Big data-oriented wheel position and geometry calculation for cutting tool groove manufacturing based on AI algorithms

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 prob...

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Bibliographic Details
Published in:International journal of advanced manufacturing technology 2022-04, Vol.119 (9-10), p.6717-6728
Main Authors: Li, Guochao, Liu, Zhigang, Lu, Jie, Zhou, Honggen, Sun, Li
Format: Article
Language:English
Subjects:
Algorithms
Big Data
CAE) and Design
Computer-Aided Engineering (CAD
Datasets
Decision trees
Engineering
Error analysis
Five axis
Geometry
Grinding machines
Grinding wheels
Grooves
Hard materials
Industrial and Production Engineering
Machine tools
Machining
Manufacturing
Mechanical Engineering
Media Management
Original Article
Regression models
Support vector machines
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Summary: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.
ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-022-08749-5