial manufacturing parameters (CMP), including grinding wheel positions and geometries, to get the desired groove geometry has been an urgent problem for groove machining and cutting tool manufacture
In order to meet the increasing manufacturing requirements of diverse cutting tool grooves, MORGM was established based on artificial intelligence (AI) algorithms and big data technology
Summary
Groove is one of the most important structure of integral cutting tools, such as end mill, drill and reamer. The objective function method was an optimization approach to calculate the wheel position until the function was approximately solved. Wang et al [12] built an objective function by the error of rake angle, core radius and groove width between the desired and the machined groove. Ren et al [13] created a system of nonlinear equations to calculate the wheel position, and the wheel position can be calculated accurately to ensure the accuracy of the groove parameters: rake angle, core radius and flute width. Li [2]built a general model is established to calculate the wheel path for complex groove machining based on a mathematical optimization model, which have three constraints and one objective. Considering that artificial intelligence (AI) algorithms was good at building regression models, the study aimed to build a multiple output regression model of groove manufacture (MORGM) based on AI algorithms and big data of groove machining processes
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